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SYSTEMIC INTEGRATIVE ANALYSIS OF TUMORS ASSOCIATED WITH HUMAN ONCOVIRUS INFECTIONS

Grant number: 23/06086-6
Support Opportunities:Scholarships in Brazil - Master
Effective date (Start): June 01, 2023
Effective date (End): January 31, 2025
Field of knowledge:Biological Sciences - Immunology - Cellular Immunology
Principal Investigator:Otávio Cabral Marques
Grantee:Pedro Marçal Barcelos
Host Institution: Faculdade de Ciências Farmacêuticas (FCF). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:18/18886-9 - Systemic and integrative analysis of the immune response to Zika and Dengue viral infections, AP.JP

Abstract

Viruses are estimated to cause between 15% and 20% of all reported cancer cases. There areseven known human oncoviruses: Epstein-Barr Virus (EBV), Hepatitis B Virus (HBV), HumanPapillomavirus (HPV), Human T-Cell Leukemia Virus Type 1 (HTLV-1), Hepatitis C Virus(HCV ), Human Herpesvirus 8 (KSHV) and Merkel Cell Polyomavirus (MCV). These virusescan lead to uncontrolled cell growth and the development of cancer. The mechanisms by whichoncoviruses contribute to the cancer process may involve the activation of oncogenes and theinhibition of tumor suppressor genes, among others that may range from metabolic modulationto the control and evasion of the immune response. Therefore, due to these characteristics, it isbelieved that viruses participate in establishing tumors and their progression, essential factorsin the modulation of the tumor microenvironment (TME). However, although some studieshave managed to elucidate aspects of viral participation in the development and progression oftumors, an integrative systems approach is missing to holistically explain the influence ofviruses in tumor growth and development, potentially allowing the discovery of new targetsand/or therapeutic procedures. Therefore, this study aims to characterize consistently alteredbiological pathways in the tumor microenvironment of tumors of viral and non-viral etiology.Thus, a systemic integrative analysis (data meta-analysis using Fisher's method followed bybatch effect correction) of Datasets deposited in public databases (ArrayExpress, GeneExpression Omnibus (GEO) and Proteome Exchange) will be carried out to investigate thegenes and proteins that most impact tumor dynamics concerning their interaction networks(network and co-expression analysis) and correlations (bi- and multivariate). From these data,a ranking of altered genes and proteins (based on machine learning techniques such as RandomForrest) will be conducted to investigate predictors and possible therapeutic targets of greaterrelevance associated with the viral presence. Furthermore, this integrative analysis will allowus to identify common pathways, shared correlations, and co-expressed genes. The findings ofthis study will demonstrate the usefulness of systems biology in unraveling the complexinteractions between oncoviruses and the tumor microenvironment, providing valuable insightsinto the mechanisms underlying oncovirus infections and the development of more effectivetreatments.

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