Identifying key genes related to inflammasome in severe COVID-19 patients based on a joint model with random forest and artificial neural network

BACKGROUND: The coronavirus disease 2019 (COVID-19) has been spreading astonishingly and caused catastrophic losses worldwide. The high mortality of severe COVID-19 patients is an serious problem that needs to be solved urgently. However, the biomarkers and fundamental pathological mechanisms of severe COVID-19 are poorly understood. The aims of this study was to explore key genes related to inflammasome in severe COVID-19 and their potential molecular mechanisms using random forest and artificial neural network modeling. METHODS: Differentially expressed genes (DEGs) in severe COVID-19 were screened from GSE151764 and GSE183533 via comprehensive transcriptome Meta-analysis. Protein-protein interaction (PPI) networks and functional analyses were conducted to identify molecular mechanisms related to DEGs or DEGs associated with inflammasome (IADEGs), respectively. Five the most important IADEGs in severe COVID-19 were explored using random forest. Then, we put these five IADEGs into an artificial neural network to construct a novel diagnostic model for severe COVID-19 and verified its diagnostic efficacy in GSE205099. RESULTS: Using combining P value < 0.05, we obtained 192 DEGs, 40 of which are IADEGs. The GO enrichment analysis results indicated that 192 DEGs were mainly involved in T cell activation, MHC protein complex and immune receptor activity. The KEGG enrichment analysis results indicated that 192 GEGs were mainly involved in Th17 cell differentiation, IL-17 signaling pathway, mTOR signaling pathway and NOD-like receptor signaling pathway. In addition, the top GO terms of 40 IADEGs were involved in T cell activation, immune response-activating signal transduction, external side of plasma membrane and phosphatase binding. The KEGG enrichment analysis results indicated that IADEGs were mainly involved in FoxO signaling pathway, Toll-like receptor, JAK-STAT signaling pathway and Apoptosis. Then, five important IADEGs (AXL, MKI67, CDKN3, BCL2 and PTGS2) for severe COVID-19 were screened by random forest analysis. By building an artificial neural network model, we found that the AUC values of 5 important IADEGs were 0.972 and 0.844 in the train group (GSE151764 and GSE183533) and test group (GSE205099), respectively. CONCLUSION: The five genes related to inflammasome, including AXL, MKI67, CDKN3, BCL2 and PTGS2, are important for severe COVID-19 patients, and these molecules are related to the activation of NLRP3 inflammasome. Furthermore, AXL, MKI67, CDKN3, BCL2 and PTGS2 as a marker combination could be used as potential markers to identify severe COVID-19 patients.