Identifying Tumorigenesis and Prognosis-Related Genes of Lung Adenocarcinoma: Based on Weighted Gene Coexpression Network Analysis

BioMed Research International
Ming YiKongming Wu

Abstract

Lung adenocarcinoma is the most frequently diagnosed subtype of nonsmall cell lung cancer. The molecular mechanisms of the initiation and progression of lung adenocarcinoma remain to be further determined. This study aimed to screen genes related to the progression of lung adenocarcinoma. By weighted gene coexpression network analysis (WGCNA), we constructed a free-scale gene coexpression network to evaluate the correlations between multiple gene sets and patients' clinical traits, then further identify predictive biomarkers. GSE11969 was obtained from the Gene Expression Omnibus (GEO) database which contained the gene expression data of 90 lung adenocarcinoma patients. Data of the Cancer Genome Atlas (TCGA) were employed as the validation cohort. After the average linkage hierarchical clustering, a total of 9 modules were generated. In the clinical significant module (R = 0.44, P < 0.0001), we identified 29 network hub genes. Subsequent verification in the TCGA database showed that 11 hub genes (ANLN, CDCA5, FLJ21924, LMNB1, MAD2L1, RACGAP1, RFC4, SNRPD1, TOP2A, TTK, and ZWINT) were significantly associated with poor survival data of lung adenocarcinomas. Besides, the results of receiver operating characteristic curves indicat...Continue Reading

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Citations

Jan 15, 2021·Infection, Genetics and Evolution : Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases·Komi NambouManawa Anakpa
Jan 31, 2021·Animals : an Open Access Journal From MDPI·Yongliang FanZhangping Yang
Apr 30, 2021·Evolutionary Bioinformatics Online·Xuan LuoMengNa Wu

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Datasets Mentioned

BETA
GPL7015
GSE11969

Methods Mentioned

BETA
Profiler
GTPase

Software Mentioned

Cytoscape
Ensembl
Profiler
WGCNA
LOWESS
Kaplan Plotter
: Profiler
IPASS
R

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