ceRNA network construction and comparison of gastric cancer with or without Helicobacter pylori infection

Gastric cancer (GC) is a lethal disease, and among its variety of etiological factors, Helicobacter pylori (H. pylori) infection is the strongest risk factor. However, the genetic and molecular mechanisms underlying H. pylori-related GC need further elucidation. We investigated the competing endogenous RNA (ceRNA) network differences between H. pylori (+) and H. pylori (−) GC. The long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) expression data from 32 adjacent noncancerous samples and 18 H. pylori (+) and 141 H. pylori (−) stomach adenocarcinoma samples were downloaded from the TCGA database. After construction of lncRNA–miRNA–mRNA ceRNA networks of H. pylori (+) and H. pylori (−) GC, Panther and Kobas databases were used to analyze the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Finally, survival analysis was used to discover the key genes. In H. pylori (+) GC, we identified a total of 1,419 lncRNAs, 82 miRNAs, and 2,501 mRNAs with differentially expressed profiles. In H. pylori (−) GC, 2,225 lncRNAs, 130 miRNAs, and 3,146 mRNAs were differentially expressed. Furthermore, three unique pathways (cytokine–cytokine receptor interaction, HIF-1 signaling pathway, and Wnt signaling pathway) were enriched in H. pylori (+) GC. According to the overall survival analysis, three lncRNAs (AP002478.1, LINC00111, and LINC00313) and two mRNAs (MYB and COL1A1) functioned as prognostic biomarkers for patients with H. pylori (+) GC. In conclusion, our study has identified the differences in ceRNA regulatory networks between H. pylori (+) and H. pylori (−) GC and provides a rich candidate reservoir for future studies.     

Integrated analysis of ceRNA network and tumor-infiltrating immune cells in esophageal cancer

Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis.     

Construction and analysis of dysregulated lncRNA-associated ceRNA network in colorectal cancer

Colorectal cancer (CRC) is one of the most frequently diagnosed digestive system cancer. The aim of the present study was to investigate the interactions among messenger RNAs (mRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs) in CRC to reveal the mechanisms of CRC. Differentially expressed genes (DEGs) were identified from public gene expression data sets. One thousand eighty-one common dysregulated mRNAs in two data sets were identified. Gene function analysis and protein-protein interaction network analysis indicated that these DEGs might play important roles in CRC. LINC00365 was selected through coding- noncoding network analysis and its expression was validated upregulated in 22 paired clinical samples and four CRC cell lines. A competing endogenous RNA network composed of 70 miRNAs, nine mRNAs, and LINC00365 was constructed. Eight of nine mRNAs were validated upregulated in The Cancer Genome Atlas data set. Our results suggested that LINC00365 was an oncogene in CRC and it could regulate the expression of several mRNAs through sponging miRNAs.     

Dysregulated ceRNA network modulated by copy number variation-driven lncRNAs in breast cancer: A comprehensive analysis

Breast cancer is a malignancy harmful to physical and mental health in women, with quite high mortality. Copy number variations (CNVs) are vital factors affecting the progression of breast cancer. Detecting CNVs in breast cancer to predict the prognosis of patients has become a promising approach to accurate treatment in recent years. The differential analysis was performed on CNVs of long noncoding RNAs (lncRNAs) as well as the expression of lncRNAs, microRNAs (miRNAs) and mRNAs in normal tissue and breast tumor tissue based on The Cancer Genome Atlas (TCGA) database. The CNV-driven lncRNAs were identified by the Kruskal–Wallis test. Meanwhile, a competitive endogenous RNA (ceRNA) network regulated by CNV-driven lncRNA was constructed. As the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed, the mRNAs in the dysregulated ceRNA network were mainly enriched in the biological functions and signaling pathways, including the Focal Adhesion-PI3K-Akt–mTOR-signaling pathway, the neuronal system, metapathway biotransformation Phase I and II and blood circulation, etc. The relationship between the CNVs of five lncRNAs and their gene expression in the ceRNA network was analyzed via a chi-square test, which confirmed that except for LINC00243, the expression of four lncRNAs was notably correlated with the CNVs. The survival analysis revealed that only the copy number gain of LINC00536 was evidently related to the poor prognosis of patients. The CIBERSORT algorithm showed that five lncRNAs were correlated with the abundance of immune cell infiltration and immune checkpoints. In a word, by analyzing CNV-driven lncRNAs and the ceRNA network regulated by these lncRNAs, this study explored the mechanism of breast cancer and provided novel insights into new biomarkers.     

