Abstract

   Despite improvement in our understanding of long noncoding RNAs
   (lncRNAs) role in cancer, efforts to find clinically relevant
   cancer-associated lncRNAs are still lacking. Here, using nascent RNA
   capture sequencing, we identify 1145 temporally expressed
   S-phase-enriched lncRNAs. Among these, 570 lncRNAs show significant
   differential expression in at least one tumor type across TCGA data
   sets. Systematic clinical investigation of 14 Pan-Cancer data sets
   identified 633 independent prognostic markers. Silencing of the top
   differentially expressed and clinically relevant S-phase-enriched
   lncRNAs in several cancer models affects crucial cancer cell hallmarks.
   Mechanistic investigations on SCAT7 in multiple cancer types reveal
   that it interacts with hnRNPK/YBX1 complex and affects cancer cell
   hallmarks through the regulation of FGF/FGFR and its downstream
   PI3K/AKT and MAPK pathways. We also implement a LNA-antisense
   oligo-based strategy to treat cancer cell line and patient-derived
   tumor (PDX) xenografts. Thus, this study provides a comprehensive list
   of lncRNA-based oncogenic drivers with potential prognostic value.
     __________________________________________________________________

   Although we know lncRNAs play a role in cancer, the identification of
   clinically relevant and functional lncRNAs is lacking. Here, the
   authors identify 633 prognostic markers, 570 S-phase cancer-associated
   lncRNAs, and show SCAT7 regulates FGF/FGFR and PI3K/AKT/MAPK pathways
   via interaction with hnRNPK/YBX1 complexes.

Introduction

   Recent ultra-high-throughput transcriptome sequencing data suggest that
   nearly two-thirds of the human genome is pervasively transcribed,
   giving rise to more than double the number of lncRNAs compared to
   protein-coding RNAs^[40]1. It is evident from studies that lncRNAs do
   not represent transcriptional noise, but they participate in diverse
   biological functions with implications in development, differentiation,
   and disease^[41]2–[42]4. lncRNAs operate at the transcriptional and
   post-transcriptional level to control gene expression in a
   spatiotemporal fashion^[43]5. Considering the influence of lncRNAs in a
   wide-range of biological processes, and that a significant portion of
   disease-associated single-nucleotide polymorphisms (SNPs) map to lncRNA
   loci^[44]6, one could expect a greater role for lncRNAs in human
   disease. Recent investigations have also implicated numerous lncRNAs in
   cancer progression and metastatic dissemination^[45]7–[46]10. Despite
   the increased number of studies addressing the role of lncRNAs in
   cancer, our understanding of lncRNAs in cancer initiation and
   progression as well as their relevance to clinical prognosis is in its
   infancy. Hence large-scale lncRNA-based functional screens, coupled
   with clinical investigations, are required to understand their roles in
   cancer progression and their use for diagnostic and prognostic
   purposes.

   The ability to sustain chronic proliferation is considered one of the
   hallmarks of cancer^[47]11. The cell division cycle plays a central
   role in the control of normal and chronic cell proliferation via
   responding to cell cycle regulators such as cyclins and
   cyclin-dependent kinases (CDKs), growth factors and repressors, tumor
   suppressors and oncogenes^[48]11. Cell division comprises two phases:
   the DNA synthesis (S) phase and the mitotic (M) phase. The pathways
   controlling accurate DNA replication during S-phase are critical for
   normal cell proliferation, as replication errors could result in
   abnormal cell proliferation^[49]12,[50]13. Thus, characterization of
   molecules that control S-phase progression would be of immense
   importance in understanding the role of S-phase in normal and disease
   conditions.

   The role of lncRNAs in S-phase regulation is still poorly understood in
   comparison to protein-coding genes. Accumulated knowledge has so far
   been unable to explain the precise molecular pathways that control cell
   proliferation in normal and cancer conditions^[51]11,[52]14. Thus,
   exploring the functional role of lncRNAs in cell cycle progression may
   provide insights into hitherto unanswered questions about how cell
   division is controlled in normal and cancer cells. Consistent with
   this, several investigations have shown that lncRNAs regulate cell
   cycle progression through controlling the expression of critical cell
   cycle regulators such as cyclins, CDKs, CDK inhibitors, and other
   factors^[53]15–[54]17. In addition, a recent investigation implicated a
   subset of lncRNAs from 56 well-known cell cycle-linked loci, showing
   periodic expression patterns across cell cycle phases, in cell cycle
   regulation^[55]18. However, a comprehensive characterization of lncRNAs
   showing temporal expression during S-phase and their functional link to
   cell cycle regulation, cancer progression, and their use in prognosis
   has not been investigated.

   In this study, we address two fundamental propositions that can enhance
   the current comprehension of the critical roles of lncRNAs in cancer
   development and progression. First, we study the contribution of
   S-phase-specific lncRNAs to cell cycle progression and other important
   cancer-associated hallmarks. Second, we investigate the capability of
   S-phase-specific lncRNAs to act as independent prognostic biomarkers in
   different cancers. To address these aims, we employ nascent RNA-seq
   during S-phase progression and generate a resource of lncRNA-based
   oncogenic cancer drivers that can be used as prognostic markers in the
   risk assessment as well as potential therapeutic regimens in the
   treatment of cancer.

Results

Identification of temporally expressed lncRNA across S-phase

   We have previously reported an optimized nascent RNA capture assay for
   detecting temporally expressed S-phase RNAs in HeLa cells^[56]19.
   Compared to the standard steady-state RNA isolation from various
   S-phase stages, this method detects accurate expression timing of the
   analyzed transcripts^[57]19. Briefly, HeLa cells were synchronized at
   the G1/S boundary using thymidine and hydroxyurea. Subsequently, the
   G1/S block was released and the cells were allowed to proceed in the
   presence of 5-ethynyl uridine (EtU) in order to specifically label the
   newly synthesized nascent RNA populations across different S-phase time
   points. To identify S-phase-specific lncRNAs, EtU labeled and
   steady-state (unlabeled) RNAs were individually collected at different
   time points spanning S-phase (T1, T2, and T3) in addition to RNA from
   unsynchronized cells (Fig. [58]1a). Following strand-specific library
   preparation, all RNA samples were subjected to deep sequencing
   (Supplementary Data [59]1). Principal component analysis of whole RNA,
   noncoding RNAs, and lncRNA expression profiles revealed that
   EtU-labeled RNAs had a better separation across different time points
   of S-phase compared to unlabeled samples (Fig. [60]1b). Based on the
   enrichment over unsynchronized samples, we identified 1734 and 1674
   lncRNAs in EtU labeled and unlabeled samples, respectively. Further, to
   unveil the dynamically expressed S-phase lncRNAs, we performed Short
   Time-series Expression Miner (STEM) clustering^[61]20. We obtained 1145
   lncRNAs with four significant temporal expression patterns, and 937
   lncRNAs with two significant temporal patterns, in EtU labeled
   (Fig. [62]1c; Supplementary Data [63]1) and unlabeled samples
   (Supplementary Fig. [64]1a and Supplementary Data [65]1), respectively.
   Both EtU labeled and unlabeled samples shared 394 temporally expressed
   lncRNAs (Fig. [66]1c; Supplementary Fig. [67]1b). We next investigated
   the enrichment of E2F1 transcription factor binding sites at the
   promoters (±2 kb from transcription start site (TSS)) of temporally
   expressed lncRNAs and found that 72 EtU labeled and 67 unlabeled lncRNA
   promoters contain E2F1 binding sites (Supplementary Data [68]1). We
   randomly selected dynamically expressed S-phase lncRNAs and validated
   their temporal expression patterns across different cell cycle phases
   (G1, S, and G2/M) in EtU labeled and unlabeled samples (Supplementary
   Fig. [69]1c). To further confirm the specificity of this method in
   capturing temporally expressed lncRNAs during S-phase, we validated
   selected lncRNAs using a drug-free synchronization of HeLa cells using
   the serum starvation method (Supplementary Fig. [70]1d, e).

Fig. 1.

   [71]Fig. 1
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   Identification of temporally expressed lncRNAs across S-phase using
   nascent RNA capture assay. a Cell cycle diagram showing Nascent RNA
   capture at three different time points (2, 3.5, and 5 h) of S-phase. b
   Principle component analysis (PCA) of expression profiles by
   considering complete profile (noncoding and protein-coding), all
   noncoding and only lncRNAs. c Time-series analysis of S-phase
   associated lncRNAs with twofold enrichment at least in one time point
   over the unsynchronized sample. The S-phase lncRNAs show four
   significant temporal patterns with STEM clustering. Venn diagram shows
   the overlap of lncRNAs enriched in EtU labeled and unlabeled samples.
   The p-values are obtained using permutation tests from STEM clustering.
   d Molecular pathway analysis (left) and gene ontology (right)
   enrichment analysis for nearby (<50 kb proximity) protein-coding genes
   to S-phase associated lncRNAs. The KEGG pathways or biological
   functions presented in the heatmaps show at least 10 genes with
   p-value < 0.05. The hypergeometric p-values are obtained from GeneSCF
   for pathway and GO enrichment analysis. e Workflow of the analysis for
   the identification of S-phase associated lncRNAs and its significance
   in different cancers

   Since lncRNAs exert their actions via regulating protein-coding RNAs in
   cis and/or trans^[73]21–[74]24, we performed enrichment analysis using
   GeneSCF^[75]24 for the neighboring protein-coding genes (within ±50 kb
   window of S-phase-specific lncRNAs). Interestingly, the neighboring
   protein-coding genes associated with EtU-labeled S-phase-specific
   lncRNAs demonstrated significant enrichment of molecular pathways
   related to cancer (Fig. [76]1d; Supplementary Data [77]1). Moreover,
   biological process analysis revealed an enrichment of transcriptional
   regulation, cell division, DNA repair, and cell migration processes. On
   the other hand, the protein-coding genes associated with unlabeled
   lncRNAs showed less enrichment of cancer-related pathways and cell
   division processes. These results indicate that nascent RNA enrichment
   via EtU labeling can efficiently distinguish temporally expressed
   lncRNAs during S-phase, which may be functionally engaged in vital
   cellular processes.

S-phase lncRNAs show differential expression in cancers

   We next designed a workflow which integrated epigenomic, functional,
   and clinical approaches to address the functional implications of
   EtU-labeled S-phase lncRNAs in multiple cancer types (Fig. [78]1e).
   Since, cell cycle perturbation is one of the hallmarks of cancer
   development^[79]11, we addressed the functional connection between
   S-phase lncRNAs and cancer initiation and progression. We utilized the
   RNA-sequencing data of 6419 solid tumors and 701 normal tissue samples
   spanning 16 cancer types, from The Cancer Genome Atlas (TCGA)^[80]25
   (Supplementary Data [81]2). Our analysis revealed that 570 out of 1145
   S-phase-specific lncRNAs were differentially expressed in at least one
   cancer type with log-fold change ± 2 and false discovery rate
   (FDR) < 1E–004 (Methods section, Fig. [82]2a; Supplementary
   Data [83]2). Interestingly, nearly 73% of the differentially expressed
   lncRNAs show upregulation in corresponding cancer types. We found that
   84 S-phase lncRNAs were differentially expressed between normal and
   tumor tissues, in more than five cancer types, with four of them
   differentially expressed in 10 cancer types. Also, different cancer
   types originating from same tissues shared many differentially
   expressed S-phase lncRNAs (Fig. [84]2b). For instance, lung
   adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) share 98
   differentially expressed S-phase lncRNAs. Likewise, kidney chromophobe
   (KICH), kidney papillary carcinoma (KIRP), and kidney clear cell
   carcinoma (KIRC) harbor 16 common differentially expressed S-phase
   lncRNAs. Since the latter cancers originate from distinct cell
   types^[85]26,[86]27, it remains to be seen whether these common and
   differentially expressed S-phase lncRNAs underpin the etiology of
   different kidney and lung cancers in a cell-independent manner. These
   observations indicate that temporally expressed lncRNAs show
   differential expression across multiple cancers and could serve as
   potential candidates for understanding their role in cancer development
   and progression.

Fig. 2.

