# Novel Prognostic Biomarkers for Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (CESC) Patients via Analysis of Competing Endogenous RNA (ceRNA) Network

Authors: Yanru Dong; Weibo Wen; Tiezheng Yuan; Lan Liu; Xiangdan Li
Journal: Disease Markers
 (2023)

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## Abstract

Background. Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common malignant gynecological cancer. The ceRNA networks play important roles in many tumors, while RILPL2-related ceRNA network has been seldom studied in CESC. Methods. All CESC data was obtained from TCGA database. Differentially expressed RNAs and predicted target RNAs were cross analyzed to construct ceRNA network. RNA and clinicopathological characteristics’ influence on overall survival (OS) were determined by univariate and multivariate Cox regression analyses. Lasso regression was used to construct the prediction model. Coexpression analysis was performed to explore the association of gene expression with CESC. This was followed by an experimental validation based on these results. Results. Between high and low RILPL2 expression CESC patients, totally 1227 DEmRNAs, 39 DEmiRNAs, and 1544 DElncRNAs were identified. After multiple cross analyses, 1 miRNA hsa-miR-1293, 20 mRNAs, and 43 lncRNAs were maintained to construct ceRNA network. CADM3-AS1, LINC00092, and ZNF667-AS1 in ceRNA network were significantly associated with the OS of CESC patients, and patients with low expression of these lncRNAs had worse prognosis. Significant lower expressions of these lncRNAs were also observed in CESC cell line compared with normal cell line. Conclusion. Low expressions of CADM3-AS1, LINC00092, and ZNF667-AS1 in ceRNA network were probably promising poor prognostic biomarkers for CESC patients. The genes show a prospective research area for CESC-targeted treatment in the future.


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## Body

## 1. Background

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), as a kind of prevalent gynecological cancer [1], has emerged as the second-highest cause of female cancer-related deaths behind breast cancer, particularly in some developing countries [2]. Currently, the gold standard of treatment for CESC patients at early stage is still surgery, while those patients at moderate and advanced stages will be treated with concurrent chemoradiotherapy [3]. Despite the overall survival (OS) of CESC patients diagnosed at an early stage is generally acceptable, the prognosis of advanced CESC patients is quite undesirable, with lower than 50% 5-year survival rate [4]. Once the tumor invasion, metastasis, or recurrence happened, the prognosis of CESC patients would be much worse, with less than 20% 5-year OS [5]. Moreover, the CESC patients have been more younger [6], and the morbidity and recurrence rate is growing gradually in the past decades [7], implying an imperative need for more effective diagnostic and prognostic approaches of CESC. Recently, the application of some biomarkers, human papillomavirus (HPV) vaccines, and immune therapy has achieved encouraging results [3, 8]. However, the differential OS of CESC patients still could not be avoided. For example, the International Federation of Gynecology and Obstetrics (IFGO) stage system has been considered as a crucial prognostic factor for CESC, but significantly differential OS occurs in the same FIGO staging [9]. All the above evidence indicates that more novel diagnostic and prognostic biomarkers are still urgently needed to improve risk stratification, target treatment strategies, and prognosis of CES patients.Given that CESC is usually affected by complex factors, such as genomics and molecular, biological, and metabolic processes, various RNAs are the crucial members involving many biological processes in CESC [10]. Among which, 98% of the transcriptome is consist of noncoding RNAs (ncRNAs) [11]. The role of many ncRNAs has been revealed with the development of high-throughput sequencing, involving tumor progression, cell proliferation, metastasis, and so on [12]. As short ncRNAs, microRNAs (miRNAs) could regulate the gene expression via catalyzing mRNA cleavage or by inhibiting mRNA translation [13]. Besides, as a kind of competing endogenous RNAs (ceRNAs), long noncoding RNAs (lncRNAs) and miRNAs usually regulate each other through some certain biding sites [11]. All these RNAs, mRNA, miRNAs, and lncRNAs show significant potential as diagnostic or prognostic biomarkers in various cancers [14]. For instance, lncRNA CCAT2 has been suggested to be correlated with the prognosis of CESC patients, serving as a prognostic biomarker [15]. Zhou et al. recently identified a prognostic predictive ceRNA network comprising 9 lncRNAs-8 miRNAs-10 mRNAs in squamous cell carcinoma of the tongue [16]. The ceRNA hypothesis has demonstrated the competitive activity of lncRNAs for certain binding sites of target miRNAs, thereby regulating the mRNA expression indirectly [17].Available bioinformatic data confirm that most immune-related lncRNAs in the human genome contain miRNA recognition sites and that lncRNAs can interfere with immune cells, thereby affecting the NF-κB signaling pathway and mediating the immune and inflammatory response processes. In the past few years, the ceRNA regulatory theory of lncRNA-miRNA-mRNA has been demonstrated in the study of various diseases, such as malignancies, cardiovascular diseases, and autoimmune diseases, where the interaction between lncRNA, miRNA, and mRNA regulates the expression of target genes. Any abnormal expression of RNA in this network can lead to the development and progression of certain diseases. Currently, transcript and microarray analysis is widely used in various diseases, including various tumors and immune diseases, and the data can be analyzed to explore new biomarkers, which provide a reference for improving diagnosis and treatment. In addition, ceRNA networks can elucidate novel mechanisms that promote disease development in transcriptional regulatory networks. The combination of microarray and bioinformatic analysis allows the exploration of potential key gene and pathway networks that are closely associated with disease development. Our group has previously investigated the crucial role of RILPL2 (Rab interacting lysosomal protein like 2) in the development and prognosis of CESC patients. Some studies have explored the possible role of certain lncRNA- or miRNA-related ceRNA networks in cervical cancer [18]. However, as far as we know, few studies have focused on the RILPL2-related ceRNA network in CESC.Accordingly, based on our previous findings on the important role of RILPL2 in the progression and prognosis of CESC, we herein aim to further study the ceRNA network related to RILPL2 expression and explore more potential prognostic biomarkers for CESC patients. Our research would give more insights into improving the prognosis of CESC patients indirectly.


