CXCL8 and COVID-19: Figure 6 shows the experiments of the SP-LIME algorithm with the random forest classifier, presenting the genes with the highest impact on model prediction. For example, PGLYRP4 and ANXA10 are the most critical genes in the classification process. Interestingly, some feature genes were screened out based on the machine learning algorithm, including IRF9, IFI6, OAS1, PARP9, PGLYRP4, LIF, HEPHL1, and IL8, which were overlapped with identified hub gene candidates, highlighting the potential involvement of these hub genes during COVID-19 pathogenesis.