   [87]Fig. 2
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   Characterization of S-phase lncRNAs as oncogenic drivers and
   independent prognostic markers using pan-cancer TCGA data sets. a
   Heatmap of 570 S-phase lncRNAs showing significant differential
   expression at least in one cancer type from TCGA. The significance was
   considered based on log-fold change ± 2 and FDR < 1E–004
   (Benjamini–Hochberg’s method). The highlighted lncRNAs are the ones
   selected for functional validation. The plot on the left side shows the
   frequency of individual lncRNAs differentially expressed across
   different cancer types. b Venn diagrams illustrating the overlap
   between common S-phase lncRNAs which are differentially expressed in
   different types of kidney and lung cancers. c Heatmap of S-phase
   lncRNAs with anti-correlative promoter methylation status to the
   differential expression levels in corresponding cancer. Hypomethylated
   promoter associated with higher expression and hypermethylation to less
   expression compared to normal. The highlighted lncRNAs are
   differentially methylated in more than 100 patients (samples)
   supporting the methylation pattern in corresponding cancer. Box plots
   of highlighted lncRNAs denoting the anti-correlative relation between
   promoter methylation and transcript expression. Box plots middle line
   shows the median, the box limits are 25th and 75th percentiles,
   whiskers are nearer quartile ± 1.5 times interquartile range and points
   beyond whiskers are the outliers. The p-values for the comparisons were
   obtained using Mann–Whitney test and corrected p-values using Family
   Wise Error Rate (FWER). The significant differentially methylated
   regions were filtered on the basis of FWER < 0.05. The above
   information for statistical analysis was extracted from COSMIC
   repository. d Heatmap showing the potential independent prognostic
   values of 520 S-phase lncRNAs based on dichotomization approach. The
   red color indicates higher expression of lncRNAs that predicts poor
   survival outcome. The blue color indicates lower expression of lncRNAs
   associated with poor survival in patients. e Venn diagrams indicating
   the numbers of S-phase lncRNAs that independently predict the survival
   outcome in different types of kidney and lung cancers

S-phase lncRNAs show epigenetic alterations

   We set out to address whether differential CpG methylation underlies
   the differential expression of S-phase lncRNAs seen in normal and tumor
   samples. We analyzed CpG methylation over their genomic regions in
   relation to gene expression. We utilized the processed CpG methylation
   data from the catalog of somatic mutations in cancers (COSMIC) for 12
   TCGA cancer types (Infinium Human Methylation 450 beadchip platform;
   ~4000 samples, from international cancer genome consortium (ICGC)
   release 18)^[89]28 (Supplementary Data [90]2). Differential methylation
   was considered significant only if a particular pattern
   (hypomethylation or hypermethylation) was supported by at least 10
   patient samples and with null patients opposing the pattern (Methods
   section). CpG methylation analysis over 570 differentially expressed
   S-phase lncRNA promoters revealed 35 lncRNAs that exhibit differential
   methylation (Fig. [91]2c). Among these, 22 lncRNAs were hypomethylated
   with higher expression in tumors, whereas 13 lncRNAs were
   hypermethylated with lower expression in tumors. Since gene-body
   methylation has been shown to be positively correlated with gene
   expression^[92]29, we analyzed gene-body methylation over S-phase
   lncRNAs and found 20 transcripts with differential methylation over the
   gene-body that correlated with gene expression (Supplementary
   Fig. [93]2). Out of 20 lncRNAs, 7 were hypermethylated and highly
   expressed, whereas 13 lncRNAs were hypomethylated with lower expression
   in tumors. Importantly, the promoter region of RP11-152P17.2 was
   hypomethylated and highly expressed in HNSC, KIRP, and LIHC tumors
   compared to normal tissues (Supplementary Data [94]2).

S-phase lncRNA act as independent prognostic markers

   We employed a systematic approach to identify the prognostic value of
   S-phase lncRNAs across 14 cancers. For each cancer type, we stratified
   the patients into high and low expression groups based on the
   expression levels of each lncRNA (Methods section). Then, we assessed
   the difference in survival using the Kaplan–Meier (KM) method. We
   tested the randomness in survival prediction using receiver operating
   characteristic^[95]7 curves and filtered candidates with an area under
   the curve (AUC) less than 0.5. To calculate the hazard ratios
   associated with the expression of each S-phase lncRNAs in each
   respective cancer type, we generated a multivariate Cox-regression
   model (Supplementary Data [96]2). The accuracy of the predictive
   multivariate prognostic models was investigated using Brier score-based
   prediction error curve over the reference model. Our systematic
   analysis based on dichotomization approach identified 520 S-phase
   lncRNAs that predict survival and also act as potential independent
   biomarkers at least in one cancer type (Fig. [97]2d, e; Supplementary
   Data [98]2). In addition, we identified 375 S-phase lncRNAs as
   continuous biomarkers in at least one cancer type (Supplementary
   Data [99]2) with a significant overlap of 262 S-phase lncRNAs with the
   dichotomization-based approach (Supplementary Fig. [100]2b and
   Supplementary Data [101]2). Notably, the multivariate models
   demonstrated that KIRC harbors the maximum number of S-phase
   lncRNA-based prognostic biomarkers. Of note, RP11-59D5__B.2 is the top
   candidate in our clinical investigations and it acts as an independent
   prognostic indicator in five cancers: BLCA, KIRC, KIRP, STAD, and HNSC
   (Supplementary Fig. [102]2c-f and Supplementary Data [103]2). Taken
   together, a large proportion of S-phase lncRNAs show an independent
   prognostic capacity, in relation to clinical covariates, in different
   cancer types.

SCATs modulate cell cycle progression and cell proliferation

   For further functional validation, we selected eight S-phase lncRNAs
   among the top candidates having a higher frequency of differential
   expression across TCGA data sets (Fig. [104]2a) which also
   independently predict the patients’ survival in multiple cancers
   (Fig. [105]2d, Fig. [106]3a; Supplementary Fig. [107]3). Hereafter, we
   address the selected candidates as S-phase cancer-associated
   transcripts (SCATs). We depleted the SCATs using small-interfering RNA
   oligonucleotides (siRNAs) or locked nucleic acid (LNA) antisense
   oligonucleotides using HeLa cells as a model system (Supplementary
   Fig. [108]3i). We assessed the effect of SCATs downregulation on cell
   proliferation using the colorimetric MTT assay (Fig. [109]3b). SCATs
   depletion induced a significant effect on cell proliferation.
   Similarly, the impact of SCATs loss-of-function on cell cycle
   progression was assessed using flow cytometry (Fig. [110]3c). Loss of
   function of SCATs induced cell cycle perturbations, accompanied with an
   accumulation of cells at the G1 phase and a decrease in DNA synthesis.
   Furthermore, downregulation of SCATs induced cellular apoptosis, as
   indicated by a significant increase in caspase 3/7 activity
   (Fig. [111]3d).

Fig. 3.

   [112]Fig. 3
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   The top clinically relevant S-phase lncRNAs regulate crucial cancer
   cell hallmarks. a Kaplan–Meier plots of SCAT1 –SCAT8 indicating overall
   survival of patients in KIRC. The higher expression of all SCATs is
   correlated with poor overall survival. The expression cut-off and the
   significance value for each SCAT are indicated in the plots. UQ
   represents upper quartile of the patients’ expression levels. The
   Forest plots represent the multivariate models derived for each SCAT in
   combination with the significant clinical parameters. The hazard ratio
   (HR) using Cox proportional hazard analysis and the associated p-values
   were calculated using Wald statistics. b Proliferation capacity of HeLa
   cells as measured by MTT colorimetric assay 48 h post-silencing of
   SCATs using two different LNAs (SCAT1, SCAT2, SCAT4, SCAT5, and SCAT6)
   or siRNAs (SCAT3 and SCAT7). Data are represented as percentage
   compared to cells transfected with respect to the negative control. No
   significant difference was observed between LNA-negative control and
   siRNA-negative control. c Cell cycle profiles of HeLa cells depleted
   with two different LNAs or siRNAs targeting the seven SCATs. d
   Estimation of the caspase 3/7 activities 48 h post-silencing of SCATs
   in HeLa cells. Data are expressed as fold change with respect to the
   corresponding negative controls. e MTT proliferation assay of Caki-2
   (KIRC) cell line depleted with two independent LNAs (SCAT4, SCAT8) or
   siRNAs (SCAT7). f Cell cycle profiles of Caki-2 cells depleted with two
   different LNAs or siRNAs. g Estimation of the caspase 3/7 activities
   48 h post-silencing of the corresponding SCAT in Caki-2 cells. Data in
   b–g are shown as mean ± SEM of three independent experiments.
   Significance levels were derived using unpaired two-tailed Students’
   t-test. (*p ≤ 0.05; **p = 0.01 – 0.001; ***p < 0.001)

   Since some of the SCATs showed differential expression and predict
   survival in KIRC tumors, we used a KIRC cell line Caki-2, to further
   validate their functional role in cell homeostasis. First, we
   investigated the temporal expression patterns of SCAT4, SCAT5, SCAT7,
   and SCAT8 during cell cycle progression in serum-starved Caki-2 cells
   and found their elevated expression during S-phase (Supplementary
   Fig. [114]3j). Then, we downregulated SCAT4, SCAT7, and SCAT8
   (Supplementary Fig. [115]3k) and measured cell proliferation, cell
   cycle profile, and caspase activity. Consistent with the
   loss-of-function experiments in HeLa cells, depletion of the selected
   SCATs in Caki-2 cells altered cell proliferation, inhibited cell cycle
   progression, and induced apoptosis (Fig. [116]3e-g).

SCAT1 and SCAT5 act as as independent prognostic factors

   Our clinical investigations revealed CTD-2357A8.3 (SCAT1) and LUCAT1
   (SCAT5) as common independent prognostic biomarkers for lung and
   kidney-derived cancers, respectively (Fig. [117]2e). SCAT1 is
   differentially expressed in 10 cancers (Fig. [118]4a), including LUAD
   and LUSC. Higher expression of SCAT1 in both LUAD and LUSC correlates
   with the poor clinical outcome (Fig. [119]4b; Supplementary
   Fig. [120]4a-f). Additionally, SCAT1 shows an elevated expression in
   the S-phase of synchronized A549 cells (Supplementary Fig. [121]4g).
   Consistent with the clinical data, downregulation of SCAT1 in A549
   cells exhibited a drastic inhibition of cell proliferation
   (Fig. [122]4c; Supplementary Fig. [123]4h), cell cycle arrest at G1
   phase (Fig. [124]4d), and promoted cellular apoptosis (Fig. [125]4e),
   indicating its role in lung carcinogenesis. Similarly, SCAT5, which is
   differentially expressed in five cancers (Fig. [126]4f), acts as an
   independent prognosticator in all three types of kidney cancers (KIRP,
   KIRC, and KICH) (Figs. [127]3a and [128]4g; Supplementary
   Fig. [129]4i-n and Supplementary Data [130]2). Its higher expression
   predicts poor clinical outcome in all three cancers and similar to
   other SCATs, SCAT5 expression is induced during S-phase in Caki-2 cells
   (Supplementary Fig. [131]3j). The depletion of SCAT5 in Caki-2 cells
   led to decrease in cell proliferation, cell cycle arrest at G1 phase,
   and induces apoptosis (Fig. [132]4h-j; Supplementary Fig. [133]4o).

Fig. 4.

   [134]Fig. 4
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   SCAT1 and SCAT5 act as oncogenic drivers and prognostic markers for
   lung-derived and kidney-derived cancers, respectively. a Bar graph
   showing the significant differential expression levels of SCAT1
   expressed as log[2] fold change across 10 different cancer types
   obtained from TCGA data sets. b Kaplan–Meier plots of SCAT1 indicating
   overall survival of patients in LUAD (upper left panel) and LUSC (lower
   left panel) cancer types. The higher expression of the SCAT1 is
   correlated with poor overall survival. The upper and lower right panels
   represent the multivariate models of LUAD and LUSC cancers,
   respectively, derived from Cox proportional hazard analysis and
   associated p-values were calculated using Wald statistics. c MTT
   proliferation assay of A549 (LUAD) cell line depleted with two
   different LNA oligos targeting SCAT1. d Cell cycle profiles of control
   and SCAT1 KD A549 cells. e Estimation of caspase 3/7 activity in
   control and SCAT1 KD A549 cells. f Bar graph showing the significant
   differential expression levels of SCAT5 expressed as log[2] fold change
   across five different cancer types obtained from TCGA data sets. g
   Kaplan–Meier plots of SCAT5 indicating overall survival of patients in
   KICH (upper left panel) and KIRP (lower left panel) kidney cancer. The
   higher expression of the SCAT5 is correlated with poor overall
   survival. The upper and lower right panels represent the multivariate
   models of KICH and KIRP cancers, respectively. h MTT proliferation
   assay of Caki-2 cell line 48 h post-silencing of SCAT5 using two
   different LNAs. i Cell cycle profiles of control and SCAT5 KD Caki-2
   cells. j Estimation of caspase 3/7 activity in Caki-2 cells 48 h
   post-silencing of SCAT5. The significance in figures a and f was
   derived using Benjamini–Hochberg’s method. Note that the data presented
   in c–e and h–j represents the mean values of three independent
   experiments and statistical significance was derived using a two-tailed
   unpaired Student’s t-test. Data are plotted as mean ± SD (*p ≤ 0.05;
   **p = 0.01 – 0.001; ***p < 0.001)

SCAT7 modulates hallmarks of cancer across cell lines

   We chose RP11-465N4.4 (SCAT7) to gain mechanistic insights into S-phase
   lncRNA induced cancer initiation and progression. Current annotations
   refer to SCAT7 as a polyadenylated antisense lncRNA with two variants
   of 788 and 500 nucleotides. The 788 nucleotide variant spans two
   protein-coding genes (RNPEP and ELF3). Northern blot analysis also
   confirmed the presence of these two variants (Supplementary
   Fig. [136]5a). The noncoding capacity of SCAT7 was confirmed using both
   CPAT probability and CPC score (Supplementary Fig. [137]5a). The
   sequence conservation analysis of SCAT7, as determined by phastCons
   score and phyloP, indicates no sequence homology among vertebrates
   (Supplementary Fig. [138]5b). SCAT7 RNA copy number analysis of HeLa
   and A549 cells by droplet digital PCR (ddPCR) revealed 224 and 496
   copies/ng RNA, respectively (A549 cells known to have >2500 copies
   MALAT1 and NEAT1^[139]30).

