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肝细胞癌中 RNA 结合蛋白的综合生物信息学分析。

Integrated bioinformatic analysis of RNA binding proteins in hepatocellular carcinoma.

机构信息

Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital and Institute, Beijing 100142, China.

Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing 100044, China.

出版信息

Aging (Albany NY). 2020 Dec 19;13(2):2480-2505. doi: 10.18632/aging.202281.

DOI:10.18632/aging.202281
PMID:33411682
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7880356/
Abstract

RNA binding proteins (RBPs) are aberrantly expressed in a tissue-specific manner across many tumors. These proteins, which play a vital role in post-transcriptional gene regulation, are involved in RNA splicing, maturation, transport, stability, degradation, and translation. We set out to establish an accurate risk score model based on RBPs to estimate prognosis in hepatocellular carcinoma (HCC). RNA-sequencing data, proteomic data and corresponding clinical information were acquired from the Cancer Genome Atlas database and the Clinical Proteomic Tumor Analysis Consortium database respectively. We identified 406 differentially expressed RBPs between HCC tumor and normal tissues at the transcriptional and protein level. Overall, 11 RBPs (BRIX1, DYNC1H1, GTPBP4, PRKDC, RAN, RBM19, SF3B4, SMG5, SPATS2, TAF9, and THOC5) were selected to establish a risk score model. We divided HCC patients into low-risk and high-risk groups based on the median of risk score values. The survival analysis indicated that patients in the high-risk group had poorer overall survival compared to patients in the low-risk group. Our study demonstrated that 11 RBPs were associated with the overall survival of HCC patients. These RBPs may represent potential drug targets and can help optimize future clinical treatment.

摘要

RNA 结合蛋白 (RBPs) 在许多肿瘤中以组织特异性方式异常表达。这些在转录后基因调控中发挥重要作用的蛋白质参与 RNA 的剪接、成熟、运输、稳定性、降解和翻译。我们着手建立一个基于 RBPs 的精确风险评分模型，以估计肝细胞癌 (HCC) 的预后。分别从癌症基因组图谱数据库和临床蛋白质组肿瘤分析联盟数据库中获取了 RNA 测序数据、蛋白质组数据和相应的临床信息。我们在转录和蛋白质水平上鉴定了 HCC 肿瘤组织和正常组织之间的 406 个差异表达的 RBPs。总的来说，选择了 11 个 RBPs（BRIX1、DYNC1H1、GTPBP4、PRKDC、RAN、RBM19、SF3B4、SMG5、SPATS2、TAF9 和 THOC5）来建立风险评分模型。我们根据风险评分值的中位数将 HCC 患者分为低风险组和高风险组。生存分析表明，高风险组患者的总体生存率低于低风险组患者。我们的研究表明，11 个 RBPs 与 HCC 患者的总体生存率相关。这些 RBPs 可能代表潜在的药物靶点，并有助于优化未来的临床治疗。

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