Abstract To verify the expression profile of long non-coding RNAs (lncRNAs) and mRNAs in cervical cancer, identify their clinical significance in HPV16-associated cervical cancer, and annotate the biological function of mRNAs. Three pairs of cancerous and paracancer tissues were selected in cervical squamous cell carcinoma (IB2 stage), high-throughput sequencing was utilized to determine the expression levels of lncRNAs and mRNAs. The detection results were validated by GEPIA database analysis and RT-qPCR. Functional annotations of differential mRNAs were conducted through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network states. Furthermore, the association between antisense lncRNA and mRNA in cervical cancer was analyzed to predict the biological functions of lncRNA. Finally, recombinant lentivirus CV224-HPV16 E6/E7 was transfected into HcerEpic to establish a stable cell line with overexpressed HPV16 E6/E7, then differential lncRNAs were detected by RT-qPCR. Compared to paracancerous tissues, there were 3,608 lncRNAs significantly upregulated and 4,383 lncRNAs significantly downregulated in cervical cancer tissues (Fold change >2 and P < 0.05). Additionally, 3,666 mRNAs were significantly upregulated, while 2,220 mRNAs were significantly downregulated (Fold change >2 and P < 0.05). GO/KEGG enrichment analysis showed that differentially expressed mRNA played a significant role in cell cycle and cell senescence, and was related to signal pathways such as cAMP and MAPK, forming a complex network among the proteins encoded by these mRNAs. Further analysis indicated that the 20 antisense lncRNAs with the most remarkable differences might exert biological functions by influencing their corresponding mRNAs. The results of RT-qPCR revealed that CDKN2B-AS1, HAGLROS and GATA6-AS1 were potentially regulated by HPV16 E6/E7, which were in accordance with those obtained from chip detection. In this study, differentially expressed lncRNAs associated with HPV16 infection were screened and explored their transcriptional molecular functions and biological pathways, providing a molecular basis for predicting diagnostic markers of cervical cancer. Keywords: cervical cancer, lncRNA, mRNA, HPV16 E6/E7, high-throughput sequencing 1 Introduction Cervical cancer is the fourth most common malignant tumor in women worldwide in terms of both incidence and fatality ([46]Sung et al., 2021). Globally, there were about 604,127 new cervical cancer diagnoses in 2020, and 341,831 fatalities ([47]Sung et al., 2021), which seriously threatened women’s health. Currently, cervical cancer can be treated through diverse approachcng surgery ([48]Greggi et al., 2020), chemotherapy ([49]Crafton and Salani, 2016), radiotherapy ([50]Tan et al., 2019), and immunotherapy ([51]Ferrall et al., 2021; [52]Feng et al., 2020). However, the 5-year survival rate was only 59.8% ([53]Zeng et al., 2018), signifying a substantial challenge in boosting the survival rates of individuals diagnosed with cervical cancer. Cervical cancer development is closely associated with ongoing infection with high-risk human papillomaviruses (HPV). Studies have shown that the most predominant oncogenic HPV types are 16 (57%) and 18 (16%) ([54]Crosbie et al., 2013). The E6 and E7 proteins of these high-risk HPV types possess oncogenic characteristics. However, not all patients infected with HPV will eventually develop cervical cancer, as the progression of the disease is regulated by numerous factors. The specific mechanisms of these factors are still not fully understood ([55]Okunade, 2020). Therefore, further research on the pathogenesis of cervical cancer, together with the discovery of novel treatment targets and sensitive and specific diagnostic markers, has significant clinical value for improving the efficiency of cervical cancer diagnosis and treatment. Long noncoding RNAs (lncRNAs) are transcripts that are more than 200 nucleotides long ([56]Chen et al., 2022). They have the ability to control how genes that code for proteins are expressed by exerting an influence on the transcription, post-transcriptional and translation modifications of mRNAs ([57]Quinn and Chang, 2016; [58]Choi et al., 2019), thereby leading to tumorigenesis and