Computational Identification and Validation of Non-Synonymous SNPs in Progesterone Receptor Membrane Complex 1 Linked to Lung Cancer

Abstract

Numerous gene polymorphisms have been attributed to Lung cancer, but PGRMC1 (Progesterone receptor membrane component 1) is a lesser-known candidate among them. However, emerging research is slowly suggesting the role of polymorphisms in PGRMC1 gene-associated tumorigenesis. Nevertheless, phenotypic changes still need to be studied. The main aim of this study is to identify the most deleterious nsSNPs (non-synonymous single nucleotide polymorphisms) in PGRMC1 that can potentially increase the susceptibility to lung cancer progression. In this work, we scrutinized highly detrimental nsSNPs for PGRMC1 from the available dbSNP database. We further categorized using the FATHMM server to enlist the nsSNPs, which are driver mutants capable of affecting the function of the PGRMC1 protein. We employed clinical evidence from the COSMIC database for further evaluation and confirming the presence of nsSNPs in lung cancer patients. There are 12 nsSNPs reported in lung cancer patients, which are L32M, R47C, D141N, G20W, S57C, R70P, I89V, G118V, A191D, G95V, E157K, and G168V predicted to damage the functions. Conclusively, through a comprehensive comparison of the outcomes obtained through these computational methods, we identified novel I89V, D120E, G95V and G168V nsSNPs that pose substantial risks to the functionality of the PGRMC1 protein. Focusing on the importance of PGRMC1 in lung cancer, analyzing its function was conceded to unveil the interlink between genetic mutation and phenotypic changes. Thus, this study provides insights into the influence of PGRMC1variants in Lung cancer. The evaluated nsSNPs can significantly aid future research on the gene and its association with lung cancer progression in large population distribution frequencies of genotypes among different subgroups.