## 2. Methods

### 2.1. Research Objects

All CESC-related data here was downloaded from The Cancer Genome Atlas (TCGA) (https://tcga-data.nci.nih.gov/tcga/) database, searching with keyword “TCGA-CESC”. Totally, 302 CESC patients’ data was downloaded, of which 306 CESC tumor samples’ mRNA-seq data and corresponding lncRNA data was obtained. Moreover, the survival information of 291 CESC patients was complete (Table 1), which was used for subsequent survival analysis. Additionally, the miRNA-seq data of 312 CESC samples (including 3 adjacent samples) were also downloaded.Table 1
Clinicopathological characteristics of TCGA patients with cervical cancer.
CharacteristicsGroupsPatientsAgeMedian46Range20-88TNM stageI159 (54.64%)II64 (21.99%)III41 (14.09%)IV21 (7.22%)Unknown6 (2.06%)GradeG118 (6.19%)G2129 (44.33%)G3116 (39.86%)GX24 (8.25%)Unknown4 (1.37%)Smoking historyNonsmoker141 (48.45%)Reformed smoker51 (17.53%)Smoker63 (21.65%)Unknown36 (12.37%)RaceAmerican Indian or Alaska native7 (2.41%)Asian19 (6.53%)Black or African American30 (10.31%)Native Hawaiian or other Pacific Islander2 (0.69%)White204 (70.10%)Unknown29 (9.97%)StatusAlive219 (75.26%)Dead72 (24.74%)


### 2.2. Differentially Expressed Analysis

According to the median expression of RILPL2, all CESC samples were divided into two groups, high and low RILPL2 expression CESC samples, which were then subjected to the differentially expressed analysis (limma of R [19]). The differentially expressed mRNA (DEmRNA) was screened based on false discovery rate FDR<0.05 and logfoldchangeFC>1(FDR was calculated according to the Benjamini and Hochberg method). The screening criteria of differentially expressed miRNA (DEmiRNA) and lncRNA (DElncRNA) were FDR<0.05 and logFC>0.5.


### 2.3. Construction of ceRNA Regulation Network

The ceRNA hypothesis has demonstrated the competitive activity of some lncRNAs for some binding sites of target miRNAs, thereby regulating the mRNA expression indirectly [17, 20, 21]. Based on this hypothesis, the ceRNA regulation network was then constructed following the below steps. (1) The target miRNAs of DElncRNAs and the lncRNA-miRNA interaction pairs were predicted using LncBase v2 (http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=lncbasev2%2Findex) [22]. (2) The cross analysis was conducted on the DEmiRNAs and target miRNAs of DElncRNAs using VennDiagram of R (https://CRAN.R-project.org/package=VennDiagram). Then, the target genes of the overlapped miRNAs were predicted utilizing miRDB (http://www.mirdb.org/) [23] and TargetScan (version 7.2) (http://www.targetscan.org/vert_72/) [24]. Only those target genes predicted by two databases simultaneously were considered as reliable target genes. (3) After cross analysis of the predicted target genes and DEmRNAs, the overlapped mRNAs were used for further analysis. (4) Then, further integrating analysis was done on lncRNA-miRNA pairs and miRNA-mRNA pairs to construct lncRNA-miRNA-mRNA ceRNA regulating network, which was visualized using Cytoscape (version 3.7.2) (https://cytoscape.org/) [25].


### 2.4. Gene Expression Profiling Interactive Analysis (GEPIA)

The GEPIA (http://gepia.cancer-pku.cn/index.html) [26] platform includes RNA sequencing data from 9736 tumor tissues and 8587 normal tissues from TCGA and GTEx databases, which mainly provides functions including gene expression analysis, gene correlation analysis, survival analysis, similar gene prediction, and dimensionality reduction analysis. In this study, the differential expression of RNAs was compared between tumor and adjacent samples in TCGA-CESC and GTEx databases.


