Comprehensive Analysis of Alternative Splicing Signature in Gastric Cancer Prognosis Based on The Cancer Genome Atlas (TCGA) and SpliceSeq Databases

Overview

Journal Med Sci Monit

Specialties General Medicine
Pathology

Date 2020 Nov 21

PMID 33219199

Authors

Xiaohu Cheng

Xianghua Li

Yimei Gu

Lianbang Zhou

Jingjing Tang

Xiang Dai

Heng Jiang

Yang Huang

Yingfeng Zhang

Tongtong Xu

Zhining Liu

Qihong Zhao

Affiliations

Soon will be listed here.

Abstract

BACKGROUND Increasing evidence suggests that the alternative splicing (AS) signature plays a role in the carcinogenesis and prognosis of various cancers. However, the prognostic role of AS in gastric cancer is not clear and needs to be clarified. MATERIAL AND METHODS To identify the differentially expressed AS (DEAS) events, we performed a differential expression analysis between normal and tumor tissue. The DEAS event was further applied to construct a prognostic signature by performing univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. The Kaplan-Meier curve analysis and receiver operating characteristic curve (ROC) analysis were used to evaluate the prognostic value of the AS signature. In addition, the network of the splicing events with splicing factors was constructed using the Cytoscape software. RESULTS A total of 30 005 alternative splicing (AS) events with 372 patients were retrieved from the SpliceSeq database and TCGA database. By performing differential expression analysis, a total of 419 alternative splicing events were screened out, including 56 upregulated and 363 downregulated. We further constructed an AS-related prognostic signature by conducting a series bioinformatics analyses. Moreover, we identified that the AS signature could serve as an independent predictor for the prognosis of GC. We also found that AS signature had a more robust and precise efficacy for prognostic prediction in GC patients. Interestingly, the areas under 3- and 5-year survival curves are similar, both of which are greater than 1-year survival curve, suggesting that the long-term predictive accuracy of our prognostic model built upon AS signature is superior. CONCLUSIONS We performed a comprehensive analysis of overall prognostic-associated AS events concerning GC and constructed a prognostic model to predict the long-term prognostic survival outcomes in GC patients. We also developed a network of splicing events with splicing factors to reveal new potential molecular diagnostic biomarkers and therapeutic targets for GC patients.

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