Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms
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 an...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:77a9c2b93d9c4ab79865cb09185340292021-11-16T06:50:32ZIdentification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms1664-802110.3389/fgene.2021.755245https://doaj.org/article/77a9c2b93d9c4ab79865cb09185340292021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.755245/fullhttps://doaj.org/toc/1664-8021 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 (p < 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.Haomin ZhangDi LuDi LuQinglun LiFengfeng LuJundong ZhangJundong ZhangZining WangZining WangXuechun LuJinliang WangFrontiers Media S.A.articleEGFR–mutant lung adenocarcinomaprognosisWGCNATCGAGEOGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021) |
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EGFR–mutant lung adenocarcinoma prognosis WGCNA TCGA GEO Genetics QH426-470 |
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EGFR–mutant lung adenocarcinoma prognosis WGCNA TCGA GEO Genetics QH426-470 Haomin Zhang Di Lu Di Lu Qinglun Li Fengfeng Lu Jundong Zhang Jundong Zhang Zining Wang Zining Wang Xuechun Lu Jinliang Wang Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms |
| description |
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 (p < 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. |
| format |
article |
| author |
Haomin Zhang Di Lu Di Lu Qinglun Li Fengfeng Lu Jundong Zhang Jundong Zhang Zining Wang Zining Wang Xuechun Lu Jinliang Wang |
| author_facet |
Haomin Zhang Di Lu Di Lu Qinglun Li Fengfeng Lu Jundong Zhang Jundong Zhang Zining Wang Zining Wang Xuechun Lu Jinliang Wang |
| author_sort |
Haomin Zhang |
| title |
Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms |
| title_short |
Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms |
| title_full |
Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms |
| title_fullStr |
Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms |
| title_full_unstemmed |
Identification of Six Prognostic Genes in EGFR–Mutant Lung Adenocarcinoma Using Structure Network Algorithms |
| title_sort |
identification of six prognostic genes in egfr–mutant lung adenocarcinoma using structure network algorithms |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/77a9c2b93d9c4ab79865cb0918534029 |
| work_keys_str_mv |
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