Identification of Biomarkers Associated With CD4+ T-Cell Infiltration With Gene Coexpression Network in Dermatomyositis - Huang, Peng; Tang, Li; Zhang, Lu; Ren, Yi; Peng, Hong; Xiao, Yangyang; Xu, Jie; Mao, Dingan; Liu, Lingjuan; Liu, Liqun (Frontiers in Immunology, 2022)
Background: Dermatomyositis is an autoimmune disease featuring damage to the skin and muscles. So far, there are few bioinformatics studies related to latent pathogenic genes and immune cell infiltration of DM. CD4+ T cells are of crucial importance in the occurrence and development of DM. Therefore, this study intended to explore CD4+ T cells infiltration-associated key genes in DM and construct a new disease prediction model. Methods: Downloaded GSE46239 and GSE142807 datasets from GEO. WGCNA and CIBERSORT algorithm were performed to identify the most correlated gene module with CD4+ T cells. Matascape was used to GO enrichment and KEGG pathway analysis for the key gene module. Lasso-penalized Cox regression analysis was used to identify the key genes and construct the prediction model. The correlation between key genes and CD4+ T cells infiltration was investigated. GSEA was performed to research the underlying signaling pathways of key genes. Moreover, key genes-correlated transcription factors were recognized through RcisTarget and Gene-motif rankings databases. Based on GeneCards, analyzed the relationship between key genes and disease regulatory genes. Using miRcode and DIANA-LncBase database to build the lncRNA-miRNA-mRNA (ceRNA) network. Results: In the brown module, 5 key genes (C1orf106, COG8, EVPL, GIMAP6, and IFI6) highly associated with CD4+ T cells infiltration were identified, and the disease prediction model was constructed. The model shows a better predicting performance in the training set and validation set. The expression levels of key genes promoted the CD4+ T cells infiltration. GSEA results revealed that key genes were remarkably enriched on many immunity-associated pathways, for instance, antigen processing and presentation, JAK/STAT signaling pathway. The cisbp_M2205, transcription factors binding site, is enriched in C1orf106, EVPL, IF16. In addition, the correlation analysis of key genes and disease regulatory genes showed that COG8 was significantly negatively correlated with ISG15, and GIMAP6 was significantly positively correlated with IFIH1. Finally, 3,835 lncRNAs and 52 miRNAs significantly correlated with key genes were used to build a ceRNA network. Conclusion: The C1orf106, COG8, EVPL, GIMAP6, and IFI6 genes are associated with CD4+ T cells infiltration and could be recognized as potential biomarkers and immunotherapeutic targets of DM.