Immunological significance of prognostic alternative splicing signature in hepatocellular carcinoma

比例危险模型 肝细胞癌 单变量 生存分析 列线图 医学 多元统计 RNA剪接 Lasso(编程语言) 拼接因子 选择性拼接 内科学 肿瘤科 计算生物学 生物信息学 生物 计算机科学 基因 机器学习 信使核糖核酸 遗传学 核糖核酸 万维网
作者
Qianhui Xu,Hao Xu,Rongshan Deng,Nanjun Li,Ruiqi Mu,Zhixuan Qi,Yunuo Shen,Zijie Wang,Jingchao Wen,Jiaxin Zhao,Di Weng,Wen Huang
出处
期刊:Cancer Cell International [BioMed Central]
卷期号:21 (1): 190-190 被引量:19
标识
DOI:10.1186/s12935-021-01894-z
摘要

Hepatocellular carcinoma (HCC) ranks the sixth prevalent tumors with high mortality globally. Alternative splicing (AS) drives protein diversity, the imbalance of which might act an important factor in tumorigenesis. This study aimed to construct of AS-based prognostic signature and elucidate the role in tumor immune microenvironment (TIME) and immunotherapy in HCC. Univariate Cox regression analysis was performed to determine the prognosis-related AS events and gene set enrichment analysis (GSEA) was employed for functional annotation, followed by the development of prognostic signatures using univariate Cox, LASSO and multivariate Cox regression. K-M survival analysis, proportional hazards model, and ROC curves were conducted to validate prognostic value. ESTIMATE R package, ssGSEA algorithm and CIBERSORT method and TIMER database exploration were performed to uncover the context of TIME in HCC. Quantitative real-time polymerase chain reaction was implemented to detect ZDHHC16 mRNA expression. Cytoscape software 3.8.0 were employed to visualize AS-splicing factors (SFs) regulatory networks. A total of 3294 AS events associated with survival of HCC patients were screened. Based on splicing subtypes, eight AS prognostic signature with robust prognostic predictive accuracy were constructed. Furthermore, quantitative prognostic nomogram was developed and exhibited robust validity in prognostic prediction. Besides, the consolidated signature was significantly correlated with TIME diversity and ICB-related genes. ZDHHC16 presented promising prospect as prognostic factor in HCC. Finally, the splicing regulatory network uncovered the potential functions of splicing factors (SFs). Herein, exploration of AS patterns may provide novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.,) for prognostic prediction of HCC. The AS-SF networks could open up new approach for investigation of potential regulatory mechanisms. And pivotal players of AS events in context of TIME and immunotherapy efficiency were revealed, contributing to clinical decision-making and personalized prognosis monitoring of HCC.
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