Comprehensive analysis of a ceRNA network reveals potential prognostic cytoplasmic lncRNAs involved in HCC progression

The aberrant expression of long noncoding RNAs (lncRNAs) has drawn increasing attention in the field of hepatocellular carcinoma (HCC) biology. In the present study, we obtained the expression profiles of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) in 371 HCC tissues and 50 normal tissues from The Cancer Genome Atlas (TCGA) and identified hepatocarcinogenesis-specific differentially expressed genes (DEGs, log fold change ≥ 2, FDR < 0.01), including 753 lncRNAs, 97 miRNAs, and 1,535 mRNAs. Because the specific functions of lncRNAs are closely related to their intracellular localizations and because the cytoplasm is the main location for competitive endogenous RNA (ceRNA) action, we analyzed not only the interactions among these DEGs but also the distributions of lncRNAs (cytoplasmic, nuclear or both). Then, an HCC-associated deregulated ceRNA network consisting of 37 lncRNAs, 10 miRNAs, and 26 mRNAs was constructed after excluding those lncRNAs located only in the nucleus. Survival analysis of this network demonstrated that 15 lncRNAs, 3 miRNAs, and 16 mRNAs were significantly correlated with the overall survival of HCC patients (p < 0.01). Through multivariate Cox regression and lasso analysis, a risk score system based on 13 lncRNAs was constructed, which showed good discrimination and predictive ability for HCC patient survival time. This ceRNA network-construction approach, based on lncRNA distribution, not only narrowed the scope of target lncRNAs but also provided specific candidate molecular biomarkers for evaluating the prognosis of HCC, which will help expand our understanding of the ceRNA mechanisms involved in the early development of HCC.     

Construction of lncRNA-associated ceRNA networks to identify prognostic lncRNA biomarkers for glioblastoma

Long noncoding RNAs (lncRNAs) serve as competitive endogenous RNAs (ceRNAs) that play significant regulatory roles in the pathogenesis of tumors. However, the role of lncRNAs, especially the lncRNA-related ceRNA regulatory network, in glioblastoma (GBM) has not been fully elucidated. The goal of the current study was to construct lncRNA-microRNA-mRNA-related ceRNA networks for further investigation of their mechanism of action in GBM. We downloaded data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases and identified differential lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) associated with GBM. A ceRNA network was constructed and analyzed to examine the relationship between lncRNAs and patients’ overall survival. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) were used to analyze the related mRNAs to indirectly explain the mechanism of action of lncRNAs. The potential effective drugs for the treatment of GBM were identified using the connectivity map (CMap). After integrated analysis, we obtained a total of 210 differentially expressed lncRNAs, 90 differentially expressed miRNAs, and 2508 differentially expressed mRNAs (DEmRNAs) from the TCGA and GEO databases. Using these differential genes, we constructed a lncRNA-associated ceRNA network. Six lncRNAs in the ceRNA network were associated with the overall survival of patients with GBM. Through KEGG analysis, it was found that the DEmRNAs involved in the network are related to cancer-associated pathways, for instance, mitogen-activated protein kinase and Ras signaling pathways. CMap analysis revealed four small-molecule compounds that could be used as drugs for the treatment of GBM. In this study, a multi-database joint analysis was used to construct a lncRNA-related ceRNA network to help identify the regulatory functions of lncRNAs in the pathogenesis of GBM.     

Identification of a potential prognostic lncRNA-miRNA-mRNA signature in endometrial cancer based on the competing endogenous RNA network

Endometrial cancer is one of the most common gynecological malignant tumors. The roles of competing endogenous RNAs (ceRNAs) in this disease, however, remain unclear. In this study, we constructed a ceRNA network to reveal the core ceRNAs in endometrial cancer. Differentially expressed genes were summarized from The Cancer Genome Atlas database, whereupon 140 genes were identified for building the network. Further correlation, survival, and enrichment analyses suggested that these genes may help towards elucidating the molecular mechanisms of endometrial cancer. After validation of the findings with the GSE17025 data set, LINC00958, microRNA-761, and DOLPP1 were highlighted as the critical genes in the ceRNA network. Our work suggests that LINC00958 may regulate DOLPP1 by “sponging” miR-761 in endometrial cancer.     

To construct a ceRNA regulatory network as prognostic biomarkers for bladder cancer

Emerging evidence demonstrates that competing endogenous RNA (ceRNA) hypothesis has played a role in molecular biological mechanisms of cancer occurrence and development. But the effect of ceRNA network in bladder cancer (BC), especially lncRNA‐miRNA‐mRNA regulatory network of BC, was not completely expounded. By means of The Cancer Genome Atlas (TCGA) database, we compared the expression of RNA sequencing (RNA‐Seq) data between 19 normal bladder tissue and 414 primary bladder tumours. Then, weighted gene co‐expression network analysis (WGCNA) was conducted to analyse the correlation between two sets of genes with traits. Interactions between miRNAs, lncRNAs and target mRNAs were predicted by MiRcode, miRDB, starBase, miRTarBase and TargetScan. Next, by univariate Cox regression and LASSO regression analysis, the 86 mRNAs obtained by prediction were used to construct a prognostic model which contained 4 mRNAs (ACTC1 + FAM129A + OSBPL10 + EPHA2). Then, by the 4 mRNAs in the prognostic model, a ceRNA regulatory network with 48 lncRNAs, 14 miRNAs and 4 mRNAs was constructed. To sum up, the ceRNA network can further explore gene regulation and predict the prognosis of BC patients.