Mitochondrial-related hub genes in dermatomyositis: muscle and skin datasets-based identification and in vivo validation

Authors

Shuo Wang
Yiping Tang
Xixi Chen
Siyuan Song
Xi Chen
Qiao Zhou
Li Zeng

Publication Date

1-1-2024

Journal

Frontiers in Genetics

DOI

10.3389/fgene.2024.1325035

PMID

38389573

PMCID

PMC10882082

PubMedCentral® Posted Date

2-8-2024

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

dermatomyositis, mitochondria, bioinformatic analysis, IFI27, CMPK2, LAP3, immune landscape

Abstract

Background: Mitochondrial dysfunction has been implicated in the pathogenesis of dermatomyositis (DM), a rare autoimmune disease affecting the skin and muscles. However, the genetic basis underlying dysfunctional mitochondria and the development of DM remains incomplete.

Methods: The datasets of DM muscle and skin tissues were retrieved from the Gene Expression Omnibus database. The mitochondrial related genes (MRGs) were retrieved from MitoCarta. DM-related modules in muscle and skin tissues were identified with the analysis of weighted gene co-expression network (WGCNA), and then compared with the MRGs to obtain the overlapping mitochondrial related module genes (mito-MGs). Subsequently, differential expression genes (DEGs) obtained from muscle and skin datasets were overlapped with MRGs to identify mitochondrial related DEGs (mito-DEGs). Next, functional enrichment analysis was applied to analyze possible relevant biological pathways. We used the Jvenn online tool to intersect mito-MGs with mito-DEGs to identify hub genes and validate them using reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry staining. In addition, we evaluated immune infiltration in muscle and skin tissues of DM patients using the one-sample gene set enrichment analysis (ssGSEA) algorithm and predicted potential transcription factor (TF) -gene network by NetworkAnalyst.

Results: The WGCNA analysis revealed 105 mito-MGs, while the DEG analysis identified 3 mito-DEGs. These genes showed functional enrichment for amino acid metabolism, energy metabolism and oxidative phosphorylation. Through the intersection analysis of the mito-MGs from the WGCNA analysis and the mito-DEGs from the DEG set, three DM mito-hub genes (IFI27, CMPK2, and LAP3) were identified and validated by RT-qPCR and immunohistochemistry analysis. Additionally, positive correlations were observed between hub genes and immune cell abundance. The TF-hub gene regulatory network revealed significant interactions involving ERG, VDR, and ZFX with CMPK2 and LAP3, as well as SOX2 with LAP3 and IFI27, and AR with IFI27 and CMPK2.

Conclusion: The mito-hub genes (IFI27, CMPK2, and LAP3) are identified in both muscles and skin tissues from DM patients. These genes may be associated with immune infiltration in DM, providing a new entry point for the pathogenesis of DM.