Abstract

   Cholangiocarcinoma (CCA) is an aggressive malignancy associated with
   unfavorable prognosis, and it’s difficult to diagnose and no effective
   treatments are available. Long non-coding RNAs (lncRNAs) play important
   roles in tumorigenesis and metastasis. Intact lncRNAs from exosomes
   have sparked much interest as potential biomarker for the non-invasive
   analysis of disease. Here, via exosome sequencing on lncRNAs, GO
   analysis, KEGG pathway and co-expression analysis, receiver operating
   characteristic curve and survival analyses, we found that, compared
   with control group, lncRNAs of ENST00000588480.1 and ENST00000517758.1
   showed significantly increased expressions in CCA group. Moreover, area
   under the curve (AUC) was increased to 0.709 when combined the two
   lncRNAs, they had a sensitivity and specificity of 82.9% and 58.9%
   respectively. Further, the higher levels of the two lncRNAs showed a
   significantly increasing trend with the advancement of cancer TNM
   stages, and prognosticated a poor survival. In addition, KEGG pathway
   analysis showed that the most significant difference term was “p53
   signaling pathway” (KEGG ID: hsa04115, p: 0.001). The altered lncRNAs
   and their target genes were included to reconstruct a co-expression
   network. These altered lncRNAs were mainly related to cellular
   processes, environmental information processing and organismal systems,
   etc. Collectively, our findings provided the potential roles of lncRNAs
   of ENST00000588480.1 and ENST00000517758.1 in CCA, and implicated these
   lncRNAs as potential diagnostic and therapeutic targets for CCA.

   Keywords: cholangiocarcinoma, exosome, long non-coding RNAs, RNA
   sequencing, functional analysis

INTRODUCTION

   Cholangiocarcinoma (CCA) is an incurable and lethal cancer due to its
   silent clinical characters, difficulties in early diagnosis, and
   limited therapeutic approaches. Biopsy and cytology brush biopsy
   examination is the current gold standard for diagnosis, but it is very
   difficult to obtain the cells, and the sensitivity is only about 20%
   [[46]1]. Surgery is an effective means of treatment, but 70% of
   patients lose the opportunity to surgery because failure of early
   diagnosis [[47]2]. Therefore, new diagnosis and treatment approaches of
   CCA are clearly needed.

   Tumor-derived exosomes are emerging as a new type of cancer biomarker
   [[48]3]. Exosomes are 30-100 nm membrane vesicles secreted by a variety
   of cell types, including tumor cells, it can be generally detected in
   body fluids such as Serum, urine, saliva, bile, ascites, amniotic
   fluid, milk, etc [[49]4]. Exosomes are one type of extracellular
   vesicles (EVs) according to their size and they have been the topic of
   great interest in recent years in medical research [[50]5]. Exosomes
   are known to deliver diverse molecules to target cells ranging from
   mRNAs, non-coding RNAs (ncRNAs) to proteins, which have the diagnostic
   potential. It is reported that circulating exosomal microRNAs have
   diagnostic potential for Lung Cancer [[51]6], colon cancer [[52]7], and
   esophageal squamous cell carcinoma [[53]8]. Combination of serum
   proteins and microRNAs can increases sensitivity and specificity of
   pancreatic cancer [[54]9]. Biliary vesicle microRNA-based panel for CCA
   diagnosis demonstrate a sensitivity of 67% and specificity of 96%
   [[55]10]. So it is significant to explore and definite the potential
   functions of the cargos exosomes.

   As a subclass of ncRNAs that are present in exosomes, long-ncRNAs
   (lncRNAs), which are > 200 nucleotides (nt) in length, regulate gene
   expression at the epigenetic, transcriptional, and posttranscriptional
   levels, play important roles in diverse biological processes, including
   cell proliferation, invasion, differentiation, apoptosis, metastasis
   and immune response [[56]11]. A recent study showed that lncRNAs
   microarrays of intrahepatic cholangiocarcinoma (ICC) and paired
   adjacent noncancerous tissues reveal that lncRNAs are significantly
   changed [[57]12], however, up to date, changes of lncRNAs in exosomes
   of CCA remain unclear. In our present study, we used RNA sequencing
   (RNA-seq) to determine lncRNA repertoires of bile exosome of CCA and
   benign biliary obstruction patients. In order to explore the function
   of altered lncRNAs, we also measured the gene expression profile. The
   enriched pathways, biological process, cellular components and
   molecular functions were explored using bioinformatics. The
   relationship between the differentially expressed lncRNAs and genes was
   also analyzed by constructing the co-expression network.