   SCAT7 was upregulated in multiple cancers (BLCA, BRCA, KIRP, LIHC,
   LUAD, LUSC, PRAD, and UCEC) (Fig. [140]5a). Moreover, our detailed
   clinical investigations demonstrated its potential to independently
   predict clinical outcome in KIRC and COAD patients (Fig. [141]3a;
   Supplementary Fig. [142]3g and Supplementary Data [143]2). Furthermore,
   SCAT7 promoter showed significant differential methylation in UCEC
   patients (Fig. [144]2c; Supplementary Data [145]2). The temporal
   expression analysis of SCAT7 during cell cycle progression indicated
   elevated levels in the S-phase of HeLa and Caki-2 cell lines
   (Supplementary Figs. [146]1c, d and [147]3j). Furthermore, qPCR
   expression analysis indicated that SCAT7 exhibits an elevated
   expression in LUAD cell lines (A549 and H2228) compared to normal lung
   cells (Supplementary Fig. [148]5c). Collectively, these results
   indicate that SCAT7 could be an oncogenic lncRNA with a potential
   prognostic capability. Hence, we sought to investigate the functional
   role of SCAT7 in the maintenance of cancer cell hallmarks in different
   cell lines representing multiple cancer types. To this end, both
   variants of SCAT7 were downregulated in cell lines representing KIRC
   (Caki-2 and 786-O), LUAD (A549 and H2228), LIHC (HEPG2), and BRCA
   (MCF7) using siRNA or short hairpin RNA (shRNA). Downregulation of
   SCAT7 variants in the knockdown cells in Northern blot analysis
   indicates the specificity of our RNAi knockdown experiments
   (Supplementary Fig. [149]5a). Downregulation of SCAT7 in Caki-2, 786-O,
   A549, H2228, HepG2, and MCF7 cells affected cell proliferation
   (Fig. [150]5b; Supplementary Fig. [151]5d, e) and cell cycle
   progression, with preferential accumulation of cells at G1 phase
   (Fig. [152]5c; Supplementary Fig. [153]5f). Additionally, the knockdown
   of SCAT7 led to decreased proliferation even in the non-cancerous
   HEK293 cells (Supplementary Fig. [154]5g). Furthermore, SCAT7 depletion
   altered the S-phase progression in multiple cell lines as indicated by
   the nascent 5-ethynyl-2’-deoxyuridine analog (EdU) incorporation assay
   (Supplementary Fig. [155]5h). Notably, compared to other cell lines,
   the transient knockdown of SCAT7 in H2228 cells induced a significant
   accumulation of cells at G2/M phase (Supplementary Fig. [156]5f).

Fig. 5.

   [157]Fig. 5
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   SCAT7 acts as an oncogenic driver in renal, lung, and liver cancers. a
   SCAT7 expression as log[2] fold change across cancer types from TCGA
   data sets. The significance was obtained using Benjamini–Hochberg’s
   method. b MTT of Caki-2, A549, and HepG2 cells upon SCAT7 KD with two
   shRNAs or siRNAs. c Cell cycle profiles of Caki-2, A549, and HepG2
   cells after shRNA or siRNA-based SCAT7 KD. d Percentage of apoptotic
   cells in SCAT7 KD Caki-2 and A549 48 h post-seeding. e Migration areas
   for stable SCAT7 KD Caki-2 and A549 cells were calculated with respect
   to a starting (t = 0) migration control area for each cell line. f
   Matrigel transwell assay in Caki-2 and A549 SCAT7 stable KD cells. The
   number of invasive cells was counted 24 h post-seeding. g Soft agar
   colony-forming assay using Caki-2 and A549 KD cells. h MTT of HeLa,
   Caki-2, and A549 cells overexpressing SCAT7. i Colorimetric
   β-galactosidase staining of BJ-BRAF and TIG3-BRAF human fibroblasts
   72 h post-silencing SCAT7 using three siRNAs. Senescent cells are in
   dark blue color. j Quantification of senescent cells upon SCAT7 KD in
   BJ-BRAF and TIG3-BRAF cells shown as percentage of the whole cells
   populations. k SCAT7 qPCR in serial passages of BJ-BRAF cells. NS, not
   significant. l SCAT7 expression in BJ-BRAF cells at day 0 and day 3
   upon tamoxifen-induced senescence (200 nM) at one passage interval. m
   MTT assay of BJ-BRAF cells overexpressing SCAT7 compared to empty
   vector. n Percentage of positively stained senescent cells 3 days
   post-tamoxifen treatment in control and SCAT7-overexpressing BJ-BRAF
   cells. o, p Expression of SCAT7, p16, p15 (o) and IL8 (p) in control
   BJ-BRAF and SCAT7-overexpressing cells at day 0 and day 3
   post-treatment with tamoxifen. q Quantification of senescent cells upon
   SCAT7 KD in A549 cells. The values are expressed as percentage of the
   whole cell population. Note that the data presented in b–h and j–q
   represents mean values of three independent experiments and the
   statistical significance was derived using a two-tailed unpaired
   Student’s t-test. Data are plotted as mean ± SD (*p ≤ 0.05; **p = 0.01
   – 0.001; ***p < 0.001)

   We next focused on Caki-2, 786-O, and A549 stable KD cell lines to
   elucidate the functional role of SCAT7 in apoptosis, cell viability,
   cell migration and invasion. SCAT7 knockdown in Caki-2 and A549 cell
   lines increased caspase 3/7 activity (Fig. [159]5d) and affected their
   viability, migration, and invasion properties (Fig. [160]5e,f;
   Supplementary Fig. [161]5i-m). The invasion capacity was severely
   suppressed in Caki-2 and A549 knockdown cells, while the effect was
   very limited in the case of 786-O knockdown cells. Additionally, we
   investigated the anchorage-independent cell growth using soft agar
   colony formation assay for Caki-2, 786-O, and A549 knockdown cell lines
   10 days after incubation (Fig. [162]5g; Supplementary Fig. [163]5n).
   The ability of knockdown cells to form anchorage-independent colonies
   was drastically reduced, and accordingly, the total number of colonies,
   as well as the area of each individual colony was decreased. In
   accordance with its role in restricting the cellular proliferation upon
   knockdown, overexpression of SCAT7 increased cell proliferation by
   2.25, 2.08, and 1.9 fold in HeLa, Caki-2, and A549 cells, respectively
   (Fig. [164]5h). However, we were unable to overexpress SCAT7 in the
   786-O cell line due to its inherited resistance to plasmid
   transfection. Collectively, our data demonstrate the critical role of
   SCAT7 in regulating some of the most important cancer hallmarks in
   multiple cancer cell lines.

Downregulation of SCAT7 induces cell senescence

   Given that SCAT7 knockdown induces cell cycle perturbations and
   deformed cell morphology (Supplementary Fig. [165]5o), we investigated
   the contribution of SCAT7 in cell senescence. To this end, we used two
   fibroblast cell lines; BJ and TIG3, immortalized with B-RAF
   transformation. Transient downregulation of SCAT7 using three siRNAs
   for 72 h induced senescence in both the cell lines as indicated by
   β-galactosidase staining and upregulation of senescence markers like
   p16, IL8, and p21 (Fig. [166]5i, j; Supplementary Fig. [167]5p). We
   also observed a spontaneous decrease in the expression levels of SCAT7
   during serial passaging of fibroblast cells mimicking the normal
   physiological aging (Fig. [168]5k). The immortalized BJ-BRAF
   fibroblasts express the activated form of mouse B-RAF (V600E) under the
   control of estrogen receptor (ER:B-RAF)^[169]31. Thus, the addition of
   4-hydroxytamoxifen (4-OHT) activates B-RAF and consequently
   senescence-associated signaling pathways^[170]32,[171]33. We sought
   then to assess the transcriptional activity of SCAT7 upon induction of
   premature senescence. Towards this, we treated BJ-BRAF cells with
   200 nM of 4-HT for 72 h and checked the transcriptional activation of
   some senescence-associated markers (Supplementary Fig. [172]5q).
   Interestingly, the onset of premature senescence resulted in
   significant reduction of SCAT7 expression (Fig. [173]5l). On the other
   hand, overexpression of SCAT7 in BJ-BRAF fibroblasts increased their
   proliferation (Fig. [174]5m; Supplementary Fig. [175]5r) as well as
   transcriptional activity of key senescence markers (Supplementary
   Fig. [176]5s). Cells overexpressing SCAT7 were able to bypass the
   tamoxifen-induced senescence, as indicated by less β-galactosidase
   activity and transcriptional repression of the key senescence markers
   p16, p15, and IL8 (Fig. [177]5n-p). We next tested the hypothesis that
   SCAT7 silencing in cancer cell lines may promote senescence. Toward
   this, we performed β-galactosidase staining on HeLa, A549 and Caki-2
   cell lines upon SCAT7 downregulation. Downregulation of SCAT7 induced
   the senescence phenotype in both HeLa and A549 cells while Caki-2 cells
   remained unaffected (Fig. [178]5q). Therefore, our data demonstrate the
   crucial role of SCAT7 in regulating cellular senescence, highlights the
   crosstalk between cell proliferation, cell cycle progression, and
   senescence.

SCAT7 regulates FGF/FGFR, PI3K/AKT, and Ras/MAPK pathways

   To gain an insight into the SCAT7-mediated molecular pathways and
   cancer-related processes, we performed RNA sequencing of HeLa, Caki-2,
   and A549 cells upon SCAT7 downregulation (Supplementary Fig. [179]6a).
   Subsequent functional enrichment analysis of RNA-seq data revealed that
   the depletion of SCAT7 affected major signaling pathways and vital
   biological processes (Fig. [180]6a-c; Supplementary Data [181]3). For
   instance, FGF/FGFR and the downstream PI3K/AKT and Ras/MAPK pathways
   were largely affected while cell proliferation, cell adhesion, cellular
   senescence, cell migration, and apoptotic processes were also
   perturbed. We validated some of the dysregulated genes at the
   transcriptional and translational levels (Fig. [182]6d and
   Supplementary Fig. [183]6b) in the SCAT7 downregulated cells. We
   detected a reduction in FGFR, p-AKT, and p-ERK1/2 levels in the SCAT7
   downregulated HeLa, Caki-2, and A549 cells. We also investigated the
   status of some of the well-known cell cycle master regulators; such as
   CCND1, CDK4, RB, and Phospho-Ser795 RB (p-RB), in SCAT7 depleted cells
   (Fig. [184]6e). CCND1 was downregulated at the transcriptional and
   translational level upon SCAT7 knockdown while p-RB, but not RB, was
   downregulated at the protein level. Conversely, the CDK4 expression was
   neither affected at the transcriptional nor the translational levels.
   Since SCAT7 overlaps two neighboring protein-coding genes ELF3 and
   RNPEP, we investigated the effect of its downregulation on the
   neighboring genes. SCAT7 depletion, using different siRNAs, shRNAs and
   LNAs targeting the unique non-overlapping exon of SCAT7, did not affect
   the expression of neighboring genes in HeLa and Caki-2 cell lines.
   However, we found reduced ELF3 expression only in A549 cells
   (Supplementary Data [185]3).

Fig. 6.

