Exploring the crosstalk mechanism and immune relationship between SARS-CoV-2 infection and IgA nephropathy based on comprehensive bioinformatics analysis and machine learning

Abstract

Background: COVID-19 is a disease caused by SARS CoV-2 infection and has caused a global pandemic. Currently, it remains a threat to global human health. IgA nephropathy (IgAN) is the main pathological type of renal injury in patients with COVID-19, and leads to poor prognosis. However, the correlation and pathogenesis between COVID-19 and IgAN are not completely clear. The purpose of this study is to identify the crosstalk gene between COVID-19 and IgAN, and to preliminarily explore its clinical value and molecular mechanism. Methods: We obtained the COVID-19 and IgAN expression profile datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) analysis was applied to identify the crosstalk DEGs (CDEGs). Two machine learning methods (LASSO regression and SVM-RFE) were used to further screen for common diagnostic biomarkers of these two diseases from CDEGs. Furthermore, we conducted GO and KEGG pathway analysis, protein-protein interaction (PPI) network construction, hub gene identification, immune microenvironment analysis, and candidate traditional Chinese medicine herbs and ingredients prediction on CDEGs. Result: A total of 61 CDEGs were identified by the intersection of DEGs between COVID-19 and IgAN cohorts. CDEGs were mainly involved in many infectious disease pathways and cytokine-cytokine receptor interaction pathways. Functional enrichment showed that IL-1B involved six pathways, which may be a key gene in the common mechanism of COVID-19 and IgAN. After screening through machine learning, we found that TOP2A was the best shared diagnostic biomarker for COVID-19 and IgAN. Hub genes, as well as immune-related genes, and transcription factors of hub genes, were screened out. The results of immune infiltration showed that CDEGs are highly correlated with multiple infiltrating immune cells. Based on the CDEGs, cluster analysis was used to respectively regroup COVID-19 and IgAN samples into two clusters. 10 potential traditional Chinese medicine herbs and ingredients for treating COVID-19 related IgAN were proposed. Conclusion: In our study, we reveal the molecular network and signal pathway connecting COVID-19 and IgAN. Furthermore, we have screened the optimal diagnostic biomarkers for COVID-19 and IgAN. Our findings reveal a close relationship between CDEGs and the immune microenvironment of diseases. This provides clues for further exploring the common mechanism and interaction of these two diseases, and may provide new targets and drugs for intervention of COVID-19 related IgAN.