RESULTS

Identification of exosomes in human bile samples

   Exosomes isolated from human bile were imaged by using Transmission
   electron microscope, TEM. We noted the presence of 30-150 nM of
   vesicles, consistent with previously reported features of exosomes
   (Figure [58]1A). Then we employed multiparameter nanoparticle tracking
   analysis (NTA), and found that the majority of EVs in human bile were
   between 30 and 150 nM and that the mode of exosome sizes was 72.2 nM
   (Figure [59]1B), suggesting that EVs isolated are most likely exosomes.
   The distribution coefficient was 0.436. To further confirm that these
   spherical structures are exosomes, we assayed for presence of CD63 and
   CD81, Flow cytometry analysis positive expression of CD63 was 96.4% and
   CD81 was 91.9%. (Figure [60]1C).

Figure 1. Identification of exosomes in human bile samples.

   [61]Figure 1
   [62]Open in a new tab

   (A) Typical transmission electron microscopy picture demonstrating the
   presence of 30-110 nm of spherical structures in human bile. Further
   characterization demonstrates that these vesicles display exosome
   characteristics. (B) Mode of exosome isolated from human bile was
   determined to be approximately 72.2 nm (x-axis depicts exosome size and
   y-axis depicts exosome intensity of each size). (C) Presence of typical
   exosome proteins (CD63 and CD81) in exosome preparations from human
   bile.

Differentially expressed lncRNAs

   A total of 64, 530 transcripts were detected in the current sequencing
   study, including 30632 transcripts identified as lncRNA. Comparing the
   mean value of Reads Per Kilobase Million (RPKM) of the CCA group with
   that of the normal group, 54 transcripts were selected from the 30632
   lncRNAs according to the criteria described in the materials and
   methods section (19 up-regulated and 35 down-regulated). The top 15
   up-regulated and 15 down-regulated lncRNAs were listed in Table [63]1.
   Their heat map and volcano plots were displayed in Figures [64]2A and
   [65]2B. Briefly, data normalized by Z score transformation were used in
   the calculation of significant changes in expression in the Control and
   CCA group, and the transformed data were used to construct the heatmap.
   The red signal referred to high relative expression and the blue signal
   referred to low relative expression.

Table 1. Different expression of lncRNAs.

   lncRNA log2^(Fold change) p-Value lncRNA log2^(Fold change) p-Value
   Top 15 up-regulated lncRNAs Top 15 down-regulated lncRNAs
   ENST00000544663.1 3.571 0.033 ENST00000571590.1 -3.054
   ENST00000571590.1
   [66]NR_002794 3.220 0.023 ENST00000548810.1 -2.316 ENST00000548810.1
   ENST00000516869.1 2.350 0.000 ENST00000584779.1 -2.272
   ENST00000584779.1
   ENST00000554988.1 2.152 0.000 ENST00000518675.1 -2.257
   ENST00000518675.1
   ENST00000607140.1 2.080 0.006 ENST00000521904.1 -2.221
   ENST00000521904.1
   ENST00000454253.1 1.911 0.031 ENST00000523279.1 -2.145
   ENST00000523279.1
   ENST00000517758.1 1.900 0.021 ENST00000511793.1 -2.086
   ENST00000511793.1
   ENST00000599110.1 1.807 0.049 ENST00000531710.1 -2.027
   ENST00000531710.1
   ENST00000580625.1 1.782 0.000 ENST00000579049.1 -2.007
   ENST00000579049.1
   ENST00000553637.1 1.782 0.000 ENST00000441146.1 -2.004
   ENST00000441146.1
   ENST00000607393.1 1.677 0.028 ENST00000452506.1 -1.970
   ENST00000452506.1
   ENST00000588480.1 1.668 0.026 ENST00000458250.1 -1.955
   ENST00000458250.1
   ENST00000450669.2 1.588 0.017 ENST00000589623.1 -1.921
   ENST00000589623.1
   ENST00000560221.1 1.482 0.042 ENST00000565668.2 -1.898
   ENST00000565668.2
   ENST00000561980.1 1.424 0.046 ENST00000579037.1 -1.804
   ENST00000579037.1
   [67]Open in a new tab

Figure 2. The heat map and volcano plots.