   [186]Fig. 6
   [187]Open in a new tab

   SCAT7 regulates cell cycle progression and cell proliferation via FGF
   signaling. a–c Heatmaps showing upregulated and downregulated genes
   with corresponding molecular pathways and biological processes upon
   silencing of SCAT7 in HeLa (a), Caki-2 (b), and A549 (c) cell lines. d
   Western blot showing the proteins levels of FGFR2, FGFR3, AKT, Ser 473
   Phospho-AKT (p-AKT S473), ERK1/2, and Phoshpo-ERK1/2 (p-ERK 1/2) upon
   silencing of SCAT7 in HeLa, Caki-2, and A549 cell lines. FGFR4 protein
   levels were only investigated in A549 cells. e Real-time qPCR
   validation (left panel) and Western blot (right panel) showing a
   significant reduction in the expression levels of CCND1 but not CDK4. f
   Real-time qPCR validation of the expression levels of SCAT7 and its
   targets FGFR2, FGFR3, and FGFR4 in HeLa, Caki-2, and A549 cells upon
   SCAT7 KD with two independent siRNAs or shRNAs. g Expression of SCAT7
   and its target FGFR2 in HeLa and Caki-2 cells overexpressing SCAT7.
   Data are shown as relative fold change normalized to GAPDH. h MTT assay
   in HeLa and Caki-2 cells, after transfection with siRNAs targeting
   FGFR2, and A549 cells, transfected with esiRNA to silence FGFR3. i Cell
   cycle profiles of HeLa and Caki-2 cells transfected with siRNAs
   targeting FGFR2 (left and middle). The right panel shows the cell cycle
   profile of A549 cells transfected with FGFR3 esiRNA. Note that the data
   presented in e–i represent mean values of three independent experiments
   and the statistical significance was derived using a two-tailed
   unpaired Student’s t-test. Data are plotted as mean ± SD (*p ≤ 0.05;
   **p = 0.01 – 0.001; ***p < 0.001)

   Based on RNA-seq analysis and subsequent validations across different
   cell lines, we hypothesized that SCAT7 modulates some of the cancer
   hallmarks through the regulation of different FGF/FGFR members. For
   instance, SCAT7 knockdown affected the mRNA levels of FGFR2, FGFR3,
   FGF7, and FGF21 in HeLa cells while only FGFR2 was downregulated in the
   Caki-2 and 786-O cells (Fig. [188]6f; Supplementary Fig. [189]6c).
   Similarly, FGFR3 and FGFR4 were downregulated upon SCAT7 depletion in
   A549 cells (Fig. [190]6f). Conversely, overexpression of SCAT7 restored
   the expression of FGFR2 in HeLa and Caki-2 cells (Fig. [191]6g).
   Moreover, the expression pattern of different FGFR members matched
   SCAT7 expression during cell cycle progression in the investigated cell
   lines (Supplementary Fig. [192]6d) and in tumor tissues derived from
   LUAD, KIRC, and UCEC (Supplementary Data [193]3). For instance, in HeLa
   cells, SCAT7, FGFR2, and FGFR3 are expressed early in the S-phase. In
   the case of A549 cells, SCAT7, FGFR3, and FGFR4 demonstrate a higher
   expression at G2 phase. To test our hypothesis that SCAT7 executes its
   actions via FGF/FGFR signaling, FGFR2 was depleted in HeLa and Caki-2
   cells using two siRNAs, and FGFR3 and FGFR4, separately, using esiRNAs
   in A549 cells (Supplementary Fig. [194]6e). Interestingly, the effects
   of the FGFR2 depletion on cell cycle, cell proliferation, and vitality
   phenocopied the effects of the SCAT7 depletion in the HeLa and Caki-2
   cells (Fig. [195]6h, i; Supplementary Fig. [196]6f). Furthermore,
   SCAT7-induced cell proliferation was attenuated when FGFR2 was
   silenced; indicating that SCAT7-induced cell proliferation is, in part,
   carried out by the FGF/FGFR2 signaling (Supplementary Fig. [197]6g).
   Though downregulation of both FGFR3 and FGFR4 in A459 cells led to a
   decrease in cell proliferation, only FGFR3 downregulation affected cell
   cycle progression in A549 cells (Fig. [198]6h, i; Supplementary
   Fig. [199]6h, i). Collectively, our data strongly suggest the
   involvement of SCAT7 in the modulation of cellular homeostasis and cell
   cycle progression through the regulation of the FGF/FGFR and the
   downstream PI3K/AKT and Ras/MAPK signaling pathways.

SCAT7/hnRNPK/YBX1 complex regulates cancer cell hallmarks

   We next studied the mechanisms by which SCAT7 regulates cancer
   progression via FGF/FGFR signaling in HeLa cells. We first determined
   the subcellular distribution of SCAT7 and found it to be enriched in
   the nucleoplasmic and chromatin compartments (Supplementary
   Fig. [200]7a). Then, we performed chromatin oligo-affinity
   precipitation (ChOP) in UV crosslinked cells using biotinylated
   oligonucleotides to pull-down SCAT7 and its interacting proteins. Using
   two independent biological replicates, we identified 96 proteins that
   were specifically enriched in both replicates of SCAT7 ChOP
   (Fig. [201]7a; Supplementary Data [202]3). We selected two proteins,
   hnRNPK and YBX1, well-known for functions related to transcriptional
   regulation and cell cycle progression, for detailed mechanistic
   investigations^[203]34,[204]35. We validated the interaction of hnRNPK
   and YBX1 proteins with SCAT7 using RNA immunoprecipitation (RIP) assay
   (Fig. [205]7b) and ChOP pull-down followed by Western blot
   (Fig. [206]7c).

Fig. 7.

   [207]Fig. 7
   [208]Open in a new tab

   SCAT7 interacts with hnRNPK and YBX1 to regulate cell proliferation and
   cell cycle progression. a Venn diagram showing SCAT7 interacting
   proteins in HeLa cells identified using ChOP-MS in two independent
   biological replicates. b RIP using hnRNPK or YBX1 antibody followed by
   qPCR for SCAT7. c Validation of SCAT7 interaction with hnRNPK and YBX1
   by ChOP followed by immunoblotting in HeLa cells. LacZ and SCAT7
   reverse biotinylated probes were used as negative controls. d MTT in
   HeLa cells after transfection with two siRNAs targeting hnRNPK or YBX1.
   e Cell cycle analysis upon hnRNPK and YBX1 KD in HeLa cells. f FGFR2
   expression by real-time qPCR in hnRNPK and YBX1 KD HeLa cells. g
   Western blot of FGFR2, FGFR3, AKT, Ser 473 Phospho-AKT (p-AKT S473),
   ERK1/2, and Phoshpo-ERK1/2 (p-ERK 1/2) in hnRNPK and YBX1 KD HeLa
   cells. h, i ChOP followed by qPCR for SCAT7 enrichment at FGFR2 (h) and
   FGFR3 (i) promoters in HeLa cells. Four primer pairs were used to
   assess the occupancy at every 250 bp upstream of FGFR2 and FGFR3 TSS.
   j, k ChIP using hnRNPK or YBX1 antibody followed by qPCR for hnRNPK and
   YBX1 occupancy at FGFR2 (j) and FGFR3 (k) promoters in control and
   SCAT7 KD HeLa cells. l Interaction of SCAT7 with hnRNPK and YBX1 by
   SCAT7 ChOP followed by immunoblotting in A549 cells. m ChOP followed by
   qPCR for SCAT7 enrichment at FGFR3 promoter in A549 cells. n hnRNPK or
   YBX1 ChIP followed by qPCR depicting the occupancy of hnRNPK and YBX1
   at the FGFR3 promoter in control and SCAT7 KD A549 cells. o SCAT7 ChOP
   followed by immunoblotting with hnRNPK or YBX1 antibody in Caki-2
   cells. p ChOP followed by qPCR quantification of SCAT7 enrichment at
   FGFR2 promoter in Caki-2 cells. q hnRNPK ChIP followed by qPCR
   depicting the occupancy of hnRNPK at FGFR2 promoter in control and
   SCAT7 KD Caki-2 cells. Note that the data presented in b, d–f, h–k, m,
   n, p, and q represent mean values of three independent experiments and
   the statistical significance was derived using a two-tailed unpaired
   Student’s t-test. Data are plotted as mean ± SD (*p ≤ 0.05; **p = 0.01
   – 0.001; ***p < 0.001)

   Next, we investigated the role of hnRNPK and YBX1 in the regulation of
   cell proliferation, cell cycle progression, FGFR2, and FGFR3
   expression, and pathways downstream to FGFRs in HeLa cells. Transient
   knockdown of hnRNPK or YBX1 by siRNAs (Supplementary Fig. [209]7b) led
   to a significant decrease in cell proliferation (Fig. [210]7d) and cell
   cycle arrest at the G1 phase (Fig. [211]7e). Also, FGFR2 and FGFR3
   expression was substantially downregulated at the mRNA (Fig. [212]7f)
   and protein levels (Fig. [213]7g) in hnRNPK or YBX1 knockdown cells.
   The activities of the MAPK and AKT pathways downstream to FGFRs were
   also reduced upon hnRNPK or YBX1 knockdown (Fig. [214]7g). To
   understand the mechanism of FGFR2 and FGFR3 regulation by
   SCAT7/hnRNPK/YBX1 ribonucleoprotein (RNP) complex, we first checked the
   interaction of SCAT7/hnRNPK/YBX1 complex with mRNA and the proximal
   promoters of FGFR2 and FGFR3 genes. In our RIP assays with hnRNPK and
   YBX1, we failed to detect FGFR2 or FGFR3 mRNAs, indicating that SCAT7
   does not modulate FGFR2 and FGFR3 mRNA levels post-transcriptionally
   (Supplementary Fig. [215]7c). We then assessed the occupancy of SCAT7
   over the FGFR2 and FGFR3 promoter regions using ChOP-qPCR
   (Fig. [216]7h, i). Our analysis revealed a significant enrichment of
   SCAT7 at the FGFR2 (−250 to −750 bp relative to TSS (Fig. [217]7h) and
   FGFR3 (−750 bp to −1 kb relative to TSS) (Fig. [218]7i) promoters. This
   observation led us to check whether SCAT7 mediates the transcriptional
   activation of FGFR2 and FGFR3 via recruiting hnRNPK and YBX1 to the
   FGFR2 and FGFR3 promoters. We detected specific binding of hnRNPK and
   YBX1, as assessed by ChIP assay, at the FGFR2 promoter (−250 to −500 bp
   relative to TSS) and this binding was substantially reduced upon SCAT7
   downregulation (Fig. [219]7j). Similarly, hnRNPK and YBX1 occupied the
   −500 bp to −1 kb promoter region of FGFR3 and their binding was
   affected specifically at the −750 bp to −1 kb promoter region following
   SCAT7 downregulation (Fig. [220]7k).

   Further, we investigated the role of SCAT7/hnRNPK/YBX1 complex in A549
   and Caki-2 cells for transcriptional regulation of FGFR3 and FGFR2,
   respectively. We performed SCAT7 ChOP pull-downs in A549 (Fig. [221]7l)
   and Caki-2 (Fig. [222]7o) cells followed by Western blotting.
   ChOP-Western indicated a specific interaction of SCAT7 with both hnRNPK
   and YBX1 proteins in A549 cells (Fig. [223]7l), but only with hnRNPK in
   Caki-2 cells (Fig. [224]7o). ChOP-qPCR assays in A549 and Caki-2 cells
   revealed SCAT7 occupancy at the promoter regions of FGFR3 (−750 bp to
   −1 kb) and FGFR2 (−250 bp to −500 bp), respectively (Fig. [225]7m, p).
   Similar to our observations in HeLa cells, we detected the occupancy of
   hnRNPK and YBX1 at the −750 bp to −1 kb FGFR3 promoter region in A549
   cells and their occupancy was reduced upon SCAT7 downregulation
   (Fig. [226]7n). In Caki-2 cell line, hnRNPK was enriched at the −250 to
   −500 bp FGFR2 promoter region and its occupancy was affected upon SCAT7
   downregulation (Fig. [227]7q). In addition, we observed a decrease in
   the RNA Polymerase II occupancy over the coding region of FGFRs in
   SCAT7 depleted cells (Supplementary Fig. [228]7d). Taken together,
   these observations indicate that SCAT7/hnRNPK/YBX1 RNP plays a crucial
   role in the transcriptional activation of FGFR2 and/or FGFR3 in
   different cancer models.

