基于GEO数据库的不完全射频消融后肝细胞癌关键基因的生物信息学研究

doi:10.3969/j.issn.1009-976X.2025.01.003

摘要/Abstract

摘要： 目的本研究旨在通过比较不完全射频消融（IRFA）处理肝细胞癌（HCC）患者与HCC的基因表达谱,识别与IRFA处理HCC相关的差异表达基因,并提供潜在治疗靶点。方法从GEO数据库中下载GSE212604数据集,根据是否接受IRFA治疗分为IRFA组和对照组,分析筛选出差异表达基因。利用功能通路富集分析（GO、KEGG）挖掘潜在的关键生物学功能和信号通路。同时下载GSE186280数据集,通过加权基因共表达网络分析（WGCNA）构建HCC的共表达基因模块,筛选关键模块中的核心基因。结果共鉴定出263个差异表达基因,其中上调基因179个,下调基因84个。GO和KEGG分析显示,这些基因主要富集于代谢重塑、细胞骨架调控、免疫调节和神经相关机制。此外,WGCNA分析发现与IRFA后HCC密切相关的基因模块,进一步筛选出15个核心基因,包括LOC112268313、RNA5-8SN3等。结论本研究通过生物信息学分析识别了与IRFA处理HCC相关的关键基因,表明免疫信号和p53、PPAR等通路可能在其发生和进展中发挥重要作用。这些关键基因和信号通路有望成为肝HCC治疗和诊断的新靶点。

关键词: 肝细胞癌, 不完全射频消融, 差异表达基因, 加权基因共表达网络分析

Abstract: Objective The aim of this study was to identify differentially expressed genes associated with IRFA-treated HCC and to provide potential therapeutic targets by comparing the gene expression profiles of hepatocellular carcinoma （HCC） patients after incomplete radiofrequency ablation （IRFA） with those of HCC. Methods The GSE212604 dataset was downloaded from the GEO database and divided into IRFA and control groups according to whether they received IRFA treatment or not, and analysed and screened for differentially expressed genes. Potential key biological functions and pathways were identified using functional pathway enrichment analysis （GO, KEGG）. We also downloaded the GSE186280 dataset and constructed the co-expressed gene modules of HCC by weighted gene co-expression network analysis （WGCNA） to screen the core genes in the key modules. Results A total of 263 differentially expressed genes were identified, including 179 up-regulated genes and 84 down-regulated genes, and GO and KEGG analyses showed that these genes were mainly enriched in metabolic remodelling, cytoskeletal regulation, immune regulation and neural-related mechanisms. In addition, WGCNA analysis identified gene modules closely related to HCC after IRFA, and further screened 15 core genes, including LOC112268313, RNA5-8SN3 and so on. Conclusion In this study, key genes associated with IRFA-treated HCC were identified by bioinformatics analysis, suggesting that immune signalling and pathways such as p53 and PPAR may play an important role in its occurrence and progression. These key genes and pathways are expected to be new targets for the treatment and diagnosis of HCC.

Key words: hepatocellular carcinoma, incomplete radiofrequency ablation, differentially expressed genes, weighted gene co-expression network analysis

中图分类号:

R735.7

张舟, 黄碧, 赵慧英. 基于GEO数据库的不完全射频消融后肝细胞癌关键基因的生物信息学研究[J]. 岭南现代临床外科, 2025, 25(01): 12-18.

ZHANG Zhou, HUANG Bi, ZHAO Hui-ying. Bioinformatics of key genes in hepatocellular carcinoma after incomplete radiofrequency ablation using the GEO database[J]. Lingnan Modern Clinics In Surgery, 2025, 25(01): 12-18.

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