   [68]Figure 2
   [69]Open in a new tab

   (A) Heat maps showing unsupervised clustering of CCA and controls based
   on differentially expressed up-regulated lncRNAs (red) and long
   non-coding genes (blue). (B) Volcano plots of all the detected
   transcripts in both the CCA and biliary obstruction groups. The
   vertical dotted line delimits up-and down-regulation. Red plots:
   significant lncRNAs.

Gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway
enrichment analysis of the differentially expressed genes

   A total of 4002 terms in the GO-biological process were involved in
   current sequencing data. The top 10 biological process terms with the
   highest p-value were listed in Table [70]2, and the most significant
   process was related to “Regulation of autophagy”. The top 10 cellular
   components with the highest p-value were shown in Table [71]3, and the
   most significant process was related to “Golgi membrane”. The top 10
   molecular functions terms were displayed in Table [72]4, and the most
   significant one was “Metal ion binding”. GO enrichment for the Cluster
   analysis of the pathways was shown in Figure [73]3A.

Table 2. Top 10 GO biological processes.

   GOBPID Sample number Background number P value Term
   GO:0010506 10 71 2.085E-04 regulation of autophagy
   GO:0006044 5 16 2.207E-04 N-acetylglucosamine metabolic process
   GO:0070227 9 62 2.933E-04 lymphocyte apoptotic process
   GO:0046777 17 188 4.304E-04 protein autophosphorylation
   GO:0006914 14 141 5.827E-04 autophagy
   GO:0070232 6 31 7.679E-04 regulation of T cell apoptotic process
   GO:0016125 13 128 1.523E-03 sterol metabolic process
   GO:0070231 7 45 1.701E-03 T cell apoptotic process
   GO:0008203 12 116 1.876E-03 cholesterol metabolic process
   GO:0006807 290 6306 1.933E-03 nitrogen compound metabolic process
   [74]Open in a new tab

   “GOBPID”: the ID of biological process in GO database; “Sample number”:
   the number of candidate genes; “Background number”: the number of
   background genes in the pathway; “P value”: significance level; “Term”:
   pathway function.

Table 3. Top 10 GO cellular components.

   GOCCID Sample number Background number P value Term
   GO:0000139 39 574 0.001 Golgi membrane
   GO:0005622 557 12706 0.004 intracellular
   GO:0044424 549 12559 0.005 intracellular part
   GO:0043231 430 9789 0.009 intracellular membrane-bounded organelle
   GO:0032040 3 10 0.011 small-subunit processome
   GO:0030684 4 20 0.011 preribosome
   GO:0005730 40 689 0.012 nucleolus
   GO:0044431 40 690 0.012 Golgi apparatus part
   GO:0005737 418 9569 0.013 cytoplasm
   GO:0005905 7 60 0.013 coated pit
   [75]Open in a new tab

   “GOCCID”: the ID of cellular component in GO database.

Table 4. Top 10 GO molecular functions.

   GOMFID Sample number Background number P value Term
   GO:0046872 217 3944 1.39E-06 metal ion binding
   GO:0043169 220 4015 1.54E-06 cation binding
   GO:0043167 301 5849 3.32E-06 ion binding
   GO:0003824 260 5326 3.18E-04 catalytic activity
   GO:0016740 99 1762 4.43E-04 transferase activity
   GO:0001882 99 1802 8.74E-04 nucleoside binding
   GO:0032550 98 1788 9.9E-04 purine ribonucleoside binding
   GO:0001883 98 1791 1.039E-03 purine nucleoside binding
   GO:0032549 98 1792 1.056E-03 ribonucleoside binding
   GO:0035639 97 1780 1.233E-03 purine ribonucleoside triphosphate binding
   [76]Open in a new tab

   “GOMFID”: the ID of molecular function in GO database.

Figure 3. GO and KEGG pathway analysis of the differentially expressed genes.

   [77]Figure 3
   [78]Open in a new tab

   (A) GO enrichment for the cluster analysis of the pathways related to
   biological processes, cellular components and molecular functions.
   Horizontal axis denoted the respective -Log10 (p-value) of different
   pathways and the vertical axis represented the pathway name. Numbers of
   genes in each cluster are shown in the right black histogra. (B) KEGG
   enrichment for the Cluster analysis of the pathways related to cellular
   processes, environmental information processing, genetic information
   processing, human diseases as well as organismal systems. Horizontal
   axis denoted the respective -Log10 (p-value) of different pathways and
   the vertical axis represented the pathway name. Numbers of genes in
   each cluster are shown in the right black histogra. (C) Detailed
   network of “P53 signaling pathway”. Every node represented the key
   enzyme or transporter in this pathway. Nodes marked in red were
   associated with significantly up-regulated genes in the current
   sequencing data, and the green nodes denoted genes with no significant
   change.

