IN-SILICO STUDY BASED EVIDENCE OF MICROBE DRIVEN MODULATION OF CANCER PROGNOSIS

Abstract

Cancer has been a major cause of disease burden on humanity for eons with one-fifth of all instances of cancer being connected with microbial prevalence and dysbiosis. There has been an inrush of instances asserting the dual character of human microbiota in maintaining homeostasis and its association with various ailments, including cancer. The diverse microbiota inhabiting the gut constitute a complicated symbiotic relationship conferring benefits to the organisms’ body in numerous ways, such as aiding metabolism, immunity, and nutrition. The microbiome has been seen to be affected by behavior, central nervous system & cardiovascular physiology, dysbiosis, innate & adaptive immunity, and diet & environment. Disturbances in regulatory pathways mainly responsible for guarding homeostasis as well as microbial dysbiosis lead to disease development. Pathogens that are specifically associated with cancer do not work in solitude, rather, it’s an association of microbes that have a cumulative impact on immune function and genome stability. Accumulation of certain bacteria promotes persistent inflammation, genetic alterations in principal inflammation-modulating genes which in turn elevate dysbiosis and thus cancer.

Tumor antigens that are present on the cancer cell surface like MUC16 and mesothelin show high- affinity binding towards each other and have been attributed to increasing the metastasis and

migration capabilities of cancerous cells. In numerous instances of this morbidity certain bacteria have been found to be closely associated in solid tumors as well as in surrounding normal tissues. The new age of bioinformatics has revolutionized the science of omics and vaccinology by aiding high-speed in-silico protein structure determination & epitope identification using fast and precise

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tools. In the present study, we have used the immunoinformatics strategy to identify and model a microbial peptide in one such cancer-associated microbe which shows close homology with tumor antigen CA125 which is aberrantly overexpressed in ovarian cancer cells among various other diseases. The present study is based on a methodology which utilizes various bioinformatics tools which are online as well as offline. The major techniques employed in the project are homology modelling, protein-protein docking, and epitope prediction aided by visualization tools. Our results exhibit a novel epitope-containing microbial peptide present in a cancer-associated bacteria that shows binding to mesothelin through molecular docking studies, which could possibly hinder its extensive binding to CA125 and therefore putatively alter the disease prognosis.