Jianwei Guo,1,2,* Tianyi Lei,1,3,* Xiang Yu,1,3 Peng Wang,1,2 Hongyuan Xie,1,3 Guilin Jian,1,4 Quanbo Zhang,1,2,* Yufeng Qing1,3 

1Research Center of Hyperuricemia and Gout, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, People’s Republic of China; 2Department of Geriatrics, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, People’s Republic of China; 3Department of Rheumatology and Immunology, Affiliated Hospital of North Sichuan Medical College, Nanchong, 637000, People’s Republic of China; 4Emergency Department,Suining Third People, S Hospital, Suining, Sichuan, 629000, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Quanbo Zhang; Yufeng Qing, Email quanbozhang@126.com; qingyufengqq@163.com

Background: Dermatomyositis (DM) is an inflammatory muscle disease that increases the risk of cancer, although the precise connection is not fully understood. The aim of this study was to investigate the mechanisms linking DM to cancer and identify potential therapeutic targets.
Methods: We conducted differential gene expression analysis on the GSE128470 dataset and employed WGCNA to pinpoint key genes related to DM. Central genes were identified with the LASSO and SVM-RFE methods. The expression levels and diagnostic relevance of these genes were confirmed via the GSE1551 dataset. Immune cell infiltration was analyzed in relation to central genes, and RT‒qPCR was utilized to evaluate the expression of key genes across various cancers.
Results: In total, differentially expressed genes (DEGs), involved mainly in innate immunity, cytokine responses, and autoimmune diseases, were identified. In the WGCNA, 399 significant genes related to DM were identified, with central genes including MIF, C1QA, and CDKN1A. Immune infiltration analysis revealed diverse immune cell populations in DM patients, with significant correlations between central genes and these immune cells. MIF levels were notably elevated in various tumors and correlated with the prognosis of specific cancers. Furthermore, MIF was negatively associated with most immune cells but positively correlated with CD4+ Th1 cells, NKT cells, and MDSCs. Factors such as immune regulatory elements, TMB, and MSI indicated that MIF may affect immunotherapy outcomes. The increased expression of MIF mRNA was confirmed via RT‒qPCR.
Conclusion: The findings demonstrate that MIF, C1QA, and CDKN1A are differentially expressed in DM patients, with MIF showing significant alterations in DM patients with cancer. MIF may serve as a crucial prognostic biomarker and therapeutic target for various cancers, playing a pivotal role in linking DM to cancer through the modulation of CD4+ Th1 cells, NKT cells, and MDSCs.

Keywords: dermatomyositis, pan-cancer, immune infiltration, machine learning, WGCNA, MIF