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使用结构网络算法鉴定表皮生长因子受体（EGFR）突变型肺腺癌中的六个预后基因

Identification of Six Prognostic Genes in EGFR-Mutant Lung Adenocarcinoma Using Structure Network Algorithms.

作者信息

Zhang Haomin, Lu Di, Li Qinglun, Lu Fengfeng, Zhang Jundong, Wang Zining, Lu Xuechun, Wang Jinliang

机构信息

Department of Hematology, The Second Medical Center, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Disease, Beijing, China.

Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.

出版信息

Front Genet. 2021 Nov 16;12:755245. doi: 10.3389/fgene.2021.755245. eCollection 2021.

DOI:10.3389/fgene.2021.755245
PMID:34868228
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8635158/
Abstract

This study aims to determine hub genes related to the incidence and prognosis of EGFR-mutant (MT) lung adenocarcinoma (LUAD) with weighted gene coexpression network analysis (WGCNA). From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we used 253 EGFR-MT LUAD samples and 38 normal lung tissue samples. At the same time, GSE19188 was additionally included to verify the accuracy of the predicted gene. To discover differentially expressed genes (DEGs), the R package "limma" was used. The R packages "WGCNA" and "survival" were used to perform WGCNA and survival analyses, respectively. The functional analysis was carried out with the R package "clusterProfiler." In total, 1450 EGFR-MT-specific DEGs were found, and 7 tumor-related modules were marked with WGCNA. We found 6 hub genes in DEGs that overlapped with the tumor-related modules, and the overexpression level of B3GNT3 was significantly associated with the worse OS (overall survival) of the EGFR-MT LUAD patients ( < 0.05). Functional analysis of the hub genes showed the metabolism and protein synthesis-related terms added value. In conclusion, we used WGCNA to identify hub genes in the development of EGFR-MT LUAD. The established prognostic factors could be used as clinical biomarkers. To confirm the mechanism of those genes in EGFR-MT LUAD, further molecular research is required.

摘要

本研究旨在通过加权基因共表达网络分析（WGCNA）确定与表皮生长因子受体突变（MT）肺腺癌（LUAD）的发生及预后相关的枢纽基因。我们从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）中获取了253个EGFR-MT LUAD样本和38个正常肺组织样本。同时，额外纳入GSE19188以验证预测基因的准确性。使用R包“limma”来发现差异表达基因（DEG）。分别使用R包“WGCNA”和“survival”进行WGCNA和生存分析。利用R包“clusterProfiler”进行功能分析。共发现1450个EGFR-MT特异性DEG，并用WGCNA标记了7个肿瘤相关模块。我们在与肿瘤相关模块重叠的DEG中发现了6个枢纽基因，B3GNT3的过表达水平与EGFR-MT LUAD患者较差的总生存期（OS）显著相关（<0.05）。对枢纽基因的功能分析显示，代谢和蛋白质合成相关术语具有附加价值。总之，我们利用WGCNA鉴定了EGFR-MT LUAD发生过程中的枢纽基因。所建立的预后因素可作为临床生物标志物。为了证实这些基因在EGFR-MT LUAD中的作用机制，还需要进一步的分子研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b94/8635158/d5b839fcd397/fgene-12-755245-g007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b94/8635158/d5b839fcd397/fgene-12-755245-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b94/8635158/c9b56d15f746/fgene-12-755245-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b94/8635158/199aaa5b0497/fgene-12-755245-g002.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b94/8635158/d5b839fcd397/fgene-12-755245-g007.jpg

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