progression ([59]Bai et al., 2023; [60]Wu et al., 2022). Depending on their position in relation to protein-coding genes, lncRNA can be classified as sense, antisense, intron, intergenic, and bidirectional ([61]St Laurent et al., 2015). Antisense lncRNAs constitute a relatively well-studied category. More than 30% of human annotated transcripts have corresponding antisense lncRNA, which regulates the corresponding justice mRNA through various mechanisms ([62]Faghihi and Wahlestedt, 2009). In order to examine the impact of differential lncRNA on the tissues of cervical cancer, this study selected cancer tissues and adjacent non-cancerous tissues from three pairs of HPV16-positive cervical squamous carcinoma patients, all clinically diagnosed at stage IB2. Arraystar LncRNA and mRNA expression profiling microarrays were utilized via high-throughput sequencing technology. Bioinformatics analyses were carried out to identify lncRNAs and mRNAs, and to predict relevant downstream regulatory signaling pathways and key genes. For mRNAs, GO functional analysis, KEGG pathway enrichment analysis, and PPI assessments were used. At the cellular level, the characteristics of these lncRNAs, the signaling pathways they participate in, and their relationship to HPV16 infection were all examined using RT-qPCR validation. This study provides theoretical support for bioinformatics in outcome prediction and contributes to the diagnosis of cervical cancer. 2 Materials and methods 2.1 Materials 2.1.1 Tissue specimens and cell lines Cancer tissues and adjacent paracancerous tissues from three patients diagnosed with HPV16-positive cervical squamous carcinoma at stage IB2, from June 2021 to February 2022, were selected from the First People’s Hospital of Zunyi City. Notably, none of the patients had received preoperative radiotherapy or chemotherapy. Subsequently, the samples were sent to Shanghai Kangcheng Bioengineering Co., Ltd. for labeling and High-throughput sequencing. The hospital’s ethics committee approved this study, and each patient signed an informed consent form. The Chinese Academy of Sciences’ Cell Bank provided the human normal cervical epithelial cells (HcerEpic) and cervical cancer cell lines (HeLa, SiHa, and C33A) used in this investigation. 2.1.2 Main reagents Beijing Solabao Technology Co. supplied the 2×SYBR Green PCR mix kit, while Gibco, USA, supplied the trypsin and DMEM high glucose medium. We purchased fetal bovine serum (FBS) from Israel Biotech; Trizol reagent was acquired from Invitrogen, USA. Chloroform was obtained from Chengdu Kelong Chemical Reagent Factory, China; Purchase PrimeScript™ RT Reagent Kit from TaKaRa, Japan; PCR primers were synthesized by Bioengineering (Shanghai) Co. Recombinant lentivirus CV224-HPV16 E6/E7 was constructed and synthesized by Shanghai Jikai Gene Science Co. The selection, probe design, image acquisition, and data analysis of the Arraystar Human LncRNA Microarray V5.0 were conducted by Shanghai Kangcheng Bioengineering Co., Ltd. 2.2 Methods 2.2.1 Total RNA extraction and quality control Total RNA was extracted from cervical cancer tissues and paracancerous tissues of three cases in accordance with the instructions accompanying the Trizol reagent. The paracancerous tissues served as the control group, whereas the cancer tissues formed the experimental group. Subsequently, The RNeasy Mini Kit (Qiagen) was then used to purify the RNA samples. The Agilent 2100 Bioanalyzer or conventional denatured gel electrophoresis were used to evaluate the integrity of the RNA. 2.2.2 Microarray hybridization analysis The Human LncRNA Microarray V5.0 has been carefully engineered to identify a wide range of protein-coding transcripts and 39,317 lncRNAs. The lncRNAs encompassed in this microarray have been judiciously selected from several authoritative public transcriptome databases, including FANTOM5 CAT (v1), GENECODE (v29), RefSeq, and NONCODE (v5). During the hybridization process, a random priming method was employed. Following purification with the RNeasy Mini Kit (Qiagen), the labeled cRNAs were assessed for activity and concentration using a NanoDrop ND-1000. Thereafter, microarray hybridization was performed, followed by washing, fixing, and scanning on a hybridization chip using the Agilent DNA Microarray Scanner (part number G2505C). 