Network analysis of differentially expressed smoking-associated mRNAs, lncRNAs and miRNAs reveals key regulators in smoking-associated lung cancer

Abstract

Lung cancer is the leading cause of cancer-associated mortality worldwide. Smoking is one of the most significant etiological contributors to lung cancer development. However, the molecular mechanisms underlying smoking-induced induction and progression of lung cancer have remained to be fully elucidated. Furthermore, long non-coding RNAs (lncRNAs) are increasingly recognized to have important roles in diverse biological processes. The present study focused on identifying differentially expressed mRNAs, lncRNAs and micro (mi)RNAs in smoking-associated lung cancer. Smoking-associated co-expression networks and protein-protein interaction (PPI) networks were constructed to identify hub lncRNAs and genes in smoking-associated lung cancer. Fur t her more, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of differentially expressed lncRNAs were performed. A total of 314 mRNAs, 24 lncRNAs and 4 miRNAs were identified to be deregulated in smoking-associated lung cancer. PPI network analysis identified 20 hub genes in smoking- associated lung cancer, including dynein axonemal heavy chain 7, dynein cytoplasmic 2 heavy chain 1, WD repeat domain 78, collagen type III α 1 chain (COL3A1), COL1A1 and COL1A2. Furthermore, co-expression network analysis indicated that relaxin family peptide receptor 1, receptor activity modifying protein 2-antisense RNA 1, long intergenic non-protein coding RNA 312 (LINC00312) and LINC00472 were key lncRNAs in smoking-associated lung cancer. A bioinformatics analysis indicated these smoking-associated lncRNAs have a role in various processes and pathways, including cell proliferation and the cyclic guanosine monophosphate cGMP)/protein kinase cGMP-dependent 1 signaling pathway. Of note, these hub genes and lncRNAs were identified to be associated with the prognosis of lung cancer patients. In conclusion, the present study provides useful information for further exploring the diagnostic and prognostic value of the potential candidate biomarkers, as well as their utility as drug targets for smoking-associated lung cancer.