SCAT7 as potential therapeutic target in cancer treatment

   Our in vitro data, as well as the mechanistic studies, clearly
   demonstrate the oncogenic nature of SCAT7 and its crucial role in
   promoting cancer-associated signaling pathways. Hence, we wanted to
   elucidate the role of SCAT7 in malignant tumorigenesis in vivo. To this
   end, we generated two different xenograft models engrafted with either
   786-O or A549 SCAT7 stable knockdown cells. Eight weeks
   post-engraftment, both SCAT7 depleted 786-O and A549 xenografts showed
   a significant decrease in growth parameters compared to control
   xenografts. Ki67 immunostaining of the dissected tumors confirmed the
   restricted proliferation capacity of SCAT7 knockdown cells in vivo
   (Fig. [229]8a, b; Supplementary Fig. [230]8a, b).

Fig. 8.

   [231]Fig. 8
   [232]Open in a new tab

   SCAT7 is a potential therapeutic target for different tumor types. a
   Tumor growth in Balb/c nude mice 8 weeks after subcutaneous inoculation
   of 1 × 10^6 Csh or SCAT7-sh2 786-O cells (n = 8 each group). b Tumors
   from Balb/c nude mice after subcutaneous injection of 1 × 10^6 Csh or
   SCAT7-sh1 or SCAT7-sh2 A549 cells for 8 weeks (n = 6 for each group).
   In a and b tumor volumes (cm^3) are expressed as mean ± SD, compared
   with Csh. c Tumor growth inhibition (TGI) for A549 subcutaneous Balb/c
   nude xenografts treated with 60 pmol of SCAT7 antisense
   oligonucleotides, LNA-1 and LNA-2, respectively, for a total of four
   injections (n = 5 for each group). d Average tumor growth of the
   xenografts treated with control or SCAT7 LNAs was calculated at each
   injection as follows: Vtx-Vt0. e Real-time qPCR of SCAT7 in A549 tumors
   collected after treatment with Ctrl-LNA, SCAT7-LNA1, and SCAT7-LNA2.
   The red bar represents a tumor treated with LNA2, but no downregulation
   was observed. Values are normalized to endogenous GAPDH. f Scatter plot
   showing the correlation between SCAT7 expression in vivo and the tumor
   volumes. The red dot represents the tumor that had no significant
   downregulation of SCAT7 upon LNA treatment. g Immunohistochemistry
   images of Ki67 staining and TUNEL assay for A549 xenografts treated
   with Ctrl-LNA, SCAT7-LNA-1, and SCAT7-LNA-2 (blue: DAPI, green:
   TUNEL-GFP). h Patient-derived xenograft (PDX) of NSG mice models
   treated with 100 pmol of Ctrl-LNA or SCAT7-LNA1 for a total of five
   injections (n = 6 for each group). i Average growth of tumor volumes of
   the PDX models treated with control or SCAT7 LNAs. j, k Model depicting
   the functional involvement of SCAT7 in regulating cancer hallmarks (j)
   and mechanism of FGF/FGFR signaling regulation by SCAT7 (k). The
   statistical significance shown in b–e and i was derived using a
   two-tailed unpaired Student’s t-test. Data are plotted as mean ± SD
   (*p ≤ 0.05; **p = 0.01 – 0.001; ***p < 0.001)

   We next tested the hypothesis that SCAT7 can serve as a target for
   therapeutic intervention. Towards this, we engrafted A549 cells
   subcutaneously into nude mice. Six weeks post-engraftment, we applied a
   treatment regimen based on the subcutaneous injection of two
   independent LNA antisense oligonucleotides targeting SCAT7, twice a
   week (Methods). We measured the tumor volumes after a course of four
   injections in two independent experiments and observed 40–50% tumor
   growth inhibition (TGI) in SCAT7 LNA groups compared to the scrambled
   LNA control group (Fig. [233]8c, d; Supplementary Fig. [234]8c). We
   also monitored the weight of the mice during the tumor development and
   post-injections in both experiments to score for any weight loss
   induced by the LNA treatment. Additionally, we measured the weights and
   sizes of liver, spleen, and kidneys to assess the cytotoxicity of the
   treatment and found no difference between the two groups (Supplementary
   Data [235]3). Interestingly, the expression levels of SCAT7 in
   xenografts correlated with tumor volumes (Fig. [236]8e, f) and tumor
   growth reduction was observed only in the tumors that had an efficient
   SCAT7 downregulation. Then, we checked the expression levels of FGFR3,
   FGFR4, and p21 (Supplementary Fig. [237]8d). In line with the in vitro
   data, FGFR3 and FGFR4 downregulation and p21 upregulation was detected
   in tumors treated with SCAT7 LNAs. Ki67 immunostaining, and TUNEL
   staining of the dissected tumors revealed the potent effect of SCAT7
   targeting on cancer malignancy in vivo (Fig. [238]8g). Furthermore, we
   implemented SCAT7 LNA-dependent therapeutic intervention over a period
   of 15 days on a lung metastatic patient-derived xenograft (PDX) mouse
   model bearing an oncogenic mutated form of KRAS (Methods). PDX mice
   models injected with SCAT7 LNA exhibited a significant reduction in the
   growth rate as well as the volumes (~46%) of the engrafted tumors
   (Fig. [239]8h, i; Supplementary Fig. [240]8e). These results
   collectively suggest that SCAT7 is an oncogenic lncRNA and it can be
   used as a possible therapeutic target in the treatment of different
   cancers.

Discussion

   The main aim of the current investigation is based on the hypothesis
   that temporally expressed transcripts across S-phase may harbor crucial
   functions in the control of cell cycle progression and that their
   deregulation may underlie tumor development and progression. In the
   recent past, compared to protein-coding mRNAs, there is a growing
   appreciation for investigating the role of lncRNAs in cancer
   development and progression given their surprising roles in
   spatiotemporal gene expression. Through characterizing the dynamic
   transcriptional changes across S-phase in real time, we identified 1145
   lncRNAs showing S-phase-specific temporal expression patterns.
   Enrichment of cancer-associated pathways in the GO analyses of
   neighboring protein-coding genes of S-phase lncRNAs, and their
   differential expression across Pan-Cancer data sets, indicate that
   S-phase lncRNAs may be strongly associated with cancer development. One
   of the interesting aspects of S-phase lncRNAs’ differential expression
   analysis across TCGA data sets is that nearly 60% of them show
   differential expression at least in two cancer types. To confirm our
   hypothesis, we have selected top eight S-phase lncRNAs that show
   differential expression across multiple cancers and investigated their
   role in cancer progression via analyzing the effect of their
   downregulation on cancer cell hallmarks in various cancer models.
   Confirming our hypothesis, downregulation or overexpression of SCATs
   affected cancer cell hallmarks such as cell cycle progression, cell
   proliferation, apoptosis, cellular senescence, and cell
   invasion/migration, indicating that our cell cycle-based functional
   screen identified potential oncogenic drivers.

   DNA methylation analyses of S-phase lncRNAs revealed that DNA
   methylation may underlie the differential expression of S-phase lncRNAs
   between tumor and normal tissue pairs. For instance, SCAT3 is
   upregulated in six types of cancers (Supplementary Data [241]2) and
   this is consistent with its promoter’s hypomethylation in several
   cancers (HNSC, KIRP, and LIHC). Conversely, our analysis identified a
   number of S-phase lncRNAs showing a hypermethylation pattern that
   correlates with their diminished expression in respective cancers.
   Therefore, our DNA methylation investigations of S-phase lncRNAs
   reflect the role of epigenetic alterations in modulating their
   transcriptional activities during tumorigenesis.

   Another important aspect of the current study is the comprehensive
   investigation of the clinical relevance of the S-phase lncRNAs across
   TCGA data sets. Our analysis identified 633 S-phase lncRNAs that act as
   independent biomarkers with high prediction accuracy (Supplementary
   Fig. [242]2b). Strikingly, our clinical investigation indicated that
   KIRC, COAD, LIHC, and HNSC harbor more than 50 S-phase lncRNAs as
   potential independent prognostic markers. For instance, SCAT8 appeared
   to be the top prognostic indicator in our studies with the higher
   hazard ratio in our multivariate models and it also interferes with
   cancer cell hallmarks, indicating that it may be an oncogenic driver in
   multiple cancers. Further, our clinical investigations identified SCAT5
   as a common independent prognostic biomarker in kidney cancers KIRP,
   KIRC, and KICH. Although SCAT5 was first identified in lung cancer cell
   patients in response to cigarette smoking^[243]36, its role in the
   etiology of different kidney cancers has not been investigated.
   Notably, its strong prognostic significance coupled with effect of its
   downregulation on the cancer cell hallmarks in KIRC cell lines,
   indicate that SCAT5 could be a potential therapeutic target in the
   treatment of kidney cancers. Similarly, SCAT1, which is upregulated in
   10 different cancers, shows a functional involvement and independent
   prognostic capacity in four cancers including LUAD and LUSC. Thus, our
   functional and clinical investigations on SCAT8, SCAT5, and SCAT1,
   suggests that our cell cycle-based functional screen indeed has
   identified potential lncRNA-based cancer drivers. Importantly, our
   analysis indicates that clinically relevant S-phase lncRNAs can be
   considered as independent prognostic biomarkers with a strong potential
   to be used in the clinical setting for better risk stratification and
   prediction of clinical outcome.

   For a further understanding of the mode of action of S-phase-enriched
   lncRNAs in tumorigenesis, we performed a detailed mechanistic
   investigation using SCAT7 as a model. The consistency of the obtained
   phenotypic effects upon SCAT7 knockdown in different model systems
   indicates a conserved functional interaction between SCAT7 and its
   downstream target genes to ensure cellular homeostasis. The crosstalk
   between cell cycle progression, apoptosis, and cellular senescence has
   been firmly established in the maintenance of cellular
   homeostasis^[244]37 and our data clearly demonstrated that the
   silencing of SCAT7 strongly interferes with these interconnected
   biological processes, thereby affecting cellular homeostasis. Upon
   SCAT7 knockdown different types of proliferative cell lines exhibit the
   characteristic features of cellular senescence including
   β-galactosidase secretion, cell cycle arrest, and induction of
   different tumor suppressor genes^[245]38 (p21, p16, and p15). This is
   consistent with the enrichment of biological processes such as cell
   cycle, apoptosis, cell proliferation, and cell senescence. Moreover,
   SCAT7 depletion in multiple cell lines show accumulation of cells in G1
   phase and S-phase progression defects. The drastic reduction in the
   EdU+ve cells upon SCAT7 depletion is also a clear indication of the
   diminished S-phase within DNA-replicating cells. Reduced G1 to S phase
   progression in SCAT7 depleted cells may be due to CCND1 downregulation
   and RB hypophosphorylation. CCND1 downregulation could be the result of
   inhibitory effects of FOXO1/3 factors due to the reduced AKT
   activity^[246]39,[247]40. Though these observations support the role of
   SCAT7 in G1 to S phase progression, more work is needed to ascertain
   the functions of SCAT7 in S-phase progression. Thus, our functional
   investigations unequivocally implicate SCAT7 in the cellular
   homeostasis through regulating the cancer cell hallmarks
   (Fig. [248]8j).

   The dissection of the regulatory pathways mediated by the action of
   SCAT7 indicated its crucial involvement in regulating pivotal signaling
   pathways across multiple cancer models. lncRNAs have been reported to
   regulate signaling pathways like Wnt^[249]41,[250]42, Notch^[251]43,
   PI3K/AKT^[252]44, and RAS/MAPK^[253]45. However, the regulation of
   FGF/FGFR signaling by lncRNAs has not been explored mechanistically in
   great detail. Given that several FGF/FGFR members were deregulated upon
   SCAT7 knockdown in multiple cancer models, we hypothesized a genuine
   connection between SCAT7 and FGF signaling in the context of cancer.
   The functional conservation of SCAT7-hnRNPK-YBX1 RNP complex in
   different cancer models presents a mechanistic model of FGF/FGFR
   regulation. The recruitment of the SCAT7-hnRNPK-YBX1 RNP complex at the
   promoter regions of FGFR2 and FGFR3 promotes transcriptional activation
   of the FGF/FGFR pathway, leading to sustained cell proliferation via
   PI3K/AKT and Ras/MAPK pathways (Fig. [254]8k). It is pertinent to note
   that the identification of hnRNPK and YBX1 as the interacting proteins
   of SCAT7 fits well in the current understanding of the functional roles
   of hnRNPK and YBX1 in the regulation of cell cycle progression, gene
   expression, and cell signaling^[255]34,[256]35,[257]46. However, it
   will be interesting to scrutinize whether cell identity, as shown in
   the KIRC model, affects SCAT7/RNPs interactions in a lineage-specific
   manner. Nevertheless, lineage-specific interactions between lncRNAs and
   RNPs have been shown to be involved in developmentally regulated
   biological functions^[258]23,[259]47–[260]49.

