Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Abstract

Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies consisting essentially of adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Although the diagnosis and treatment of ADC and SCC have been greatly improved in recent decades, there is still an urgent need to identify accurate transcriptome profile associated with the histological subtypes of NSCLC. The present study aims to identify the key dysregulated pathways and genes involved in the development of lung ADC and SCC and to relate them with the clinical traits. The transcriptional changes between tumour and normal lung tissues were investigated by RNA-seq. Gene ontology (GO), canonical pathways analysis with the prediction of upstream regulators, and weighted gene co-expression network analysis (WGCNA) to identify co-expressed modules and hub genes were used to explore the biological functions of the identified dysregulated genes. It was indicated that specific gene signatures differed significantly between ADC and SCC related to the distinct pathways. Of identified modules, four and two modules were the most related to clinical features in ADC and SCC, respectively. CTLA4, MZB1, NIP7, and BUB1B in ADC, as well as GNG11 and CCNB2 in SCC, are novel top hub genes in modules associated with tumour size, SUVmax, and recurrence-free survival. Our research provides a more effective understanding of the importance of biological pathways and the relationships between major genes in NSCLC in the perspective of searching for new molecular targets.

Identification of the subtypes of gastric cancer based on DNA methylation and the prediction of prognosis

“…7 b, c). Previous studies showed that in lung squamous cell carcinoma GNG11 was a novel hub gene in module-related tumor size, and the low mRNA expression of GNG11 was associated with the higher overall survival rate for patients [ 39 , 40 ]. The secretion of PDGFB by gastric carcinoma cells was associated with lymphatic metastasis [ 41 ].…”

Weighted Gene Co-Expression Network Analysis to Construct Competitive Endogenous RNAs Network in Chromogenic Renal Cell Carcinoma

“…It is able to cluster highly related genes from microarray samples into different color modules and explore the relationship between the genes and cancer traits [20]. WGCNA has already been used in various oncological studies to explore hub genes and the regulatory relationships between them [21][22][23]. In our study, we preformed WGCNA to select highly related module genes, which helped us elucidate the more meaningful RNAs for further prediction.…”

CD247 Functions as a Prognostic Biomarker for Cutaneous Malignant Melanoma Based on the Analysis of Tumor-immune Microenvironment

“…WGCNA was conducted by adopting the R packages of "wgcna," matrixStats," dynamic tree cut," fast cluster," Hmisc" in R software [10]. Pearson's correlation matrices were prepared for all pair-wise genes.…”