Comprehensive transcriptomic analysis and machine learning reveal unique gene expression profiles in patients with immune‐mediated necrotizing myopathy

Author:

Liu Hongjiang1ORCID,Deng Lin2,Guo Yixue3,Liu Huan1,Chen Bo1,Zhang Jiaqian1,Ran Jingjing1,Yin Geng4,Xie Qibing1

Affiliation:

1. Department of Rheumatology and Immunology, West China Hospital Sichuan University Chengdu China

2. National Key Laboratory of Fundamental Science on Synthetic Vision Sichuan University Chengdu China

3. Department of Laboratory Medicine, West China Hospital Sichuan University Chengdu China

4. Health Management Center, General Practice Medical Center, West China Hospital Sichuan University Chengdu China

Abstract

AbstractBackgroundImmune‐mediated necrotizing myopathy (IMNM) is an autoimmune myopathy characterized by severe proximal weakness and muscle fiber necrosis, yet its pathogenesis remains unclear. So far, there are few bioinformatics studies on underlying pathogenic genes and infiltrating immune cell profiles of IMNM. Therefore, we aimed to characterize differentially expressed genes (DEGs) and infiltrating cells in IMNM muscle biopsy specimens, which may be useful for elucidating the pathogenesis of IMNM.MethodsThree datasets (GSE39454, GSE48280 and GSE128470) of gene expression profiling related to IMNM were obtained from the Gene Expression Omnibus database. Data were normalized, and DEG analysis was performed using the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed using clusterProfiler. The CIBERSORT algorithm was performed to identify infiltrating cells. Machine learning algorithm and gene set enrichment analysis (GSEA) were performed to find distinctive gene signatures and the underlying signaling pathways of IMNM.ResultsDEG analysis identified upregulated and downregulated in IMNM muscle compared to the gene expression levels of other groups. GO and KEGG analysis showed that the pathogenesis of IMNM was notable for the under‐representation of pathways that were important in dermatomyositis and inclusion body myositis. Three immune cells (M2 macrophages, resting dendritic cells and resting natural killer cells) with differential infiltration and five key genes (NDUFAF7, POLR2J, CD99, ARF5 and SKAP2) in patients with IMNM were identified through the CIBERSORT and machine learning algorithm. The GSEA results revealed that the key genes were remarkably enriched in diverse immunological and muscle metabolism‐related pathways.ConclusionsWe comprehensively explored immunological landscape of IMNM, which is indicative for the research of IMNM pathogenesis.

Funder

Fundamental Research Funds for the Central Universities

Sichuan Province Science and Technology Support Program

Publisher

Wiley

Subject

Genetics (clinical),Drug Discovery,Genetics,Molecular Biology,Molecular Medicine

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