RESUMO
OBJECTIVES: The COVID-19 pandemic caused by SARS-CoV-2 has seriously threatened the human health. Growing evidence shows that COVID-19 patients who recovery will persist with symptoms of fibromyalgia (FM). However, the common molecular mechanism between COVID-19 and FM remains unclear. METHODS: We obtained blood transcriptome data of COVID-19 (GSE177477) and FM (GSE67311) patients from GEO database, respectively. Subsequently, we applied Limma, GSEA, Wikipathway, KEGG, GO, and machine learning analysis to confirm the common pathogenesis between COVID-19 and FM, and screened key genes for the diagnosis of COVID-19 related FM. RESULTS: A total of 2505 differentially expressed genes (DEGs) were identified in the FM dataset. Functional enrichment analysis revealed that the occurrence of FM was intimately associated with viral infection. Moreover, WGCNA analysis identified 243 genes firmly associated with the pathological process of COVID-19. Subsequently, 50 common genes were screened between COVID-19 and FM, and functional enrichment analysis of these common genes primarily involved in immunerelated pathways. Among these common genes, 3 key genes were recognised by machine learning for the diagnosis of COVID-19 related FM. We also developed a diagnostic nomogram to predict the risk of FM occurrence which showed excellent predictive performance. Finally, we found that these 3 key genes were closely relevant to immune cells and screened potential drugs that interacted with the key genes. CONCLUSIONS: Our study revealed the bridge role of immune dysregulation between COVID-19 and fibromyalgia, and screened underlying biomarkers to provide new clues for further clinical research.
