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Genome-wide analysis of lncRNAs, miRNAs, and mRNAs forming a prognostic scoring system in esophageal squamous cell carcinoma.

ABSTRACT: Background:Esophageal squamous cell carcinoma (ESCC) is the main subtype of esophageal carcinoma. Protein coding genes and non-coding RNAs can be powerful prognostic factors in multiple cancers, including ESCC. However, there is currently no model that integrates multiple types of RNA expression signatures to predict clinical outcomes. Methods:The sequencing data (RNA-sequencing and miRNA-sequencing) and clinical data of ESCC patients were obtained from The Cancer Genome Atlas (TCGA) database, and Differential gene expression analysis, Cox regression analysis and Spearman correlation analysis were used to construct prognosis-related lncRNA-mRNA co-expression network and scoring system with multiple types of RNA. The potential molecular mechanisms of prognostic mRNAs were explored by functional enrichment analysis. Results:A total of 62 prognostic lncRNAs, eight prognostic miRNAs and 66 prognostic mRNAs were identified in ESCC (P-value < 0.05) and a prognosis-related lncRNA-mRNA co-expression network was created. Five prognosis-related hub RNAs (CDCA2, MTBP, CENPE, PBK, AL033384.1) were identified. Biological process analysis revealed that mRNAs in prognosis-related co-expression RNA network were mainly enriched in cell cycle, mitotic cell cycle and nuclear division. Additionally, we constructed a prognostic scoring system for ESCC using ten signature RNAs (MLIP, TNFSF10, SIK2, LINC01068, LINC00601, TTTY14, AC084262.1, LINC01415, miR-5699-3p, miR-552-5p). Using this system, patients in the low-risk group had better long-term survival than those in the high-risk group (log-rank, P-value < 0.0001). The area under the ROC curve (AUCs) revealed that the accuracy of the prediction model was higher than the accuracy of single type of RNA prediction model. Conclusion:In brief, we constructed a prognostic scoring system based on multiple types of RNA for ESCC that showed high predicting prognosis performance, and deeply understood the regulatory mechanism of prognosis-related lncRNA-mRNA co-expression network.

SUBMITTER: Shi X

PROVIDER: S-EPMC7017795 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:MicroRNA (miR) signatures may aid the diagnosis and prediction of cancer; therefore, miRs associated with the prognosis of esophageal squamous cell carcinoma (ESCC) were screened. miR‑sequencing (seq) and mRNA‑seq data from early‑stage ESCC samples were downloaded from The Cancer Genome Atlas (TCGA) database, and samples from subjects with a >6‑month survival time were assessed with Cox regression analysis for prognosis‑associated miRs. A further two miR expression datasets of ESCC samples, GSE43732 and GSE13937, were downloaded from the Gene Expression Omnibus database. Common miRs between prognosis‑associated miRs, and miRs in the GSE43732 and GSE13937, datasets were used for risk score calculations for each sample, and median risk scores were applied for the stratification of low‑ and high‑risk samples. A prognostic scoring system of signature miRs was subsequently constructed and used for survival analysis for low‑ and high‑risk samples. Differentially‑expressed genes (DEGs) corresponding to all miRs were screened and functional annotation was performed. A total of 34 prognostic miRs were screened and a scoring system was created using 10 signature miRs (hsa‑miR‑140, ‑33b, ‑34b, ‑144, ‑486, ‑214, ‑129‑2, ‑374a and ‑412). Using this system, low‑risk samples were identified to be associated with longer survival compared with high‑risk samples in the TCGA and GSE43732 datasets. Age, alcohol and tobacco use, and radiotherapy were prognostic factors for samples with different risk scores and the same clinical features. There were 168 DEGs, and the top 20 risk scores positively‑correlated and the top 20 risk scores negatively‑correlated DEGs were significantly enriched for six and 10 functional terms, respectively. 'Tight junction' and 'melanogenesis' were two significantly enriched pathways of DEGs. miR‑214, miR‑129‑2, miR‑37a and miR‑486 may predict ESCC patient survival, although further studies to validate this hypothesis are required.

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Genome-wide analysis of lncRNAs, miRNAs, and mRNAs forming a prognostic scoring system in esophageal squamous cell carcinoma.

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