Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis.

Abstract

Simple SummaryNon-small cell lung cancer (NSCLC) is a major contributor to cancer related deaths worldwide. The progression of NSCLC is linked to epithelial-mesenchymal transition (EMT), a biologic process that enables tumor cells to acquire an invasive phenotype and resistance to therapies. Discovery of novel biomarkers in NSCLC progression is essential for improved prognosis and pharmacological interventions. We performed an integrated bioinformatics analysis on available gene expression datasets of transforming growth factor β (TGF-β) induced EMT in NSCLC cell lines aiming to establish new prognostic biomarkers in the disease. The retrieved candidate genes were involved in protein modifications, regulation of cell death and cell adhesions, oxidation-reduction reactions of aerobic respiration and mitochondrial translation. Out of these genes, we identified ten prognostic gene biomarkers, mostly involved in protein modifications, whose expressions correlated with patient survival in NSCLC. This ten-gene prognostic signature will be useful to improve risk prediction and guide treatment strategies in NSCLC. Deciphering the exact functions of the biomarker genes previously not linked with NSCLC will also lead to a better understanding of the pathomechanism of NSCLC progression, revealing novel therapeutic targets in the disease.The progression of non-small cell lung cancer (NSCLC) is linked to epithelial-mesenchymal transition (EMT), a biologic process that enables tumor cells to acquire a migratory phenotype and resistance to chemo- and immunotherapies. Discovery of novel biomarkers in NSCLC progression is essential for improved prognosis and pharmacological interventions. In the current study, we performed an integrated bioinformatics analysis on gene expression datasets of TGF-β-induced EMT in NSCLC cells to identify novel gene biomarkers and elucidate their regulation in NSCLC progression. The gene expression datasets were extracted from the NCBI Gene Expression Omnibus repository, and differentially expressed genes (DEGs) between TGF-β-treated and untreated NSCLC cells were retrieved. A protein-protein interaction network was constructed and hub genes were identified. Functional and pathway enrichment analyses were conducted on module DEGs, and a correlation between the expression levels of module genes and survival of NSCLC patients was evaluated. Prediction of interactions of the biomarker genes with transcription factors and miRNAs was also carried out. We described four protein clusters in which DEGs were associated with ubiquitination (Module 1), regulation of cell death and cell adhesions (Module 2), oxidation-reduction reactions of aerobic respiration (Module 3) and mitochondrial translation (Module 4). From the module genes, we identified ten prognostic gene biomarkers in NSCLC. Low expression levels of KCTD6, KBTBD7, LMO7, SPSB2, RNF19A, FOXA2, DHTKD1, CDH1 and PDHB and high expression level of KLHL25 were associated with reduced overall survival of NSCLC patients. Most of these biomarker genes were involved in protein ubiquitination. The regulatory network of the gene biomarkers revealed their interaction with tumor suppressor miRNAs and transcription factors involved in the mechanisms of cancer progression. This ten-gene prognostic signature can be useful to improve risk prediction and therapeutic strategies in NSCLC. Our analysis also highlights the importance of deregulation of ubiquitination in EMT-associated NSCLC progression.