Abstract
Computational Prediction for COVID-19 Risky Genes Associated with Lung Cancer
Lung Cancer is an uncontrolled division of faulty cells in the lungs, and it can spread to other organs. Lung Cancer is also the third most common cancer in the United States. Coronavirus Disease 2019, or COVID-19, is a virus that causes lung infection with Severe Acute Respiratory Syndrome (SARS). It has also been noticed that people with pre-existing medical conditions whose immune systems do not function correctly or do not function at all due to cancer treatment (e.g., chemotherapy and/or radiation) are prone to be infected with the COVID-19 virus and develop severe symptoms/complications. There are also few studies so far that have explored how genes associated with Lung Cancer could also serve as targets of COVID-19.
In this project, I used bioinformatics approaches in my project to study genes and their molecular mechanisms contributing to Lung Cancer and the COVID-19 disease. Specifically, I first calculated expressions for literature reported candidate genes associated with The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma (LUAD). Next, I conducted Protein-Protein Interaction (PPI) network analysis for top 30 candidate down-regulated genes, and I then performed functional annotation and pathway and survival analysis and identified conserved domains. Finally, I cross-checked the SARS-CoV-2 infection studies and literatures and pinpointed 4 surfactant genes that could serve as potential biomarkers.
I hope my research can help scientists look for better treatment strategies on Lung Cancer patients developing any pulmonary problems from the Coronavirus Disease.
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