### 2.5. Statistical Analyses

The effects of RNA expression and clinicopathological characteristics (age, grade, stage, etc.) on the overall survival of the CESC patients were determined by univariate and multivariate Cox regression analyses.P<0.05 was considered as statistically significant. R software v3.6.1 was utilized in all statistical analyses.


### 2.6. Cell Lines

In our present experiment, human normal cervical epithelial cell H8 (Shanghai Baiye Biotechnology Center, Shanghai, China) and human cervical cancer cell Hela (Cell Bank of Wuhan University, Wuhan, China) were included. All cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (C11995500BT, GIBCO), which was supplemented with 1% penicillin (SV30010, HyClone) and 10% fetal bovine serum (FBSSA500-S, AusGeneX). H8 cells were cultured in a 37°C, CO2-free incubator, and Hela cells were cultured in a 37°C, 5% CO2 incubator.


### 2.7. qRT-RCR

Total RNA was extracted with TRIzol universal reagent (DP424, Tiangen Biochemical) and was detected using NanoDrop 2000. RevertAid First Strand cDNA Synthesis Kit (K1622, Thermo) was used for reverse transcription, and TB Green® Premix Ex Taq™ II (RR820A, Takara) was utilized for PCR amplification on ABI Veriti thermal cycler. The internal reference was GAPDH and the primer sequences are listed in Table2. The amplification procedure included 95°C 30 sec predenaturation, 40 cycles, 95°C 10 sec, and 60°C 30 sec. Three repeats were adopted for each sample. All results were calculated according to 2-ΔΔCT formula.Table 2
Primer sequences for RT-PCR.
GenesForward primer (5′-3′)Reverse primer (5′-3′)Product length (bp)GAPDHGAAGGTGAAGGTCGGAGTCGAAGATGGTGATGGGATTTC227CADM3-AS1AGAGGCTCAGGTCAGACAATACAGCACCAACTGCGATGATTAGGC197LINC00092TTGTCGGAAGCACTCTACGCCGAACACCCCTTCTCCAGTCTCC116ZNF667-AS1GCAGTAGTGCCCCTGTTCATTAGAGGACAAAACAAGGTGAGAATGG144


### 2.8. Ethical Considerations

The study was done in accordance with Good Clinical Practice and the ethical principles originating in the Declaration of Helsinki. The protocol and informed consent document were approved by the institutional review board or independent ethics committee for each clinical site (Ethics No.: NU20200102).


## 2.1. Research Objects

All CESC-related data here was downloaded from The Cancer Genome Atlas (TCGA) (https://tcga-data.nci.nih.gov/tcga/) database, searching with keyword “TCGA-CESC”. Totally, 302 CESC patients’ data was downloaded, of which 306 CESC tumor samples’ mRNA-seq data and corresponding lncRNA data was obtained. Moreover, the survival information of 291 CESC patients was complete (Table 1), which was used for subsequent survival analysis. Additionally, the miRNA-seq data of 312 CESC samples (including 3 adjacent samples) were also downloaded.Table 1
Clinicopathological characteristics of TCGA patients with cervical cancer.
CharacteristicsGroupsPatientsAgeMedian46Range20-88TNM stageI159 (54.64%)II64 (21.99%)III41 (14.09%)IV21 (7.22%)Unknown6 (2.06%)GradeG118 (6.19%)G2129 (44.33%)G3116 (39.86%)GX24 (8.25%)Unknown4 (1.37%)Smoking historyNonsmoker141 (48.45%)Reformed smoker51 (17.53%)Smoker63 (21.65%)Unknown36 (12.37%)RaceAmerican Indian or Alaska native7 (2.41%)Asian19 (6.53%)Black or African American30 (10.31%)Native Hawaiian or other Pacific Islander2 (0.69%)White204 (70.10%)Unknown29 (9.97%)StatusAlive219 (75.26%)Dead72 (24.74%)


## 2.2. Differentially Expressed Analysis

According to the median expression of RILPL2, all CESC samples were divided into two groups, high and low RILPL2 expression CESC samples, which were then subjected to the differentially expressed analysis (limma of R [19]). The differentially expressed mRNA (DEmRNA) was screened based on false discovery rate FDR<0.05 and logfoldchangeFC>1(FDR was calculated according to the Benjamini and Hochberg method). The screening criteria of differentially expressed miRNA (DEmiRNA) and lncRNA (DElncRNA) were FDR<0.05 and logFC>0.5.


