Insilico analysis of functional snps in human egfr gene associated with lung cancer
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Date
2022
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UMT, Lhr
Abstract
Lung cancer is the second most commonly diagnosed disease worldwide, accounting for 11.4 percent of the global cancer burden in 2020, with 2.3 million new cases
expected. It is one of the leading causes of cancer death with estimated 1.8 million (18%) deaths worldwide in 2020. Small cell lung cancer and non-small cell lung cancer are the two main types of lung cancer. Single nucleotide polymorphisms (SNPs) are thought to be responsible for about 90% of known mutations where some mutations are considered as neutral and some mutations cause modifications in translated gene functions and gene expression. Mutation in EGFR is one of the leading causes of lung cancer which results in uncontrolled cell proliferation. In this study, Insilico analysis of Non synonymous SNPs in EGFR gene/protein was performed to access their role prognosis by using various bioinformatics tools. The sequence of protein EGFR was retrieved from NCBI in FASTA format and its length consisted of 1210 amino acids. Missense variants of EGFR gene (926) were taken from Ensembl by using the dbSNP database. After that, many other characteristics were used to distinguish between deleterious and harmful missense SNPs and SNPs of unknown importance. As a consequence, 18 missense SNPs were analyzed using the available online tools. Bioinformatics tools were used for the prediction of missense SNPs which are sequence homology based method, supervised based learning method ,consensus and structure based methods. 14 SNPs were predicted as most deleterious by maximum tools. Structural analysis was done by the I-Tasser and PyMol. Validation of secondary structure was done by the GOR4 tool. A slight change in alpha helix, extended strand and random coil was observed between wild type and mutated type EGFR secondary structure. This study can help in better understanding of the lung cancer and involvement of SNPs in disease progression and mechanism under lying.