In silico prediction of COVID-19 cytokine storm in lung cancer types

利用计算机模拟预测肺癌类型中 COVID-19 细胞因子风暴

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Abstract

Lung cancer is one of the most frequently diagnosed malignant tumors and the leading cause of cancer-related death worldwide. Mainly, Non-small-cell lung cancer (NSCLC), which accounts for more than eighty-five percent of all lung cancers, consists of two major subtypes: lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Novel coronavirus disease (COVID-19) affected millions of people caused by acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) around the globe. Lung cancer patients and COVID-19 present unique and unfortunate lethal combinations because the lungs are the primary target organ of SARS-CoV-2 infection. Clinical studies have demonstrated that an over-activated inflammatory response associated with severe COVID-19 cases is characterized by excessive auto-amplifying cytokine release, which is defined as a "cytokine storm." ACE2 and TMPRSS2 receptors play an essential role in SARS-CoV-2 infection; therefore, using in silico analysis, we did correlation analysis with immune infiltration markers in LUAD and LUSC patient groups. Our study identified a promising correlation between immune-modulators and receptor proteins (ACE-2 and TMPRSS2), creating a domain that requires further laboratory studies for clinical authentication.

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