## 2.3. Construction of ceRNA Regulation Network

The ceRNA hypothesis has demonstrated the competitive activity of some lncRNAs for some binding sites of target miRNAs, thereby regulating the mRNA expression indirectly [17, 20, 21]. Based on this hypothesis, the ceRNA regulation network was then constructed following the below steps. (1) The target miRNAs of DElncRNAs and the lncRNA-miRNA interaction pairs were predicted using LncBase v2 (http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=lncbasev2%2Findex) [22]. (2) The cross analysis was conducted on the DEmiRNAs and target miRNAs of DElncRNAs using VennDiagram of R (https://CRAN.R-project.org/package=VennDiagram). Then, the target genes of the overlapped miRNAs were predicted utilizing miRDB (http://www.mirdb.org/) [23] and TargetScan (version 7.2) (http://www.targetscan.org/vert_72/) [24]. Only those target genes predicted by two databases simultaneously were considered as reliable target genes. (3) After cross analysis of the predicted target genes and DEmRNAs, the overlapped mRNAs were used for further analysis. (4) Then, further integrating analysis was done on lncRNA-miRNA pairs and miRNA-mRNA pairs to construct lncRNA-miRNA-mRNA ceRNA regulating network, which was visualized using Cytoscape (version 3.7.2) (https://cytoscape.org/) [25].


## 2.4. Gene Expression Profiling Interactive Analysis (GEPIA)

The GEPIA (http://gepia.cancer-pku.cn/index.html) [26] platform includes RNA sequencing data from 9736 tumor tissues and 8587 normal tissues from TCGA and GTEx databases, which mainly provides functions including gene expression analysis, gene correlation analysis, survival analysis, similar gene prediction, and dimensionality reduction analysis. In this study, the differential expression of RNAs was compared between tumor and adjacent samples in TCGA-CESC and GTEx databases.


## 2.5. Statistical Analyses

The effects of RNA expression and clinicopathological characteristics (age, grade, stage, etc.) on the overall survival of the CESC patients were determined by univariate and multivariate Cox regression analyses.P<0.05 was considered as statistically significant. R software v3.6.1 was utilized in all statistical analyses.


## 2.6. Cell Lines

In our present experiment, human normal cervical epithelial cell H8 (Shanghai Baiye Biotechnology Center, Shanghai, China) and human cervical cancer cell Hela (Cell Bank of Wuhan University, Wuhan, China) were included. All cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (C11995500BT, GIBCO), which was supplemented with 1% penicillin (SV30010, HyClone) and 10% fetal bovine serum (FBSSA500-S, AusGeneX). H8 cells were cultured in a 37°C, CO2-free incubator, and Hela cells were cultured in a 37°C, 5% CO2 incubator.


## 2.7. qRT-RCR

Total RNA was extracted with TRIzol universal reagent (DP424, Tiangen Biochemical) and was detected using NanoDrop 2000. RevertAid First Strand cDNA Synthesis Kit (K1622, Thermo) was used for reverse transcription, and TB Green® Premix Ex Taq™ II (RR820A, Takara) was utilized for PCR amplification on ABI Veriti thermal cycler. The internal reference was GAPDH and the primer sequences are listed in Table2. The amplification procedure included 95°C 30 sec predenaturation, 40 cycles, 95°C 10 sec, and 60°C 30 sec. Three repeats were adopted for each sample. All results were calculated according to 2-ΔΔCT formula.Table 2
Primer sequences for RT-PCR.
GenesForward primer (5′-3′)Reverse primer (5′-3′)Product length (bp)GAPDHGAAGGTGAAGGTCGGAGTCGAAGATGGTGATGGGATTTC227CADM3-AS1AGAGGCTCAGGTCAGACAATACAGCACCAACTGCGATGATTAGGC197LINC00092TTGTCGGAAGCACTCTACGCCGAACACCCCTTCTCCAGTCTCC116ZNF667-AS1GCAGTAGTGCCCCTGTTCATTAGAGGACAAAACAAGGTGAGAATGG144


## 2.8. Ethical Considerations

The study was done in accordance with Good Clinical Practice and the ethical principles originating in the Declaration of Helsinki. The protocol and informed consent document were approved by the institutional review board or independent ethics committee for each clinical site (Ethics No.: NU20200102).


## 3. Results

### 3.1. Identification of Differentially Expressed mRNAs, miRNAs, and lncRNAs

Based on the CESC-related data in TCGA, our process overview is shown in Figure1. Firstly, according to the median expression of RILPL2, all CESC samples in TCGA databases were divided into two groups, high and low RILPL2 expression CESC samples. Compared with highly RILPL2 expressed CESC samples, there were 1227 DEmRNAs in low RILPL2 expression CESC samples, comprising 58 upregulated mRNAs and 1169 downregulated mRNAs (Figure 2(a)); 39 DEmiRNAs, of which 15 were upregulated and 24 were downregulated (Figure 2(b)); and 1544 DElncRNAs, including 37 upregulated and 1507 downregulated lncRNAs (Figure 2(c)). All DEmRNAs, DEmiRNAs, and DElncRNAs exhibited significantly differential expressions between high and low RILPL2 expression CESC patients (Figures 2(d)–2(f)).Figure 1
The process overview of this work.Figure 2
Differential expressions of mRNAs, miRNAs, and lncRNAs. (a–c) The volcano map of DEmRNAs, DEmiRNAs, and DElncRNAs, respectively. (d–f) Heat map of DEmRNAs, DEmiRNAs, and DElncRNAs, respectively.X-axis: RNAs; Y-axis: samples.
(a)(b)(c)(d)(e)(f)