2.2.3 Construction of differential expression profiles of lncRNA and mRNA We used Agilent Feature Extraction software (v11.0.1.1) in this investigation to acquire mRNA and lncRNA differential expression profiles. Both raw data and microarray plots were produced using this software. Next, the raw data was processed and quantile normalized using GeneSpring GX v12.1 software (Agilent Technologies). After screening for high-quality probes, further analysis was conducted. The differential lncRNAs or mRNAs between the two sample groups was determined by screening for differential expression profiles with a significance threshold of P < 0.05 and a fold change >2.0. 2.2.4 GO and KEGG enrichment analysis of differentially expressed mRNAs By significantly enriching differential mRNAs based on GO terms, the analysis encompassed the Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) categories to elucidate the primary biological functions of differential mRNAs and their roles in various biological processes. A smaller P value (P < 0.05) indicates a more significant GO term. Additionally, KEGG analysis was conducted to predict the signaling pathways in which differentially expressed lncRNAs may be involved. A smaller P value (P < 0.05) also signifies a more significant KEGG term. 2.2.5 Analysis of interactions between mRNA-encoded proteins Select the top 200 mRNA datasets with the most significant differences and import them into the STRING database ([63]https://cn.string-db.org). In the display options, select ‘Hide isolated nodes in network’ and then construct a network diagram to illustrate the interactions among proteins encoded by mRNA. 2.2.6 GEPIA and KM-Plotter database analysis The expression of predicted lncRNAs in cervical cancer was analyzed via the GEPIA database ([64]http://gepia2021.cancerpku.cn) and the KM-Plotter database ([65]https://www.kmplot.com/analysis/) to evaluate the prognostic implications of lncRNAs in cervical cancer patients. 2.2.7 RT-qPCR RNA was extracted from cells and tissues by Trizol lysis. Subsequently, reverse transcription of cDNA was conducted. A 20 µL reaction system was prepared by using a 2× SYBR Green PCR Mix kit. Forward and reverse primers were configured according to the manufacturer’s instructions. After pre-denaturation at 95°C for 5 min (1 cycle), denaturation at 95°C for 10 s, annealing at 60°C for 30 s, extension at 72°C for 30 s (40 cycles), and a final hold at 72°C for 5 min, the reaction program was stored at 4°C for amplification. Using GAPDH as an internal reference, the 2^−ΔΔCT technique was used to examine the target genes. In [66]Table 1, the gene primer sequences are displayed. TABLE 1. Primer used for RT-qPCR. Gene symbol Sequence Fragment size (bp) CDKN2B-AS1 forward 5′-ATTTTATTCCTGGCTCCCCTCGTC-3′ reverse 5′-TGGCGGATAGAGCAATGAGATGAC-3′ 110 DICER1-AS1 forward 5′- CGCCCTTCACTGCCTCTCTTC-3′ reverse 5′- TGCTCTGGCTGTGTCATCCTTAG-3′ 122 HAGLROS forward 5′- TGTCACCCTTAAATACCGCTCT-3′ reverse 5′- CTTCCTCCCACACAAATACTCC-3′ 153 GATA6-AS1 forward 5′- TTCTGGGAGTCGCGCATT-3′ reverse 5′- GTGGCCGCATTTGGAAAA -3′ 121 MIR205HG forward 5′- GTGCTTTATATAGGAAAGGACCAAC-3′ reverse 5′- CCATGCCTCCTGAACTTCACT -3′ 108 GAPDH forward 5′- GGAGCGAGATCCCTCCAAAAT-3′ reverse 5′- GGCTGTTGTCATACTTCTCATGG -3′ 193 [67]Open in a new tab 2.2.8 Cell transfection HcerEpic cells were diluted to 5 × 10^4 cells/mL of cell suspension, and then incubated in a 37°C incubator for 24 h. Once the cell density reached 70%, the lentivirus CV224-HPV16 E6/E7 along with co-transfection reagents constructed and packaged by Shanghai GeneChem Co., Ltd. were used for HcerEpic. The medium was swapped out for new complete media 24 h following transfection. After 48–72 h, the cells were examined for infection using an inverted fluorescence microscope, and stable transfected cell strains were selected using 2 μg/mL puromycin for subsequent experiments. 