Identification and molecular modelling of potential drugs targeting the genes involved in the progression of lung cancer in patients with idiopathic pulmonary fibrosis

Background: Idiopathic pulmonary fibrosis (IPF) is characterized by progressive fibrosis in the lung parenchyma. Given the fact that IPF patients are at significant risk of developing lung cancer (LC), the overlapping gene signatures between IPF and LC need to be explored. Methods: Two datasets (GSE79544 and GSE103888) were procured from the Gene Expression Omnibus repository and used to determine the overlapping genes between IPF and LC. Next, the prediction ability of these genes in differentiating the diseased group from controls was explored using two machine learning (ML) models (random forest and k-nearest neighbor). Potential drugs targeting the candidate genes were identified, and advanced structural analysis was conducted to determine the binding affinity between the candidate drug and target receptor. Result: A total of ten common genes (CCL13, CXCL2, MALT1, MARCKS, PLA2G7, SEMA6B, SFTPB, SPARC, SPP1, and TLCD2) are differentially expressed in IPF and LC as compared to the controls. PLA2G7 demonstrated promising potential in differentiating between IPF, LC, and controls. The increased expression correlated with poor survival in patients with LC. The expression of PLA2G7 indicated a similar trend in the validation dataset. Darapladib, a selective inhibitor that belongs to toxicity class 4 and lethal dose50 value of 800 mg/kg exhibited maximum potential in targeting PLA2G7 with a binding affinity score of -9.2 kcal/mol (chain A) and -9.3 kcal/ mol (chain B), respectively. Conclusion: The present study is the first of its kind that combines in-silico and ML algorithms to identify the gene signatures and promising drugs for treating the progression of LC in patients with IPF.