### 3.2. Construction of lncRNA-miRNA-mRNA ceRNA Regulating Network

Firstly, totally 2574 potential target miRNAs of DElncRNAs were identified using LncBase v2 database. We found that there were 2 overlapped miRNAs (hsa-miR-451a and hsa-miR-1293) between 39 DEmiRNAs and 2574 potential target miRNAs (Figure3(a)). The lncRNA-miRNA pairs are listed in Table S1. Then, the target mRNAs of hsa-miR-451a and hsa-miR-1293 were predicted using miRDB database and TargetScan database. Totally, 20 overlapped mRNAs were identified after cross analysis of predicted target mRNAs and DEmRNAs (Figure 3(b)). For the miRNA-mRNA pairs, see Table S2. Integrating lncRNA-miRNA pairs and miRNA-mRNA pairs, 1 miRNA hsa-miR-1293, 20 mRNAs, and 45 lncRNAs were obtained (Table S3). Based on the ceRNA hypothesis, there were negative regulations between lncRNAs vs. miRNAs and miRNAs vs. mRNAs. Thus, 1 miRNA (upregulated), 20 mRNAs (downregulated), and 43 lncRNAs (downregulated) were finally included in the ceRNA network (all samples used are listed in Table S4) (Figure 3(c)).Figure 3
ceRNA regulating network. (a) Venn diagram of DEmiRNAs and targeted miRNAs of DElncRNAs. (b) Venn diagram of DEmRNAs and target genes of miRNAs. (c) The lncRNA-miRNA-mRNA ceRNA network.
(a)(b)(c)


### 3.3. The RNAs Related to the Prognosis of CESC in ceRNA Network

Subsequently, the expression levels of RNAs in ceRNA network were compared between cancer and adjacent samples utilizing GEPIA. There were 12 differentially expressed mRNAs between cancer and adjacent samples, among whichANXA6, CCL21, MEIS1, NRXN3, NXPH3, PDZRN3, PLA2G2A, PRELP, SLITRK3, and SOD3 showed lower expression in tumor samples, and BCL2L14 and CXCL9 were highly expressed in cancer samples (Figure 4(a)). Besides, totally 14 lncRNAs showed differential expressions between cancer and adjacent samples, among which 13 lncRNAs (CADM3-AS1, DNM3OS, EMX2OS, HOTTIP, ITGA9-AS1, LIFR-AS1, LINC00092, LINC01278, MAGI2-AS3, NR2F2-AS1, PCAT19, PTPRD-AS1, and ZNF667-AS1) had lower expressions in tumor samples and LINC00885 was highly expressed in cancer samples (Figure 4(c)). Considering that there was no miRNA data in GEPIA, the expression levels of miRNAs in ceRNA network were analyzed with the TCGA miRNA-seq data. The results suggested that hsa-miR-1293 was also differentially expressed in cancer and adjacent samples (Figure 4(b)).Figure 4
The differentially expressed RNAs in the ceRNA network between cancer and adjacent samples. GEPIA was used to compare the expression levels of RNAs in the ceRNA network between cancer and surrounding samples. There were 12 mRNAs that differed between cancer and surrounding tissues. (a) 12 mRNAs were differentially expressed between cancer and adjacent samples.∗P value < 0.05. (b) miRNA hsa-miR-1293 was differentially expressed between cancer and adjacent samples. (c) 14 lncRNAs were differentially expressed between cancer and adjacent samples.
(a)(b)(c)Moreover, univariate and multivariate Cox regression analyses were conducted to determine whether these differentially expressed RNAs were related to the prognosis of the CESC patients. The results of the univariate Cox regression analysis showed that 1 mRNA (BCL2L14) and six lncRNAs (LINC00885, ITGA9-AS1, CADM3-AS1, ZNF667-AS1, PTPRD-AS1, and LINC00092) were correlated with the overall survival (OS) of CESC patients (P value < 0.05) (Figure 5(a)). After the multivariate Cox regression analysis including age, TNM stage, grade, smoking history, and various RNAs, we found that lncRNAs CADM3-AS1 (HR=0.845, 95% CI: 0.731-0.976, P=0.0223), LINC00092 (HR=0.834, 95% CI: 0.698-0.996, P=0.0457), and ZNF667-AS1 (HR=0.826, 95% CI: 0.714-0.956, P=0.0102) were still associated with the OS of CESC patients. The CESC patients with lower lncRNA expression had poorer prognosis (Figures 5(b)–5(d)). Collectively, CADM3-AS1, LINC00092, and ZNF667-AS1 were possible undesirable prognostic indicators for CESC patients.Figure 5
The results of univariate and multivariate Cox regression analyses. CADM3-AS1, LINC00092, and ZNF667-AS1 were also identified as potential negative prognostic factors for CESC patients. (a) Forest plot of univariate Cox regression analysis. Samples with hazard ratioHR>1 have a higher risk of death; HR<1, on the contrary. (b–d) Multivariate Cox regression analysis results of CADM3-AS1, LINC00092, and ZNF667-AS1.
(a)(b)(c)(d)