   The functional significance of FGF/FGFR members in normal development,
   maintenance of stem cell properties, and senescence is fairly well
   understood^[261]50. Regulation of FGF/FGFR members by SCAT7 in HeLa,
   KIRC, and LUAD cancer models highlight the functional role of FGF
   signaling in SCAT7-mediated tumor initiation and progression.
   Consistent with our data, several investigations have implicated FGF
   signaling in renal and lung cancers. For instance, a SNP in FGFR2 was
   shown to affect the progression-free survival alone in the metastatic
   KIRC patients undergoing anti-VEGF-targeted therapy^[262]51. Also, a
   missense mutation in FGFR2 was found to drive a durable response to
   nucleolin-targeted therapy in metastatic KIRC^[263]52. Despite the fact
   that various FGFR members harbor activating mutations in lung cancer
   patients and confer acquired resistance to tyrosine kinase inhibitors
   (TKIs)^[264]53,[265]54, only a handful of studies have addressed the
   role of these mutations in LUAD tumorogenesis. Tchaicha et al.
   generated the first genetically engineered lung cancer mouse model
   harboring an FGFR mutation in p53 null background^[266]55. The
   engineered mouse model showed more than 50% tumor regression when
   treated with a pan-FGFR inhibitor. More recently, Manchado et al.
   reported a combinatorial approach including FGFR1 inhibitor to overcome
   the adaptive resistance to MEK inhibitor in KRAS-mutant LUAD^[267]56.
   Considering the functional nexus between SCAT7 and FGF signaling,
   targeting SCAT7 alone is sufficient to inhibit tumor progression via
   repressing different members of the FGF/FGFR pathway. Supporting this,
   SCAT7 LNA-based treatment used in this study established a possible
   therapeutic intervention regimen for multiple cancers in vivo. Based on
   these observations, we suggest that a combinatorial treatment strategy
   involving SCAT7 repression alongside treatment with potent FGFRs
   inhibitors or TKIs will hold promise for lncRNA-based therapeutics.

   Collectively, we provide a comprehensive list of lncRNA-based oncogenic
   drivers with potential prognostic value. More importantly, this
   systematically analyzed functional and clinically relevant lncRNAs can
   serve as a resource for delineating the functional link between lncRNA
   and tumor development and progression.

Methods

Cell lines and cell synchronization

   The adherent Caki-2, 786-O, and HepG2 cell lines were purchased from
   CLS-GmbH (Germany). The LUAD cell lines A549 and H2228 were kindly
   provided by Bengt Hallberg’s lab at the department of Medical
   Biochemistry and Cell Biology, University of Gothenburg. HeLa and MCF-7
   cell lines were routinely maintained in our lab. The immortalized human
   fibroblasts cell lines, BJ-BRAF and TiG3-BRAF, were kindly provided by
   Andres Lund’s lab (Biotech Research and Innovation Center, University
   of Copenhagen). We cultured Caki-2, 786-O, and H2228 cell lines in
   RPMI-1640 medium (Gibco, Life Technologies, USA). A549, BJ-BRAF,
   TiG3-BRAF, and MCF-7 cell lines were maintained in DMEM medium (Gibco,
   Life Technologies; USA). HeLa and HepG2 cell lines were maintained in
   MEM medium (Gibco, Life Technologies; USA). All media were supplemented
   with 2 mM l-glutamine, 1× penicillin-streptomycin antibiotic, and 10%
   fetal bovine serum. All cell lines were tested negative for Mycoplasma
   contamination. For RT-qPCR validation, HeLa cells were synchronized
   following the addition of 2 mM of thymidine into a fresh medium for
   10 h then aspirating the medium and adding hydroxyurea to a final
   concentration of 1 mM overnight. The synchronized cells were collected
   at 2, 3.5, 5, and 9 h representing T1, T2, T3, and T4, respectively.
   For serum starvation, HeLa, A549, and Caki-2 cells were cultured in
   serum-free media for 36–44 h. The synchronized HeLa cells were
   collected at the indicated time points while Caki-2 cells were
   harvested at 4, 8, and 20 h representing T1, T2 and T3, respectively.
   In A549 cells, T1, T2, and T3 synchronized cells were harvested at 4,
   6, and 18 h, respectively.

Nascent RNA capture assay

   To capture nascent RNA and total RNA from different stages of S-Phase,
   on day 1 HeLa cells were plated at 500,000 cells per F75 plate, to
   ensure cell confluency of around 35% during synchronization. On day 2
   cells were incubated for 10 h in medium supplemented with 2 mM
   thymidine (Sigma). After 10 h, cells were washed with PBS and incubated
   with medium supplemented with 1 mM hydroxyurea (Sigma) for 14 h. The
   synchronized cells are now just at the beginning of S-phase with a
   confluency of 70%, which allowing them to resume dividing upon release
   from cell cycle block without suffering from contact inhibition. After
   a brief PBS wash, medium is then changed back to normal, and the cells
   are allowed to progress through S-phase in a synchronized manner. The
   time at which the block is released is thereafter termed “T0”. The
   labeling of nascent RNA was carried out for 2-h periods at different
   stages of S-phase. At the beginning of the labeling period, media was
   supplemented with EtU (Invitrogen) to a final concentration of 1 mM,
   and cells were harvested 2 h later. The labeling periods were defined
   as follows: T0h–T2h (beginning of S-phase), T1.5h–T3.5 h (middle of
   S-phase), and T3h–T5h (end of S-phase). This labeling protocol ensured
   that, for all three S-phase samples, the labeled nascent transcripts
   would represent only 2 h of transcription, in order to provide a
   detailed and accurate picture of the timing of transcriptional events
   occurring throughout S-phase. For unlabeled total RNA samples, we
   followed the same procedure but with medium without EtU supplement.

   Cells from EtU labeled and unlabeled samples were harvested at the end
   of their respective 2 h labeling period, and RNA were extracted with
   Tri reagent (Ambion). We performed DNA digestion with RQ1 DNase I
   (Promega) for 1 h at 37 °C, and re-extracted RNA with Tri reagent. An
   aliquot of 10 µg RNA was re-suspended in low volume and rRNA depletion
   was carried out with Ribominus kit (Invitrogen). From each S-phase
   sample, 2, 3 µg of rRNA-depleted RNA was biotinylated with Click-It
   Nascent RNA Capture kit (Invitrogen) following manufacturer’s protocol.
   The biotinylated RNA was captured using streptavidin magnetic beads.
   The captured biotinylated RNA was eluted by incubating the beads in
   200 µl buffer containing 2 mM biotin, 1 M NaCl, 50 mM MOPS, 5 mM EDTA,
   2 M 2β-Mercaptoethanol, pH 7.4 for 3 min at 95 °C. Supernatant
   containing eluted RNA was recovered immediately after heating and the
   RNA was precipitated in 30 µl 3 M sodium acetate (pH 5.2), 1 µl
   glycoblue (Invitrogen), 750 µl 100% ethanol. RNA was then resuspended
   in nuclease-free water, dosed on a Qubit fluorometer (Invitrogen), and
   RNA size profile was assessed on Bioanalyzer (Agilent) with RNA pico
   6000 kit and sent for library construction and sequencing on a Solid
   platform at Uppsala Genome Center. For unlabeled RNA samples, total RNA
   was extracted and subjected to rRNA depletion, and used for
   RNA-sequencing.

Knockdown of target genes and cloning

   We performed transient transfection using siRNA, esiRNA, and
   antisense LNA^™GapmeR molecules. Scramble siRNA, custom-made siRNAs,
   and pre-designed esiRNAs were designed and synthesized by
   Sigma-Aldrich. We obtained both in vivo and in vitro grade negative
   control and custom-made LNA^™GapmeR molecules from Exiqon. Transfection
   was carried out in standard 24-well plates using Lipofectamin® RNAiMAX
   transfection reagent (Invitrogen, California) according to the
   manufacturer’s instructions with a final concentration of 35 pmol and
   20 pmol/well of siRNA and LNA, respectively. The transfections were
   performed in three biological replicates and the efficiency of KD was
   confirmed by RT-qPCR. We generated stable cell lines using Lentifect™
   Purified shRNA lentivirus particles designed and synthesized by
   GeneCopoeia™ to target SCAT7 or scramble negative control. The
   transduction efficiency was visualized by the integrated GFP reporter
   gene. The stable 786-O, Caki-2, and A549 KD cells were selected using
   2 µg/ml, 3 µg/ml, and 2.5 µg/ml of puromycin, respectively. All
   custom-made siRNAs, LNAs, and shRNAs particles were designed to target
   the unique non-overlapping exons of the respective transcript. For
   SCAT7 overexpression, the SCAT7 fragment was cloned into pGEM-T Easy
   vector and the correct orientation was verified by sequencing. The
   confirmed clone was digested using NotI and HindIII enzymes and
   sub-cloned into the mammalian overexpression vector pcDNA3.1. HeLa,
   Caki-2, and A549 cells were transfected with either 1 µg of
   pcDNA3.1-SCAT7 vector or pcDNA3.1 empty vector using Lipofectamin® 2000
   transfection reagent. The RNA levels were measured with RT-qPCR. The
   sequences of siRNAs, shRNAs, LNAs, primers, and antibodies are listed
   in Supplementary Data [268]4.

Flow cytometry, cell cycle, and Click-iT EdU assay

   We assessed the cell cycle profiles of transient KD cells and control
   cells 48 h post-transfection. The media were aspirated, and the cells
   were washed with PBS, trypsinized, pelleted by centrifugation, washed
   twice with cold PBS, fixed with ice-cold 70% ethanol, and stored at
   −20 °C for at least 2 h. The fixed cells were re-collected by
   centrifugation, re-suspended in PBS, and kept for 30 min at 37 °C.
   Then, cells were collected and stained with PI solution containing 1%
   RNase A in PBS and kept at 4 °C for at least 4 h. The PI-stained cells
   were assayed using Eclipse single-cell flow cytometry system ec800 and
   data were analyzed with the manufacturer’s software. The cell cycle
   profiling was validated on another system using NucleoCounter NC-3000
   platform (Chemometec, Denmark). The fixed cells were stained with DAPI
   solution provided by the manufacturer and analyzed according to
   manufacturer’s instructions. All KD experiments were assayed three
   times independently and statistical significance was derived using
   two-sided unpaired Student’s t-test. The EdU incorporation assay was
   performed according to the manufacturer’s protocol using the Click-iT™
   EdU Alexa Fluor™ 488 Imaging Kit and the green fluorescence wad
   detected using the EVOS FL Auto Cell Imaging System.

Northern blotting analysis

   Total RNA was extracted from wild-type A549 and SCAT7-KD cells and an
   equal amount of 10 µg of each sample was loaded into 1% formaldehyde
   agarose gel alongside 1.0 µg of 0.5–10 kb RNA ladder (Invitrogen). We
   used 1 × MOPS-formaldehyde as a running buffer in RNase-free
   conditions. Following the gel electrophoresis, the wet transfer was
   performed overnight using 20 × SSC as a transfer buffer at room
   temperature. After the transfer, the membrane was fixed under UV then
   it was cut and immediately incubated 3 h with rapid-hyb hybridation
   buffer (GE Healthcare Life Sciences) for pre-hybridization at 42 °C.
   The radioactive probes were prepared freshly for SCAT7 and RNA ladder.
   For SCAT7, we utilized the T4 PNK enzyme (NEB) to label the SCAT7 short
   probes (the same probes used in ChOP experiment) using γ^32-P ATP
   (PerkinElmer). For the RNA ladder probes, we used the RNA ladder as a
   template to synthesis the complementary probes using labeled α-dCTP in
   a reverse transcription reaction. The probes were purified using G25
   columns (GE Healthcare Life Sciences) then they were hybridized
   separately with the corresponding membrane at 42 °C overnight. The
   membrane was washed twice at room temperature with low stringent buffer
   (2 × SSC and 0.1% SDS) for 10 min each followed by two washes of warm
   high stringency buffer (0.1 × SSC and 0.1% SDS) at 55 °C for 15 min
   each. The membranes were left for exposure overnight and scanned with
   Fuji FLA7000 phosphoimager.

Overview of annotations and tools used in this study

   The hg19 (GRCh37) genome version for alignment and transcript
   annotation from GENCODE version 19 (equivalent Ensembl GRCh37)^[269]57
   was used for processing RNA-sequencing samples. Color space reads from
   SOLID sequencing platform were aligned using LifeScope, and
   HISAT^[270]58 aligner was used for reads from Illumina sequencing
   platform. The reads for gencode transcripts were quantified using
   HTSeq-count^[271]59 with ‘intersection-strict’ mode.

