Browsing by Author "SHER ALI"

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    Computational analysis of FAM83H gene in oral cancer and potential therapeutics
    (UMT, Lhr, 2025-07-03) SHER ALI
    Globally, 19.3 million new cancer cases and 10 million cancer-related deaths were reported in 2024. In Pakistan, approximately 148,000 new cases are diagnosed annually, resulting in around 10,0000 deaths. These numbers highlight the significant burden of cancer worldwide and in Pakistan. Oral cancer affects approximately 377,713 people worldwide each year, accounting for around 2-4% of all cancers globally. In Pakistan, it's a significant health issue, particularly among males, accounting for approximately 10% of all cancers due to prevalent risk factors like tobacco and betel nut use. The objective of this study is to investigate the role of Family with sequence similarity 83, member H (FAM83H) in oral cancer, to detect missense variants and change in structure and function of FAM83H gene and to check the potential therapeutics against oral cancer. For this purpose, 28 missense SNPs were selected from NCBI through ENSEMBLE and further used for in silico analysis. In-silico analysis of FAM83H were performed by using homology-based methods such as Sorting Intolerant from Tolerant (SIFT), Protein Variation Effect Analyzer (ProVean), Protein Analysis Through Evolutionary Relationships (PANTHER), consensus methods such as Single Nucleotide Polymorphism and Gene Ontology (SNP&GO), Predictor of human Deleterious Single Nucleotide Polymorphisms (PhD-SNP), Screening for Non-Acceptable Polymorphisms 2 (SNAP2), Mutation predictor 2 (Mutpred2), and supervised learning models such as Meta-Analysis of Single Nucleotide Polymorphism (MetaSNP), Polymorphism Phenotyping v2 (PolyPhen-2), Prediction Single Nucleotide Polymorphisms (Predict-SNP). A total of 28 nsSNPs were analyzed, in which 7 were predicted to be structurally and functionally damaging through homology based, while 3 were identified deleterious through supervised learning-based methods and 2 were identified deleterious through consensus- based approaches. There were two mutations that were common in all methods, and these mutations were at position 35 where Leucine is changed into Proline and at position 92 where Serine changed into Leucine. These two mutations were predicted to associated with oral cancer. Modeling with I-TASSER showed considerable changes in protein structure and amino acid changes in these two variants as, highlighting their possible involvement in oral cancer development. For further structural analysis different tools such as CHIMERA, SAVES have been used and after that to check their therapeutic targets different compounds such as Liquiritin, Naringenin, and Liquiritigenin are docked with normal and mutated structures of genes to check the interactions, and these teractions showed that these compounds have anticancer activities and can be used as anticancer drugs. These findings provide potential targets for genetic screening and therapeutic interventions for oral cancer.