### 3.4. The Expression Levels of CADM3-AS1, LINC00092, and ZNF667-AS1 in Cell Lines

Furthermore, to verify our findings mined from the publicly obtained CESC-related data, we have detected the expression levels ofCADM3-AS1, LINC00092, and ZNF667-AS1 in H8 and Hela cell lines. The results suggested that CADM3-AS1, LINC00092, and ZNF667-AS1 were all downregulated in Hela, which was in line with our results of bioinformatic analysis (Figure 6).Figure 6
The expression levels of CADM3-AS1, LINC00092, and ZNF667-AS1 in cell lines.CADM3-AS1, LINC00092, and ZNF667-AS1 were all downregulated in Hela, which matched our bioinformatic analysis results (vs. H8 cells, ∗∗P<0.01, ∗∗∗P<0.001).


## 3.1. Identification of Differentially Expressed mRNAs, miRNAs, and lncRNAs

Based on the CESC-related data in TCGA, our process overview is shown in Figure1. Firstly, according to the median expression of RILPL2, all CESC samples in TCGA databases were divided into two groups, high and low RILPL2 expression CESC samples. Compared with highly RILPL2 expressed CESC samples, there were 1227 DEmRNAs in low RILPL2 expression CESC samples, comprising 58 upregulated mRNAs and 1169 downregulated mRNAs (Figure 2(a)); 39 DEmiRNAs, of which 15 were upregulated and 24 were downregulated (Figure 2(b)); and 1544 DElncRNAs, including 37 upregulated and 1507 downregulated lncRNAs (Figure 2(c)). All DEmRNAs, DEmiRNAs, and DElncRNAs exhibited significantly differential expressions between high and low RILPL2 expression CESC patients (Figures 2(d)–2(f)).Figure 1
The process overview of this work.Figure 2
Differential expressions of mRNAs, miRNAs, and lncRNAs. (a–c) The volcano map of DEmRNAs, DEmiRNAs, and DElncRNAs, respectively. (d–f) Heat map of DEmRNAs, DEmiRNAs, and DElncRNAs, respectively.X-axis: RNAs; Y-axis: samples.
(a)(b)(c)(d)(e)(f)


## 3.2. Construction of lncRNA-miRNA-mRNA ceRNA Regulating Network

Firstly, totally 2574 potential target miRNAs of DElncRNAs were identified using LncBase v2 database. We found that there were 2 overlapped miRNAs (hsa-miR-451a and hsa-miR-1293) between 39 DEmiRNAs and 2574 potential target miRNAs (Figure3(a)). The lncRNA-miRNA pairs are listed in Table S1. Then, the target mRNAs of hsa-miR-451a and hsa-miR-1293 were predicted using miRDB database and TargetScan database. Totally, 20 overlapped mRNAs were identified after cross analysis of predicted target mRNAs and DEmRNAs (Figure 3(b)). For the miRNA-mRNA pairs, see Table S2. Integrating lncRNA-miRNA pairs and miRNA-mRNA pairs, 1 miRNA hsa-miR-1293, 20 mRNAs, and 45 lncRNAs were obtained (Table S3). Based on the ceRNA hypothesis, there were negative regulations between lncRNAs vs. miRNAs and miRNAs vs. mRNAs. Thus, 1 miRNA (upregulated), 20 mRNAs (downregulated), and 43 lncRNAs (downregulated) were finally included in the ceRNA network (all samples used are listed in Table S4) (Figure 3(c)).Figure 3
ceRNA regulating network. (a) Venn diagram of DEmiRNAs and targeted miRNAs of DElncRNAs. (b) Venn diagram of DEmRNAs and target genes of miRNAs. (c) The lncRNA-miRNA-mRNA ceRNA network.
(a)(b)(c)