2.2.9 Statistical analysis Software such as SPSS 29.0 and GraphPad Prism 6 were used to statistically evaluate the data outputs. Mean ± standard deviation (‾x ± s) was used to convey measurement data that fit a normal distribution, while n (%) was used to represent count data. A paired t-test was utilized for intra-group comparisons, while an independent samples t-test was utilized for comparisons between the two groups. Non-parametric tests were utilized for data that did not fit a normal distribution; for between-group comparisons of count data, the χ ^2 test or Fisher’s exact test was employed. The threshold for statistical significance was set at P < 0.05. 3 Results 3.1 LncRNA and mRNA differential expression analysis Box plots comparing the experimental group with the control group showed that the dispersion of lncRNAs and mRNAs data among the six groups was largely consistent ([68]Figure 1A; [69]Supplementary Figure S1A). A total of 3608 of the 7991 lncRNAs that were found to be differentially expressed in the experimental group as opposed to the control group were upregulated, whereas 4,383 were downregulated. Furthermore, the experimental group had 5,886 differentially expressed mRNAs, of which 3,666 were upregulated and 2,220 were downregulated. Volcano plots were generated for the differentially expressed lncRNAs, effectively depicting their distribution, fold change in expression, and significance of the results. In [70]Figure 1B; [71]Supplementary Figure S1B, red dots represent differentially upregulated lncRNAs and mRNAs, green dots indicate differentially downregulated lncRNAs and mRNAs, and gray dots signify lncRNAs and mRNAs with no significant differential expression. The 20 lncRNAs and mRNAs with the most substantial differences in both upregulation and downregulation are detailed in [72]Table 2 and [73]Supplementary Table S1. Furthermore, the differentially expressed lncRNAs and mRNAs were distributed across chromosomes 1–22, as well as the X and Y chromosomes ([74]Figure 1C; [75]Supplementary Figure S1C). In addition, we also analyzed the 10 mRNA that were differentially upregulated and the 10 mRNA that were downregulated, as shown in [76]Table 3. FIGURE 1. [77]Panel A shows a box plot of normalized intensity values for samples C1, C2, C3, N1, N2, and N3. Panel B features a volcano plot with red, green, and black dots indicating up-regulated, down-regulated, and non-differentially expressed genes, respectively. Panel C presents a bar chart comparing up-regulated (red) and down-regulated (green) long non-coding RNAs (lncRNAs) across chromosomes. [78]Open in a new tab Differentially expressed lncRNAs in cervical cancer patients. (A) Box plot of the dispersion of the six sets of data for lncRNAs. (B) Volcano plot of lncRNAs with differential expression, with 2-fold upregulated lncRNAs in red and 2-fold downregulated lncRNAs in green. (C) Distribution of lncRNAs in chromosomes. TABLE 2. Most substantial differencially expressed lncRNAs in cervical cancer patients. Upregulated lncRNAs Downregulated lncRNAs lncRNA Fold-change lncRNA Fold-change CATG00000027321.1 636.9061239 G047911 6937.990902 G089593 292.4648972 HOXB-AS4 2275.910853 [79]AL049555.1 287.5567314 CATG00000038938.1 2072.279809 CATG00000092533.1 159.8965499 [80]AC099560.1 1018.316089 MIR205HG 146.3361637 XLOC_013557 976.8543315 LOC100130899 115.1445194 LINC00470 632.3659016 [81]AC024587.2 99.5060611 [82]AL139260.1 543.8341357 LINC02560 84.4043491 [83]AC007383.2 530.9284105 CDKN2B-AS1 70.7083456 ELMO1 445.1438046 LINC01956 68.5309097 [84]AC104964.3 428.5326448 EPHA1 62.1165931 G064089 425.7516074 AC074050.4 61.8415139 CATG00000022496.1 421.5776868 LINC00511 61.1640486 [85]AC074389.2 413.4464294 [86]AL021807.1 58.4494212 GATA6-AS1 399.5837675 [87]AC007848.1 57.0298711 RASAL2-AS1 394.4119172 [88]AL512413.1 54.9253505 LINC00958 275.103217 [89]AC007996.1 54.0569759 [90]AC079779.3 235.835594 KNL1 50.5805458 FOXD2-AS1 234.5802375 [91]AL445524.1 50.0969002 [92]AC105760.2 189.5778892 HAGLROS 48.7441743 DICER1-AS1 180.2364925 [93]Open in a new tab TABLE 3. Co-differential expression profiles for lncRNA and mRNA. lncRNA mRNA Protein naming Fold change -lncRNA Fold change - mRNA Expression regulation-mRNA ZNRF3-AS1 ZNRF3 Zinc and ring finger 3 2303.751676 3.0187431 Up G076835 TMEM243 Transmembrane protein 243 1220.575566 4.4602035 Down [94]AL139260.1 MYCBP MYC binding protein 543.8341357 4.5640623 Down [95]AL357835.1 TNFRSF8 TNF receptor superfamily member 8 388.2729254 2.0199254 Down [96]AC105760.2 COPS8 COP9 signalosome subunit 8 189.5778892 2.0967095 Up DICER1-AS1 DICER1 Dicer 1, ribonuclease III 180.2364925 3.8189894 Up TSPOAP1-AS1 SUPT4H1 SPT4 homolog, DSIF elongation factor subunit 172.3521916 2.6175141 Up CATG00000078061.1 PMF1-BGLAP PMF1-BGLAP readthrough 123.6459921 2.2795893 Up ADAMTS9-AS1 ADAMTS9 ADAM metallopeptidase with thrombospondin type 1 motif 9 120.0295931 5.0983664 Down [97]AC092614.1 MYO1B Myosin IB 88.2336666 2.3434914 Up LOC613266 MACROD2 MACRO domain containing 2 81.3197909 3.1545941 Up CDKN2B-AS1 CDKN2B Cyclin dependent kinase inhibitor 2B 70.7083456 102.4963838 Up [98]AC018529.2 MBP Myelin basic protein 52.3701186 2.0205056 Up RNF219-AS1 RNF219 Ring finger protein 219 52.0876677 8.4740315 Up [99]AL356134.1 SLC28A3 solute carrier family 28 member 3 46.4002541 182.7079416 Up PGM5-AS1 PGM5 phosphoglucomutase 5 45.7619376 32.1046322 Down AC120498.10 TPSG1 Tryptase gamma 1 45.6495215 10.7907811 Down SRD5A3-AS1 SRD5A3 Steroid 5 alpha-reductase 3 40.8791889 4.0924185 up FLJ13224 SINHCAF SIN3-HDAC complex associated factor 40.449987 8.1963633 up [100]AC006449.3 MLLT6 MLLT6, PHD finger containing 39.1806764 3.6028635 up [101]Open in a new tab 3.2 GO/KEGG pathway enrichment analysis of differential mRNAs Differentially expressed mRNAs were subjected to GO analysis. The top 10 GO terms with the greatest enrichment were displayed for each GO categorization after upregulated and downregulated differential mRNAs were chosen independently. The results revealed that significantly upregulated mRNAs enriched for BP were mainly involved in cellular molecular metabolic processes (GO:0044260), the cell cycle (GO:0007049), and organelle organization (GO:0006996). Enriched Cellular Components (CC) were predominantly located in the cytoplasm, linked to organelles and lumen, and involved in growth and developmental processes. Additionally, the MF observed included catalytically active nucleic acid binding, nucleotide binding, and protein binding, as illustrated in [102]Figure 2A. On the contrary, significantly downregulated mRNA-enriched BP primarily related to multicellular biological processes (GO:0032501) and might possess bioadhesive functions (GO:0022610). The CC included myofibrils, the extracellular matrix, and the plasma membrane, while the MF encompassed protein binding and metal ion binding, among others, as shown in [103]Figure 2B. FIGURE 2. [104]Bar charts labeled A and B display significant Gene Ontology (GO) terms of differentially expressed (DE) genes. The x-axes show GO identifiers and descriptions, while the y-axes represent enrichment scores (log10 p-values). Bars are color-coded: blue for molecular function, green for cellular component, and red for biological process. Chart A ranges up to 60, and chart B up to 20 on the y-axis, indicating varying enrichment levels. [105]Open in a new tab GO enrichment analysis of differentially expressed mRNAs in cervical cancer patients. (A) GO function annotation plot for expression of upregulated mRNAs, the horizontal axis indicates GO enrichment entries, the vertical axis indicates the number of genes, and a larger enrichment score indicates a greater degree of enrichment. (B) GO function annotation plot for expression of downregulated mRNAs. According to the enrichment analysis results of KEGG pathway, enrichment was evaluated through GO ID enrichment scores, P-values, and the quantity of mRNA target genes associated with every route. Subsequently, the top 10 KEGG pathways with the greatest enrichment were chosen to be shown. The findings showed that the significantly upregulated mRNAs were primarily engaged in the cell cycle’s control, spliceosome function, and cellular senescence, with associations to diseases such as Alzheimer’s disease, the Fanconi anemia pathway, and Parkinson’s syndrome ([106]Figure 3A). Conversely, the significantly downregulated mRNAs were mainly linked to the secretion of substances such as insulin and bile, and were associated with signaling pathways including cAMP and MAPK, which may regulate processes such as neuroactive ligand-receptor interactions ([107]Figure 3B). FIGURE 3. [108]Two bubble plots labeled A and B showing significant pathways of differentially expressed genes. Both