Sequencing and alignment

   For each S-phase time point, RNA from two independent experiments were
   pooled into one sample and deep sequenced on SOLID sequencing platform
   at Uppsala Genome Center. The alignment resulted in 18.5, 39.3, 21.3,
   and 29 million mappable 75 bp reads for unsynchronized, S-phase T1,
   S-phase T2, and S-phase T3 EtU-labeled samples, respectively. For
   unlabeled samples, we obtained 19.6, 30, 29, and 27 million mappable
   75 bp reads for unsynchronized, S-phase T1, S-phase T2, and S-phase T3
   samples, respectively.

Transcript annotation and read quantification

   The uniquely mapped reads were assigned to long noncoding transcripts
   from Gencode v19 annotation and obtained 4039 and 3966 lncRNAs from EtU
   labeled and unlabeled samples, respectively, which are expressed at
   least at one time point of S-phase. The obtained reads were normalized
   to their library sizes and transcript length (reads per kilobase of
   transcript per million—RPKM normalization). The log-fold change values
   were derived from obtained RPKM values by comparing individual
   time-point against unsynchronized samples.

Time-series analysis to identify lncRNAs across S-phase

   The lncRNAs having log-fold change greater than one in at least one
   S-phase time-points over unsynchronized sample were taken for further
   analysis. We obtained 1734 and 1674 lncRNAs in EtU labeled and
   unlabeled samples respectively. The Short Time-series Expression Miner
   (STEM) clustering which is specifically designed to handle short
   time-series gene expression data was used to find the significant
   temporal patterns in S-phase-derived lncRNAs. The significant temporal
   expression patterns were obtained from expression profiles of S-phase
   lncRNAs in three different time-points (2, 3.5, and 5 h) by assuming
   unsynchronized sample as a base time point. There were 1145 (out of
   1734) lncRNAs in EtU labeled and 937 (out of 1674) lncRNAs in unlabeled
   samples, showed significant temporal expression patterns across S-phase
   as in Fig. [272]1c and Supplementary Fig. [273]1a.

E2F1-bound EtU-labeled S-phase lncRNAs

   E2F1 transcription factor ChIP-seq peaks were obtained for HeLa cell
   line from ENCODE (ENCSR000EVJ) hg19 processed files. Using Homer
   ‘annotatePeaks.pl’, the E2F1 peaks were associated with promoter of the
   transcripts from Gencode v19 annotation. Individual peaks are assigned
   to the promoter if it is within ±2 kb to the TSS of a transcript.

Functional significance of nearby protein-coding genes

   The nearby protein-coding genes to EtU labeled, unlabeled and common
   S-phase lncRNAs were tested for their functional significance. We
   observed most of the first hit to the S-phase lncRNAs are within 50 kb
   distance. Protein-coding gene first hit within 50 kb window relative to
   S-phase lncRNAs from EtU labeled and unlabeled samples were extracted
   using BedTools ‘closest’ with parameters ‘-D ref’ and further used for
   functional enrichment analysis. GeneSCF v1.1^[274]24 was used to
   perform gene enrichment analysis. It is important for an enrichment
   tool to be updated frequently to get more reliable results and avoid
   misinterpretation of the data^[275]60. The major advantage of GeneSCF
   is that it uses the updated or recent terms and updated genes from the
   databases. GeneSCF is more reliable compared to most enrichment
   analysis tools because it has real-time based enrichment analysis
   feature. The code for GeneSCF is also regularly updated to accommodate
   the changes from the source database like KEGG, Gene Ontology, and
   Reactome. In this study, the terms are considered significant only if
   it is enriched with p < 0.05 and with minimum of 10 genes involved in a
   particular function. These genes were tested against two different
   functional annotations, Gene Ontology biological process (GO-BP), and
   KEGG pathways by assuming all protein-coding gene numbers from Gencode
   v19 as background (20,345) or reference set.

Processing S-phase lncRNAs in multiple cancers from TCGA

   We have aligned RNA-sequencing reads using HISAT from 16 cancer types
   from TCGA. The read abundance was quantified for Gencode v19
   transcripts using HTSeq-count in “intersection-strict” mode (options
   --no-mixed --no-discordant --no-unal –known-splicesite-infile) for
   obtaining the read counts per transcript. The obtained reads (counts)
   were normalized to their library sizes and transcript length (RPKM
   normalization). Using these normalized counts, the significance of
   differential expression between normal samples and corresponding solid
   tumor was obtained using Wilcoxon signed-rank non-parametric test and
   corrected for multiple testing with Benjamini–Hochberg’s method (FDR).
   The cell cycle-associated lncRNA to be considered as significantly
   differentially expressed cancer-associated transcript, we used log-fold
   change ± 2 and FDR < 1E–004 in at least one cancer type as criteria.
   These cancer-associated lncRNAs were further subjected to clinical
   analysis. The information on each cancer type and the number of tumor
   and normal tissue samples processed for individual cancer type is
   provided in Supplementary Data [276]2.

Processing S-phase lncRNAs for TCGA CpG methylation data

   Processed differential methylation data was downloaded from
   COSMIC^[277]28 repository for GRCh37 genome and version 74 transcript
   annotation equivalent to Gencode v19 annotation. COSMIC analysis
   predicted differentially methylated regions (DMRs) by comparing the
   beta-values derived from TCGA data level 3 for tumor and matched normal
   samples. The cancer types containing more than 19 normal samples were
   considered for statistical analysis. The p-values for the comparisons
   were obtained using Mann–Whitney test and corrected p-values using
   family wise error rate (FWER). The significant DMRs were filtered on
   the basis of FWER < 0.05. The above information for statistical
   analysis was extracted from COSMIC repository.

   The significant DMRs from COSMIC were assigned to promoter (−2000 bp
   and +500 bp from TSS) and gene body (TSS + 550 to length of transcript)
   regions of the cancer-associated lncRNAs from our study. We considered
   any lncRNA as differentially methylated only if more than 10 patient
   samples support the methylation status and also absence of any patients
   which supports opposite methylation pattern for the same region. As an
   example, the promoter region has to be considered as hypermethylated if
   there were at least 10 patients supporting hypermethylation status but
   no patients (null) show hypomethylation at the same promoter region or
   other regions within the transcript. All presented hypo and
   hypermethylation lncRNAs has FWER < 0.05 obtained with matched normal
   and tumor comparison in respective cancer types. The number of tumor
   samples used for the methylation analysis for each cancer type is
   included in Supplementary Data [278]2 and the detailed description on
   COSMIC differential methylation analysis is available at
   “[279]http://cancer.sanger.ac.uk/cosmic/analyses” under “Methylation”
   sub-heading.

Clinical investigation of S-phase lncRNAs in TCGA tumors

   For clinical investigations, we have chosen 1145 lncRNAs that were
   showing significant temporal expression in EtU-labeled samples. The
   selected lncRNAs were dichotomized with respect to the expression
   cut-off based on either mean, median, or quartiles and considered
   whichever gave the best discrimination. Thereafter, overall survival
   rate in patients above and below the selected cut-off levels, was
   calculated according to the KM method, and the log-rank test was used
   to assess differences in survival. We used survival ROC R package, in
   which the time-dependent ROC is calculated using the KM estimator for
   the expression cut-offs^[280]61. Points above the diagonal represent
   good classification results (better than random), points below the line
   poor results (worse than random). AUC < 0.5 was filtered to prevent
   randomness in the discrimination of poor and good survival groups. To
   further investigate the relation to survival, dichotomization and
   continuous levels of S-phase lncRNAs were assessed in a Cox regression
   model and hazard ratios calculated. Based on expression distribution,
   Student’s t-test or Wilcoxon rank sum test was used to evaluate mean
   expression of SCATs in relation to clinical parameters such as node (N0
   vs. N1_N3), stage (Tumor stage 1 & 2 vs. Tumor stage 3 & 4), size
   (Tumor size 1 & 2 vs. Tumor size 3 & 4), metastasis (M0 vs. M1), grade
   (Tumor grade 1 & 2 vs. Tumor grade 3 & 4), and age ( <  median age
   vs. > median age). We constructed a multivariate Cox regression model
   including clinical parameters and S-phase lncRNAs expression that were
   significant in univariate analysis to determine the prognostic effect.

   Brier score was used to assess the prediction error in the model. Pec
   from the R package was used for Brier score prediction. All the
   statistical analyses were performed using R package and p < 0.05 was
   considered as significant. We considered the lncRNA as a potential
   prognostic marker, if they showed significant survival difference in
   log-rank test and had a statistically significant hazard value in coxph
   regression analysis either in dichotomization or in continuous scale.
   Survival analysis and coxph regression analyses were performed using
   Survival package in R.

Sequencing and differential expression analysis

   The reads were cleaned for adapter sequence using Trimmomatic (v 0.32)
   and the alignment of cleaned reads was done with hg19 reference genome
   using HISAT aligner (mode: --sensitive --qc-filter). We used Gencode
   v19 annotation to quantify the read abundance using HTSeq-count. The
   differential gene expression between knockdown (KD) and control cells
   was analyzed using bioconductor package edgeR^[281]62. Genes were
   tested for differential expression only if expression (CPM) is greater
   than 1 in at least two samples of comparison groups. The significant
   candidates were filtered with log-fold change > ± 1 and FDR ≤ 0.05.

Pathway enrichment analysis of deregulated genes

   The significantly deregulated genes from siRNA knockdown samples were
   tested for pathway enrichment using tool GeneSCF v1.1. The parameters
   for GeneSCF were set to database Reactome with background genes 20,345
   (all protein-coding genes from Gencode v19 annotation used in this
   study). The enriched functions were filtered based on p < 0.05 and
   minimum of 10 genes involved in corresponding process.

Proliferation and vitality assays

   We assayed the proliferation capacity of transiently transfected cells
   48 h post-transfection using CellTiter 96® Non-Radioactive
   Proliferation Assay kit (Promega, USA) with some modifications to the
   manufacturer's protocol. The media were aspirated and cells were washed
   once with PBS, and 425 µl of fresh medium plus 75 µl of MTT dye were
   added and incubated at 37 °C in dark for 4 h. Then, each reaction was
   terminated using 500 µl of stop solution and the cells were kept
   overnight in dark at 4 °C to solubilize the MTT dye. The dye intensity
   was measured using microplate reader at 570 nm. Standard deviation (SD)
   and statistical significance were derived from three independent
   experiments. For assaying the proliferation capacity of stable KD cell
   lines, we seeded the same number of control and KD cells and applied
   the same protocol used in the transient transfection experiments. We
   performed the vitality assay for transient KD cells and stable KD cell
   lines using NucleoCounter NC-3000 platform. The cells were harvested
   and stained with a mixture of VB-48™, PI, and acridine orange dyes
   according to the manufacturer’s instructions. The results were viewed
   and analyzed by the manufacturer’s software.

Apoptosis and senescence assays

   We performed apoptosis assay for transiently transfected cells 48 h
   post-silencing using Caspase-Glo® 3/7 Assay (Promega, USA) and measured
   the luminescence according to the manufacturer’s instructions. For
   stable KD cells, in addition to the Caspase-Glo® 3/7 Assay, we
   performed fluorescent-based Guava Caspase 3/7 FAM Assay (Merk
   Millipore, Germany) following the manufacturer’s instructions. We
   analyzed the caspase 3/7 FAM activity using NucleoCounter NC-3000
   platform. For detection of senescence in fibroblasts and HeLa cells, we
   carried out transient transfections for 72 h. In case of A549 stable KD
   cells, we seeded the cells at less confluency for 72 h. We then
   detected the senescent cells using Senescence β-Galactosidase Staining
   Kit (Cell Signaling Technology, USA) following the manufacturer’s
   instructions.

Soft agar colony-forming assay

   We used a standard procedure for soft agar colony-forming assays in a
   24-well plate. In each well, we plated 500 µl of 1:1 mix of 1%
   molecular biology grade agar and 2 × medium (RMPI 1640 or DMEM)
   supplemented with 20% FBS, then agar layer was left to harden for
   30 min. The soft agar layers were prepared by mixing either stable KD
   cells or control cells with a 500 µl mix of 1:1 0.6% agar and
   2 × medium supplemented with 20% FBS. The soft agar layers containing
   2500 cells/well were left for 15 min to harden and then we added 500 µl
   of 1 × RPMI 1640 or DMEM supplemented with 10% FBS on the top of the
   agar layers to prevent any possible dehydration. For each cell line, we
   plated 10 wells. After 10 days of incubation at 37 °C, we captured
   pictures of each well using an automated Z-stack function of the EVOS™
   FL Auto Cell Imaging System (ThermoFisher Scientific). We counted the
   number of total colonies in each condition and measured the surface
   area of at least 20 representative colonies of each cell line.
   Statistical significance was derived using two-sided unpaired Student’s
   t-test.