## 3.3. The RNAs Related to the Prognosis of CESC in ceRNA Network

Subsequently, the expression levels of RNAs in ceRNA network were compared between cancer and adjacent samples utilizing GEPIA. There were 12 differentially expressed mRNAs between cancer and adjacent samples, among whichANXA6, CCL21, MEIS1, NRXN3, NXPH3, PDZRN3, PLA2G2A, PRELP, SLITRK3, and SOD3 showed lower expression in tumor samples, and BCL2L14 and CXCL9 were highly expressed in cancer samples (Figure 4(a)). Besides, totally 14 lncRNAs showed differential expressions between cancer and adjacent samples, among which 13 lncRNAs (CADM3-AS1, DNM3OS, EMX2OS, HOTTIP, ITGA9-AS1, LIFR-AS1, LINC00092, LINC01278, MAGI2-AS3, NR2F2-AS1, PCAT19, PTPRD-AS1, and ZNF667-AS1) had lower expressions in tumor samples and LINC00885 was highly expressed in cancer samples (Figure 4(c)). Considering that there was no miRNA data in GEPIA, the expression levels of miRNAs in ceRNA network were analyzed with the TCGA miRNA-seq data. The results suggested that hsa-miR-1293 was also differentially expressed in cancer and adjacent samples (Figure 4(b)).Figure 4
The differentially expressed RNAs in the ceRNA network between cancer and adjacent samples. GEPIA was used to compare the expression levels of RNAs in the ceRNA network between cancer and surrounding samples. There were 12 mRNAs that differed between cancer and surrounding tissues. (a) 12 mRNAs were differentially expressed between cancer and adjacent samples.∗P value < 0.05. (b) miRNA hsa-miR-1293 was differentially expressed between cancer and adjacent samples. (c) 14 lncRNAs were differentially expressed between cancer and adjacent samples.
(a)(b)(c)Moreover, univariate and multivariate Cox regression analyses were conducted to determine whether these differentially expressed RNAs were related to the prognosis of the CESC patients. The results of the univariate Cox regression analysis showed that 1 mRNA (BCL2L14) and six lncRNAs (LINC00885, ITGA9-AS1, CADM3-AS1, ZNF667-AS1, PTPRD-AS1, and LINC00092) were correlated with the overall survival (OS) of CESC patients (P value < 0.05) (Figure 5(a)). After the multivariate Cox regression analysis including age, TNM stage, grade, smoking history, and various RNAs, we found that lncRNAs CADM3-AS1 (HR=0.845, 95% CI: 0.731-0.976, P=0.0223), LINC00092 (HR=0.834, 95% CI: 0.698-0.996, P=0.0457), and ZNF667-AS1 (HR=0.826, 95% CI: 0.714-0.956, P=0.0102) were still associated with the OS of CESC patients. The CESC patients with lower lncRNA expression had poorer prognosis (Figures 5(b)–5(d)). Collectively, CADM3-AS1, LINC00092, and ZNF667-AS1 were possible undesirable prognostic indicators for CESC patients.Figure 5
The results of univariate and multivariate Cox regression analyses. CADM3-AS1, LINC00092, and ZNF667-AS1 were also identified as potential negative prognostic factors for CESC patients. (a) Forest plot of univariate Cox regression analysis. Samples with hazard ratioHR>1 have a higher risk of death; HR<1, on the contrary. (b–d) Multivariate Cox regression analysis results of CADM3-AS1, LINC00092, and ZNF667-AS1.
(a)(b)(c)(d)


## 3.4. The Expression Levels of CADM3-AS1, LINC00092, and ZNF667-AS1 in Cell Lines

Furthermore, to verify our findings mined from the publicly obtained CESC-related data, we have detected the expression levels ofCADM3-AS1, LINC00092, and ZNF667-AS1 in H8 and Hela cell lines. The results suggested that CADM3-AS1, LINC00092, and ZNF667-AS1 were all downregulated in Hela, which was in line with our results of bioinformatic analysis (Figure 6).Figure 6
The expression levels of CADM3-AS1, LINC00092, and ZNF667-AS1 in cell lines.CADM3-AS1, LINC00092, and ZNF667-AS1 were all downregulated in Hela, which matched our bioinformatic analysis results (vs. H8 cells, ∗∗P<0.01, ∗∗∗P<0.001).