graphs plot enrichment score against various biological pathways. Bubble size indicates selection counts, and color represents p-value. Panel A includes pathways like spliceosome and Alzheimer disease; Panel B includes pathways like dilated cardiomyopathy and cAMP signaling. Color gradient ranges from blue (higher p-value) to red (lower p-value), with larger bubbles indicating higher selection counts. [109]Open in a new tab KEGG enrichment analysis of differentially expressed mRNA in cervical cancer patients. (A) Analysis of enrichment Bubble plot of KEGG signaling pathway expressing upregulated mRNAs: the size of the bubble indicates the number of differentially expressed genes enriched in this pathway, the colors of the bubbles represent the various P-values, and the horizontal axis of the plot shows the enrichment score and the vertical axis the pathway name. The larger the enrichment fraction, the higher the enrichment degree. (B) Enrichment analysis bubble plot of KEGG signaling pathway expressing downregulated mRNAs. 3.3 Analysis of interactions between proteins encoded by differential mRNAs To use the STRING database to analyze protein interactions, the 100 most significantly upregulated and 100 most significantly downregulated mRNAs were chosen. The results showed that the proteins with the strongest interactions included CTNNB1, ATXN2, PRKCD, FCGR3B, UQCRH, RPS28, and KIF15 ([110]Figure 4). CTNNB1 encodes the β-catenin protein, which is crucial to cell adhesion and signaling ([111]Costigan et al., 2020). An RNA-binding protein called ATXN2 controls the formation of stress granules and has been linked to the etiology of a number of neurodegenerative illnesses ([112]Akçimen et al., 2021). The protein encoded by PRKCD ([113]Liu et al., 2020) is involved in a number of biological functions, including the negative regulation of the insulin receptor signaling cascade and the assembly of cellular components. This analysis highlights the biological significance of mRNAs in cervical cancer tissues compared to paracancerous tissues, providing theoretical support for subsequent experiments aimed at studying these targets. FIGURE 4. [114]Network diagram showing interconnected nodes labeled with protein codes. Nodes are color-coded: purple, green, blue, yellow, red, and pink. Central node labeled "CTNNB1" connects to various proteins, illustrating a complex protein interaction network. [115]Open in a new tab Interaction analysis between proteins encoded by differentially expressed mRNAs. 3.4 Co-differential expression profiles and lncRNA function prediction for LncRNA and mRNA in cervical cancer patients In studies on lncRNAs, antisense lncRNAs regulate their corresponding mRNAs through multiple mechanisms to get biological functions. And we analyzed the 20 antisense lncRNAs with the most significant differential expression and performed a co-expression analysis with the differentially expressed mRNAs to infer the functions of these lncRNAs ([116]Table 3). 3.5 Validation of differentially expressed lncRNAs 3.5.1 GEPIA predicts lncRNA expression in cervical cancer Based on the above analysis and literature reports, lncRNAs with significant differences were selected for verification. As shown in [117]Table 4, lncRNAs such as CDKN2B-AS1 ([118]Gui and Cao, 2020; [119]Zhang et al., 2018), MIR205HG ([120]Dong et al., 2019; [121]Li et al., 2019; [122]Yin et al., 2022), HAGLROS ([123]Li et al., 2021; [124]Chen et al., 2018a), GATA6-AS1 ([125]Gong et al., 2020; [126]Wang et al., 2020) and DICER1-AS1 ([127]Li et al., 2023) are related to cancer research. Therefore, these lncRNAs were selected for verification. The expression levels of these five lncRNAs in 306 cervical cancer tissues were compared with those in 13 normal tissues utilizing the GEPIA database. The results showed that CDKN2B-AS1, MIR205HG, and HAGLROS were highly expressed in cervical cancer ([128]Figures 5A–C), whereas GATA6-AS1 and DICER1-AS1 showed low expression levels in cervical cancer ([129]Figures 5D,E). Notably, the expression of DICER1-AS1 was not statistically significant ([130]Figure 5E). TABLE 4. Research target of differentially expressed lncRNAs in various diseases. LncRNA Up or down Research target Disease References