Cell migration and invasion assays

   We assayed the migration properties of stable KD cells using OrisTM
   Universal Cell Migration Assembly Kit (Platypus Technologies). Briefly,
   stable KD or control Caki-2, 786-O, and A549 cells were seeded into a
   96-well plate with Oris Cell Seeding Stoppers at 1.3 × 10^4, 1 × 10^4,
   and 1.7 × 10^4 cells per well, respectively. To create the detection
   area, the stoppers were removed after 16 h; stoppers were left in place
   for the reference wells (t = 0 pre-migration control) until the results
   are read. We used EVOS™ FL Auto Imaging System (Life Technologies) to
   detect cell migration at 8 and 24 h post seeding in case of Caki-2 and
   786-O cell lines, and 24 and 48 h in case of A549 cells. The area of
   pre-migration (t = 0) and post-migration (t = 8 and 24 h) were
   calculated for each condition.

   Transwell invasion assay was performed using the 24-well plates BD
   BioCoat Matrigel Invasion Chamber (BD Biosciences) with 8 µm inserts.
   For transiently transfected cells, we first performed siRNA and
   scramble sequence transfection, as described previously. Eight hours
   post-transfection 3.5 × 10^4 Caki-2 transfected cells were re-suspended
   in 1% FBS RPMI-1640 media and seeded into matrigel-coated inserts. For
   Caki-2 and A549 stable KD cell lines, 3 × 10^4 cells were re-suspended
   in the appropriate medium supplemented with 1% FBS and seeded into the
   inserts. Lower chambers were filled with 500 µl of complete medium with
   10% FBS as a chemoattractant agent. Invasion chambers were incubated at
   humidified 5% CO[2] incubator at 37 °C for 22 h. Non-migrated cells
   were scraped from the interior of the inserts by using a cotton-tipped
   swab. Cells on the lower surface of the membrane were fixed and stained
   using the Snabb-Diff kit (Labex), according to the manufacturer´s
   instructions. After staining, the inserts were washed twice in
   distilled water, and then the membranes were removed from the inserts
   and kept in slides. Invading cells were photographed at ×20
   magnification, and the total number of migrated cells was counted using
   the EVOS™ FL Auto Imaging System (Life Technologies).

Chromatin oligo-affinity precipitation

   The ChOP assay was performed as described before by (Akhade et
   al.)^[282]63 with some modifications. For identification of SCAT7
   interacting proteins, HeLa cells (20 × 10^6) were UV crosslinked. The
   crosslinked pellet was obtained by centrifugation at 1000 × g at 4 °C
   for 10 min. Cells were resuspended in 2 ml of buffer A (3 mM MgCl[2],
   10miM Tris-HCl, pH 7.4, 10 mM NaCl, 0.5%v/v NP-40, 0.5 mM PMSF and 100
   units/ml RNasin) and incubated on ice for 20 min. Nuclei were harvested
   by centrifugation and resuspended in 1.2 ml of buffer B (50 mM
   Tris-HCl, pH 7.4, 10 mM EDTA, 0.5% Triton X-100, 0.1%SDS, 0.5 mM PMSF,
   and 100 units/ml RNasin) and incubated on ice for 40 min. An equal
   volume of buffer C (15 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1 mM EDTA, 1%
   Triton X-100, 0.5 mM PMSF, and 100 units/ml RNasin) was then added and
   incubated on ice for 15 min. Samples were briefly sonicated using a
   Bioruptor sonicator (Diagenode) for 20 cycles (30 s on, 30 s off at
   High Pulse). Eight different oligos complimentary to SCAT7 were pooled
   with a final concentration of 10 µM and then used for the RNA pull
   down. As a control, a pool of eight oligos in reverse orientation to
   the SCAT7 targeting oligos was used. The oligos were added to the
   chromatin solution along with yeast tRNA (100 µg/ml), salmon sperm DNA
   (100 µg/ml), and incubated overnight at 4 °C. Samples were then
   incubated with streptavidin agarose beads for 3 h followed by one wash
   of each Low salt buffer (20 mM Tris-HCl, pH7.9, 150 mM NaCl, 2 mM EDTA,
   0.1% SDS, 1% TritonX-100, 0.5 mM PMSF, and 50 units/ml RNasin), and
   high salt buffer (20 mM Tris-HCl, pH7.9, 500 mM NaCl, 2 mM EDTA, 0.1%
   SDS, 1% Triton X-100, 0.5 mM PMSF, and 50 units/ml RNasin) and three
   washes of 1 × PBS. The protein complex was eluted from the beads by
   incubation with PBS + 0.1%SDS with intermittent mixing at 80 °C for
   10 min. All biotinylated oligonucleotides used in the ChOP pull-down
   are listed in Supplementary Data [283]4. Few changes were done to the
   above protocol while assessing the occupancy of SCAT7 at FGFR2
   promoter. These include (1) formaldehyde crosslinking of cells (instead
   of UV crosslinking), (2) sonication for 40 cycles (instead of 20
   cycles) (3) inclusion of BSA (400 µg/ml) in the oligo binding buffer
   (4) DNA isolation using phenol–chloroform method after elution from the
   beads. The % input calculations were made considering the percentage of
   input chromatin used and the Ct values obtained for the target promoter
   region from input DNA and ChOP DNA as follows:
   [MATH: <mi>%</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">Input =</mi><mi>%</mi><mspace
   width="0.3em"></mspace><mi mathvariant="normal">of</mi><mspace
   width="0.3em"></mspace><mi mathvariant="normal">starting</mi><mspace
   width="0.3em"></mspace><mi mathvariant="normal">input</mi><mspace
   width="0.3em"></mspace><mi
   mathvariant="normal">fraction</mi><mo>×</mo><msup><mrow><mn>2</mn></mro
   w><mrow><mo>∧</mo></mrow></msup><mfenced close="]" open="["
   separators=""><mrow><mi mathvariant="normal">Ct</mi><mfenced close=")"
   open="(" separators=""><mrow><mi
   mathvariant="normal">input</mi></mrow></mfenced><mi
   mathvariant="normal">-Ct</mi><mfenced close=")" open="("
   separators=""><mrow><mi
   mathvariant="normal">ChOP</mi></mrow></mfenced></mrow></mfenced> :MATH]

Chromatin and RNA immunoprecipitation

   ChIP was performed according to the protocol described in ref. ^[284]64
   with some modifications. HeLa cells (20 × 10^6) were harvested and
   crosslinked using 1% formaldehdye for 10 min at room temperature
   followed by quenching using 0.125 M of glycine for 5 min. The
   crosslinked pellet was obtained by centrifugation at 1000 × g at 4 °C
   for 10 min. The cell pellet was resuspended in 1 ml of SDS lysis buffer
   (0.1% SDS, 0.5% Triton X-100, 20 mM Tris-HCl, pH 8, and 150 mM NaCl,
   1 mM PMSF, and 100 units/ml RNasin) and incubated on ice for 30 min and
   sonicated using a Bioruptor for a total of 40 cycles (30 s on, 30 s off
   at High Pulse). Insoluble material was removed by centrifugation at
   13000 × g at 4 °C for 10 min. Sonicated DNA was enriched in the range
   of 100–500 bp. The lysate was incubated with the respective antibodies
   for immune-precipitation overnight at 4 °C. An aliquot of 4 μg of each
   antibody was used per 1 mg of lysate for immune-precipitation. The
   immune-complexes were allowed to bind to Protein G/A Dynabeads for 3 h
   at 4 °C. The immune-complexes bound to beads were obtained by magnetic
   precipitation followed by one wash of each low salt buffer (0.1% SDS,
   1% Triton-X 100, 2 mM EDTA, 20 mM Tris-HCl, 150 mM NaCl, 0.5 mM PMSF,
   and 50 units/ml RNasin) and high salt buffer (0.1% SDS, 1% Triton-X
   100, 2 mM EDTA, 20 mM Tris-HCl, 500 mM NaCl, 0.5 mM PMSF, and 50
   units/ml RNasin). The immune-precipitated material was eluted from the
   beads by adding 400 μl of elution buffer (100 mM NaHCO[3], 1% SDS,
   0.5 mM PMSF and 50 units/ml RNasin) and the samples were incubated at
   55 °C for 30 min. The eluates were then processed for DNA isolation by
   phenol–chloroform method. RIP was performed as described
   earlier^[285]23. The eluates from the RIP were processed for total RNA
   isolation by Trizol method. A aliquot of 250 ng of RNA was used for
   cDNA synthesis. The % input calculations were made considering the
   percentage of input chromatin used and the Ct values obtained for the
   target promoter region from input DNA and ChIP DNA as follows:
   [MATH: <mi>%</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">Input = %</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">of</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">starting</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">input</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">fraction</mi><mo>×</mo><msup><mrow><mi
   mathvariant="normal">2</mi></mrow><mrow><mo>∧</mo></mrow></msup><mfence
   d close="]" open="[" separators=""><mrow><mi
   mathvariant="normal">Ct</mi><mfenced close=")" open="("
   separators=""><mrow><mi
   mathvariant="normal">input</mi></mrow></mfenced><mo>-</mo><mi
   mathvariant="normal">Ct</mi><mfenced close=")" open="("
   separators=""><mrow><mi
   mathvariant="normal">ChIP</mi></mrow></mfenced></mrow></mfenced> :MATH]

Droplet digital PCR

   Droplet digital PCR was performed using the One-Step RT-ddPCR Advanced
   Kit (BioRAD #1864021) as per the manufacturer’s instructions. The PCR
   was performed in CFX96 thermal cycler and quantification was done with
   QX200 droplet reader.

Mouse xenografts and PDX models

   KIRC and LUAD xenograft models were generated using stable KD cells.
   786-O and A549 stable KD cells or control cells were re-suspended in
   cold PBS with 20% matrigel. We engrafted 1 × 10^6 cells subcutaneously
   in the right flank of 6-week-old female Balb/C nude mice (Janvier
   labs). Eight weeks post-engrafting, we dissected and measured tumors
   volumes using the following formula: (long side) × (short side)^2/2.
   For the treatment of LUAD tumors with LNA, we engrafted 1 × 10^6
   wild-type A549 cells subcutaneously. We began treatment with in vivo
   grade LNA GapmeR molecules at a final concentration of 0.1 mg/Kg when
   the outside volume of the majority of the tumors reached around
   0.5 cm^3. We used four mice per group. After terminating the
   experiment, we dissected the tumors and collected blood samples,
   livers, spleens, and kidneys from all mice. We obtained the
   immunocompromised NSG mice models engrafted with patient-derived tumors
   from the PDX Live™ library at The Jackson Laboratory (Model ID:
   TM00302). We validated the expression of SCAT7 in the obtained
   xenografts by analyzing the RNA-seq data of the original early passaged
   tumors provided confidentially by The Jackson Library. The mice were
   kept for one week to acclimatize prior to the LNA injection. For each
   group (n = 6), we injected either 100 pmol of scrambled LNA or SCAT7
   LNA1 every three days for a total period of 15 days. The tumor volumes
   and growth rates were calculated using the same previously mentioned
   formula. The animal study protocol was reviewed and approved by the
   Animal Ethical Review Board, University of Gothenburg, Sweden (Ethical
   permit no. 45–2015).

Antibodies and raw Western blots

   Uncropped scans of all western blots are shown in Supplementary
   Figs. [286]9 and [287]10. The list of all antibodies used along with
   their catalog number and dilutions used is provided as Supplementary
   Data [288]5.

Data availability

   The data associated with this publication have been deposited in GEO:
   [289]GSE92250. All other data are available from the authors upon
   reasonable request.

Electronic supplementary material

   [290]Supplementary Information^ (3.4MB, pdf)
   [291]Peer Review File^ (766.7KB, pdf)
   [292]41467_2018_3265_MOESM3_ESM.pdf^ (172.7KB, pdf)

   Description of Additional Supplementary Files
   [293]Supplementary Data 1^ (1.3MB, xlsx)
   [294]Supplementary Data 2^ (825.1KB, xlsx)
   [295]Supplementary Data 3^ (1MB, xlsx)
   [296]Supplementary Data 4^ (14.7KB, xlsx)
   [297]Supplementary Data 5^ (12.5KB, xlsx)

Acknowledgements