## 4. Discussion

In this work, based on our former findings and integrated analyses of the CESC data in TCGA database, RILPL2-related ceRNA network has been constructed, contributing to identify novel prognostic biomarkers for CESC patients. Moreover, we found that three lncRNAs in ceRNA network were significantly correlated with the OS of CESC patients.Tumor heterogeneity has been an important factor for therapy resistance in many cancers, including CESC [27], leading to the differential OS of cancer patients. We have previously explored the role of RILPL2 in the progression and prognosis of CESC. Increasing evidence has indicated the pivotal role of various RNAs in cancer development or prognosis [28]. To study more potential influence of RILPL2, we have further investigated its related lncRNA-miRNA-mRNA ceRNA regulating network in present research. Between high and low RILPL2 expression CESC patients, totally 1227 DEmRNAs, 39 DEmiRNAs, and 1544 DElncRNAs were identified. Via the multiple cross analyses of differentially expressed RNAs (DERNAs) and target RNAs, 1 miRNA hsa-miR-1293, 20 mRNAs, and 45 lncRNAs were obtained. Furthermore, based on the ceRNA hypothesis, 1 miRNA hsa-miR-1293, 20 mRNAs, and 43 lncRNAs were maintained to build ceRNA network. Subsequently, based on the data in GEPIA or TCGA, the RNA expressions in ceRNA network were compared between CESC and adjacent samples. The hsa-miR-1293, 12 mRNAs, and 14 lncRNAs were significantly differentially expressed, which might be associated with the occurrence of CESC. Among them, some RNAs have been studied in different tumors. For example, hsa-miR-1293 has been documented to target PGM5 promoting the proliferation and migration of lung adenocarcinoma cells [29]. Besides, the diagnostic or prognostic roles of hsa-miR-1293 have also been investigated in renal cell carcinoma [30], pancreatic adenocarcinoma [31], and so on. However, our present research has firstly explored hsa-miR-1293 in CESC as far as we know.Furthermore, to explore the possible association between the above RNAs and the prognosis of the CESC patients, univariate and multivariate Cox regression analyses were conducted. We found that CADM3-AS1, LINC00092, and ZNF667-AS1 were significantly associated with the OS of CESC patients, and the patients with low lncRNA expression had relatively worse prognosis. lncRNAs usually involve in the gene modulation at various levels, such as DNA methylation [32] and serving as the precursor of miRNA [33]. Many lncRNAs have been evidenced to regulate tumorigenesis and metastasis, such as lncRNA XLOC_006390 [14] and lncRNA PTENP1 [34]. In our study, low CADM3-AS1 expression was correlated with the poor OS of CESC patients, but limited studies of its role in cancers were found via searching the literature, whereas gene CADM3 has been indicated to be related to the OS of colorectal cancer [35]. In retinoblastoma, CADM3 could regulate the cell proliferation, migration, and invasion indirectly [36]. Long noncoding RNA LINC00092 acts in cancer-associated fibroblasts to drive glycolysis and progression of ovarian cancer [37]. lncRNA ZNF667-AS1 (NR_036521.1) inhibits the progression of colorectal cancer via regulating ANK2/JAK2 expression [38].Deepening functional information of CADM3-AS1 in CESC still needs more exploration. LINC00092 has been recently demonstrated to drive glycolysis and progression of ovarian cancer, which was mediated by cancer-associated fibroblasts [37]. Moreover, low LINC00092 expression was also related to the poorer prognosis and tumorigenesis in lung adenocarcinoma [39], which was line with our results in CESC. Additionally, as a member of the C2H2 zinc finger protein family, ZNF667 has been reported to be an oncogene in hepatocellular carcinoma [40]. Besides, upregulation of ZNF667-AS1 has been indicated to inhibit the progression of CESC through regulating miR-93-3p-dependent PEG3 [41], which supported our findings indirectly. Collectively, these lncRNAs are probably promising prognostic biomarkers for CESC patients, which deserve further investigation in near future.The relationship between ceRNA and CESC has been marginally explored. Currently, some papers have used bioinformatic analysis to show a relationship between ferroptosis and CESC. Li et al. [42] discovered a four-gene signature based on likely predictive ceRNA regulatory genes, and the genes (OPN3, DAAM2, HENMT1, and CAVIN3) were shown to be positively linked with CESC clinical stage. Ding et al. [43] developed a new CESC prediction model that incorporates five DEmRNAs, including ADGRF4, ANXA8L1, HCAR3, IRF6, and PDE2A, which might be used to predict prognosis in CESC patients. The strength of this study is not only the exploration of predictive genes for CESE based on ceRNA regulatory networks. It provides certain ideas for future clinical diagnosis and treatment. It also provides a new idea for future protein simulation and drug design based on CADM3-AS1, LINC00092, and ZNF667-AS1 genes. However, this study compared samples from both cancers, and the data were not from the same microarray and may have been somewhat biased.


## 5. Conclusions

In summary, we have firstly constructed RILPL2-related ceRNA network in CESC samples, which is based on 1 miRNA, 20 mRNAs, and 43 lncRNAs. Furthermore,CADM3-AS1, LINC00092, and ZNF667-AS1 in ceRNA network are evidenced to be significantly associated with the prognosis of CESC patients. These three lncRNAs are probably promising prognostic biomarkers for CESC.This study has significant limitations, despite the fact that it presents some theoretical underpinnings and research proposals for CESC. For future enhancements, the following proposals are made: (1) Because the present data is generated from the GEO database, determining the trustworthiness and quality of the statistical data is difficult. In the future, the number of data sources will be increased while the data offset will be reduced. (2) Conduct more scientific and clinical study to investigate whether some medicines might improve anticancer and immunological performance in CESC patients by modulating the quantity of ceRNA network.



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*Source: 1766080-2023-02-08.xml*