Abstract
PurposeThe molecular mechanism underlying the carcinogenesis and development of lung squamous cell carcinoma (LUSC) has not been sufficiently elucidated. This analysis was performed to find pivotal genes and explore their prognostic roles in LUSC.MethodsA microarray dataset from GEO (GSE19188) and a TCGA-LUSC dataset were used to identify differentially co-expressed genes through Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We conducted functional enrichment analyses of differentially co-expressed genes and established a protein-protein interaction (PPI) network. Then, we identified the top 10 hub genes using the Maximal Clique Centrality (MCC) algorithm. We performed overall survival (OS) analysis of these hub genes among LUSC cases. GSEA analyses of survival-related hub genes were conducted. Ultimately, the GEO and The Human Protein Atlas (THPA) databases and immunohistochemistry (IHC) results from the real world were used to verify our findings.ResultsA list of 576 differentially co-expressed genes were selected. Functional enrichment analysis indicated that regulation of vasculature development, cell−cell junctions, actin binding and PPAR signaling pathways were mainly enriched. The top 10 hub genes were selected according to the ranking of MCC scores, and 5 genes were closely correlated with OS of LUSC. Additionally, GSEA analysis showed that spliceosome and cell adhesion molecules were associated with the expression of GNG11 and ADCY4, respectively. The GSE30219 and THPA databases and IHC results from the real world indicated that although GNG11 was not detected, ADCY4 was obviously downregulated in LUSC tissues at the mRNA and protein levels.ConclusionsThis analysis showed that survival-related hub genes are highly correlated to the tumorigenesis and development of LUSC. Additionally, ADCY4 is a candidate therapeutic and prognostic biomarker of LUSC.
Highlights
Lung carcinoma is a common cancer, with nearly 228820 cancer patients and 135720 deaths in 2020, which places an enormous burden on patients and their families [1]
To detect the functional modules in lung squamous cell carcinoma (LUSC), we established two gene co-expression networks through the Weighed Gene Co-expression Network Analysis (WGCNA) package based on the GSE19188 and The Cancer Genome Atlas database (TCGA)-LUSC datasets, respectively
The two heatmaps explored the relationship between these modules and two clinical traits in the GSE19188 (Figure 2D) and TCGA-LUSC datasets (Figure 3D), suggesting that the turquoise module in GSE19188 and the turquoise module in the TCGA-LUSC dataset were highly correlated with normal lung tissues
Summary
Lung carcinoma is a common cancer, with nearly 228820 cancer patients and 135720 deaths in 2020, which places an enormous burden on patients and their families [1]. With the speedy development of genomic technology, researchers have analyzed gene expression profiles using bioinformatics approaches to explore the underlying molecular mechanisms of tumors and detect cancer-specific biomarkers [5]. Weighed Gene Co-expression Network Analysis (WGCNA) is an important algorithm to understand gene co-expression networks and gene functions [6]. Using WGCNA, we can detect the modules of closely correlated genes related to the traits of samples, which will provide insights to predict probable functions of co-expression genes [7]. Differential gene expression analysis is usually applied for the analysis of transcriptomics datasets, which is beneficial to explore underlying biological and molecular mechanisms of cancers and detect quantitative differences between the gene expression levels of intervention and control cohorts [8]
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