   KEGG pathway analysis indicated that 240 pathways were involved in the
   current sequencing data, in which many pathways were related to
   cellular processes, environmental information processing, genetic
   information processing, human diseases as well as organismal systems
   (Figure [79]3B). The most enriched top ten pathway terms with the
   lowest p-value were p53 signaling pathway, apoptosis, lysosome,
   metabolic pathways, FOXO signaling pathway, mucin type O-Glycan
   biosynthesis, amino sugar and nucleotide sugar metabolism, endocytosis,
   sulfur metabolism, tryptophan metabolism. Further details were shown in
   Table [80]5. The most significant difference term was “p53 signaling
   pathway” (KEGG ID: hsa04115, p: 0.001), which involved 9 target genes
   and the network (Figure [81]3C).

Table 5. Top 10 KEGG pathways.

   Term ID Sample number Background number P-value
   p53 signaling pathway hsa04115 9 68 0.001
   Apoptosis hsa04210 9 86 0.005
   Lysosome hsa04142 11 122 0.006
   Metabolic pathways hsa01100 56 1181 0.018
   FoxO signaling pathway hsa04068 10 133 0.025
   Mucin type O-Glycan biosynthesis hsa00512 4 31 0.030
   Amino sugar and nucleotide sugar metabolism hsa00520 5 47 0.031
   Endocytosis hsa04144 13 203 0.034
   Sulfur metabolism hsa00920 2 10 0.061
   Tryptophan metabolism hsa00380 4 40 0.062
   [82]Open in a new tab

   “Term”: pathway function; “ID”: pathway ID; “Sample number”: the number
   of candidate genes; “Background number”: the number of background genes
   in the pathway; “P value”: significance level.

Construction of lncRNAs-proteins co-expression network

   We then evaluated the co-expression network of lncRNAs and their target
   genes based on the criterion of combined score > 0.9. In this
   co-expression network, the co-expression of lncRNA-gene and gene-gene
   was showed by lines of different colors (Figure [83]4). The lncRNA-gene
   pair with the highest positive correlation coefficient (0.993) was
   CYP51A1 and SC5D. These results indicated that one lncRNA closely
   correlated with several genes, and vice versa. The network can indicate
   functional relatedness or regulatory relationships [[84]13].

Figure 4. Construction of lncRNAs-proteins co-expression network.

   [85]Figure 4
   [86]Open in a new tab

   The blue oval represented gene, and the red rectangle represented
   lncRNA. The pink lines link gene to lncRNA, green lines link gene to
   gene.

Validation of diagnostic and prognostic value of lncRNAs in CCA

   We detected exosomes of differentially expressed lncRNAs between bile
   specimens of CCA and the control patients. Significant different
   expression lncRNAs: ENST00000588480.1 and ENST00000517758.1 were
   selected for further detection using real-time RT-PCR in bile samples
   of CCA (n=35) and the control patients (n=56) (Figure [87]5A). These
   results were higher than those shown in the expression RNA-Seq data
   sets. To confirm the performance of the two lncRNAs in diagnosing CCA,
   the receiver operating characteristic curves (ROC) were applied and the
   AUC of each curve was calculated (Figure [88]5B). The result indicates
   that the diagnosis of CCA is more efficient in the combination of the
   two lncRNAs than any single ones. Furthermore, the levels of
   ENST00000588480.1 and ENST00000517758.1 showed a significantly
   increasing trend with the advancement of cancer TNM stages, Figure
   [89]5C). The relationships of the two lncRNAs with prognosis status of
   CCA patients were further examined by Kaplan-Meier curve analysis and
   log-rank test (Figure [90]5D).

Figure 5. Validation of diagnostic and prognosis value of lncRNAs in CCA.

