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 indicated that the mRNA levels of this group of genes exhibited high specificity and sensitivity to distinguish malignant lesions from nonmalignant tissues. Apart from mRNA levels, we found that the protein abundances of these 11 genes were remarkably upregulated in lung adenocarcinomas compared with normal tissues. In conclusion, by the WGCNA method, a panel of 11 genes were identified as predictive biomarkers for tumorigenesis and poor prognosis of lung adenocarcinomas.
Highlights
Lung cancer is the leading cause of cancer-related deaths all over the world and more than 80% of lung cancers are diagnosed as nonsmall cell lung cancers (NSCLCs) [1, 2]
A growing body of evidence indicates that gene alterations accumulate during the development of lung adenocarcinoma (LUAD), a proportion of LUADs are primarily driven by single gene alterations such as epidermal growth factor receptor (EGFR) mutation and ALK or ROS1 rearrangement, which are known as driven genes [5,6,7,8]
E brown module had the highest correlation with tumor differentiation (R 0.44, P < 0.0001) (Figure 4). erefore, the brown module was identified as the one with clinical significance, which was used for the following analysis
Summary
Lung cancer is the leading cause of cancer-related deaths all over the world and more than 80% of lung cancers are diagnosed as nonsmall cell lung cancers (NSCLCs) [1, 2]. As the most common subtype of NSCLC, the incidence of lung adenocarcinoma (LUAD) is increasing year by year [3]. The standard care for advanced LUAD was cytotoxic chemotherapy-involved comprehensive treatment. Due to the deeper understanding of genomics and tumorigenesis-associated molecular pathways, molecularly targeted therapies have been developed and a number of LUAD patients with these specific gene alterations could benefit from these regimens [4]. A growing body of evidence indicates that gene alterations accumulate during the development of LUADs, a proportion of LUADs are primarily driven by single gene alterations such as epidermal growth factor receptor (EGFR) mutation and (anaplastic lymphoma kinase) ALK or ROS1 rearrangement, which are known as driven genes [5,6,7,8].
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