   [91]Figure 5
   [92]Open in a new tab

   (A) Different expression of ENST00000588480.1 and ENST00000517758.1,
   the two lncRNAs were up-regulated in CCA group (p = 0.036 and 0.037).
   (B) ROC curve of lncRNAs and CA19-9. The analysis showed the AUC of
   CA19-9 was 0.729 (95% CI, 0.620 - 0.837, it had a sensitivity and
   specificity of 74.3% and 71.4%; AUC of ENST00000588480.1 was 0.680 (95%
   CI, 0.565 - 0.796), it had a sensitivity and specificity of 62.9% and
   73.2%; When ENST00000588480.1 and ENST00000517758.1 were combined, They
   got greater sensitivity (82.9%) (AUC: 0.709; 95% CI, 0.601-0.817). (C)
   The levels of ENST00000588480.1 and ENST00000517758.1 showed a
   significant increacing trend with the advancement of cancer TNM stages.
   (p = 0.039 and 0.048). (D) Kaplan-Meier survival overall survival (OS)
   curves for ENST00000588480.1 and ENST00000517758.1, The
   ENST00000588480.1 demonstrated statistically significant median overall
   survival differences (38 vs 25 months, p = 0.046) compared to
   ENST00000517758.1 (38 vs 20 months, p = 0.043).

LncRNA signature-associated signalling pathways

   A gene KEGG pathway and Go analysis was performed according to the
   meterial and methods section. 479 target genes were concerned to form a
   gene list, while 17 of them were enriched in 3 pathways: “P53 signaling
   pathway”, “apoptosis” and ENST00000588480.1 and ENST00000517758.1. A
   chart was drown to explore the associated genes and pathways of the
   three lncRNAs for further mechanism study (Figure [93]6). The major
   biological functions closely associated were apoptosis, angiogenesis,
   invasion and metastasis etc.

Figure 6. LncRNA signature-associated signalling pathways.

   [94]Figure 6
   [95]Open in a new tab

   A schematic diagram showed the major biological functions closely
   associated to were apoptosis, angiogenesis, invasion and metastasis
   etc.

DISCUSSION

   Exosomes, endosome-derived membrane microvesicles, contain specific RNA
   transcripts that are thought to be involved in cell-cell communication
   [[96]14]. Exosomes in body fluids are relatively stable because of the
   protection of the lipid bilayer, the cargos they contain have great
   potential as disease biomarkers [[97]15, [98]16]. It was reported that
   plasma lncRNA protected by exosomes as a potential stable biomarker for
   gastriccancer [[99]17]. In urine samples, exosomal lncRNA-p21 levels
   may help to distinguish prostate cancer from benign disease [[100]18].
   Exosomal lncRNAs may also be potential biomarkers for predicting
   diseases [[101]18, [102]19]. Besides, they can activate or regulate
   cell activities such as protein expression, cell proliferation and
   differentiation or antiviral responses [[103]20]. Therefore, in the
   current study, we isolated exosomes from human bile samples for
   RNA-seq. Differently expressed lncRNAs were identified using qPCR
   method. Ultimately, three lncRNAs closely associated with CCA were
   selected and both diagnostic and prognostic values were examined.
   Combined group (ENST00000588480.1 and ENST00000517758.1) got greater
   sensitivity than carbohydrate antigen 19-9 (CA19-9) (82.9% vs 74.3%).
   We proposed that the diagnostic ability of the two lncRNAs is robust
   because of independent cohort of 35 CCA patients. To our knowledge,
   this is the first study to give a comprehensive overview of the lncRNA
   transcriptome changes in CCA and biliary obstruction of bile exosomes.
   They showed a significant increasing trend with the advancement of
   cancer TNM stages. Regarding prognosis, Kaplan-Meier curve analysis the
   higher expression group were negative correlate with the survival time.
   Therefore, lncRNAs can be used as a predictor to monitor the CCA.

   LncRNAs were detected in exosomes of body fluid, and they were enriched
   in exosomes than cells [[104]16], and of high tissue specificity
   [[105]21]. The function of diagnosis potential were sufficiently
   reported in cancers [[106]22–[107]25]. Except for that, numerous
   studies have shown that lncRNAs have the function of regulate gene
   expression at different levels including chromatin modifications,
   transcription, splicing, translation, posttranscriptional regulation,
   processing of small RNAs, etc [[108]26]. It was reported that it play
   active roles in various biological behaviors of tumor cells such as
   migration, genomic stability, immune modulation and drug resistance.
   For instance, Lung cancer exosomes initiate lncRNA changes in
   mesenchymal stem cells, lncRNAs may be involved in exosome-mediated
   crosstalk between MSCs and tumor cells [[109]27]. exosomes containing
   H19 lncRNA OF cancer stem-cell-like (CSC) liver cancer cells can
   influence their tumor microenvironment by promoting angiogenesis
   [[110]28]. Extracellular Vesicle lincRNA-VLDLR was relevant to the
   chemoresistance in hepatocellular carcinoma [[111]29]. So here, we
   further determined the potential functions mediated by the changed
   lncRNAs in CCA bile exosomes.

   GO and KEEG pathway analyses showed that many important functional
   pathways were closely related to the altered genes. Further analysis
   showed that the pathways involved were mainly related to cellular
   processes, environmental information processing as well as genetic
   information processing. These results emphasized the important role of
   micro-environmental information processing on the cellular function.
   Constant with the previous studies supported that lncRNAs in the tumor
   inflammation [[112]30] or hypoxic microenvironment [[113]31] may
   resulting in an imbalance of internal environment, which enhancing the
   progression of cancer progression. Although part of the variability in
   lncRNA repertoire size may be biologically meaningful, much is likely
   to be explained by unequal sequencing depth and by variable genome
   sequence and assembly quality.

   Because the two lncRNAs were differently expressed not only between CCA
   and contol bile samples, but also between well- and poor prognosis
   patients, we speculated that these lncRNAs may play critical roles in
   both tumourigenesis and the progression of CCA, and that both of the
   two lncRNAs may be potential therapeutic targets in CCA. To
   preliminarily reveal the potential functions of the lncRNAs in CCA, the
   mRNAs co-expressed with each of the three lncRNAs were used to conduct
   KEGG and GO analysis in KOBAS, the website is
   [114]http://kobas.cbi.pku.edu.cn/index.php. ENST00000588480.1 enriches
   the Hedgehog signaling pathway, and may have the function of ‘lipid
   biosynthetic process’, ‘Wnt signaling pathway’, “binding of
   glycoprotein, ribosomal protein and phosphoprotein”, “peptide kinase
   and phosphotransferase activity”. ENST00000517758.1 enriches “p53
   signaling pathway”, “Pathways in cancer” and “Endocytosis” etc. and may
   have the function of “Metal ion binding”, “regulation of leukocyte and
   T cell apoptotic process”, “cholesterol metabolism” and “transmembrane
   transporter activity”. These informations may improve the current
   understanding of the close association between the two lncRNAs and CCA,
   but the underlying mechanism has not been deciphered. Additional
   prospective and multicentre studies with a considerable pool of
   patients are needed to determine whether this signature is applicable
   for CCA.

MATERIALS AND METHODS

Patient samples and bile exosome isolation

   Bile samples of CCA (n = 35, [115]Supplementary Table 1) and biliary
   obstruction patients (n = 56) were obtained by endoscopic retrograde
   cholangiopancreatography (ERCP) or surgery from the second affiliated
   hospital of Nanjing Medical University, aspiration of bile was
   performed after cannulation of the biliary tree before injection of
   contrast. All work presented here was approved by second affiliated
   hospital of Nanjing Medical University Review Board. All patients
   provided written informed consent. We prepared about 4ml of bile
   samples for use, They were centrifuged at 500g for 10 minutes at 4°C to
   pellet cells and debris. The supernatant was then centrifuged at 16500g
   for 20 minutes at 4°C to further remove cellular debris and then
   filtered through a 200-nm filter. Then Pipet the filtered supernatant
   to ultracentrifuge tubes and add PBS so that the tubes are more than
   2/3 full, ultracentrifugate at 120000 g for 70 min at 4°C, then we can
   get small yellow pellets at the bottom of the ultracentrifuge tube.
   Exosomes were utilized for immediate RNA sequencing, RNA extraction or
   resuspended in phosphate buffered saline and stored in -80°C.

Functional assays

   Please see the [116]SUPPLEMENTARY MATERIALS AND METHODS section.

Statistical analysis

   All the biological specimens were analysed retrospectively. Data were
   presented as the min to max. Statistical analyses were performed using
   Graph pad 6.0 and/or IBM SPSS Statistics 17.0 software, differences
   were considered statistically significant when the p < 0.05. The
   difference between CCA and control group was analyzed using a
   Mann-Whitney test. Then CCA patients were divided into high and low
   groups according to the median of the lncRNA expression, survival
   curves were estimated using the Kaplan-Meier method, and differences in
   survival distributions were evaluated by the log-rank test.

SUPPLEMENTARY MATERIALS FIGURES AND TABLES

   [117]oncotarget-08-69995-s001.pdf^ (1.6MB, pdf)

Acknowledgments