Key biomarkers and latent pathways of dysferlinopathy: Bioinformatics analysis and in vivo validation - Xie, Yan; Li, Ying-hui; Chen, Kai; Zhu, Chun-yan; Bai, Jia-ying; Xiao, Feng; Tan, Song; Zeng, Li (Frontiers in Neurology, 2022)
Background: Dysferlinopathy refers to a group of muscle diseases with progressive muscle weakness and atrophy caused by pathogenic mutations of the DYSF gene. The pathogenesis remains unknown, and currently no specific treatment is available to alter the disease progression. This research aims to investigate important biomarkers and their latent biological pathways participating in dysferlinopathy and reveal the association with immune cell infiltration. Methods: GSE3307 and GSE109178 were obtained from the Gene Expression Omnibus (GEO) database. Based on weighted gene co-expression network analysis (WGCNA) and differential expression analysis, coupled with least absolute shrinkage and selection operator (LASSO), the key genes for dysferlinopathy were identified. Functional enrichment analysis Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were applied to disclose the hidden biological pathways. Following that, the key genes were approved for diagnostic accuracy of dysferlinopathy based on another dataset GSE109178, and quantitative real-time polymerase chain reaction (qRT-PCR) were executed to confirm their expression. Furthermore, the 28 immune cell abundance patterns in dysferlinopathy were determined with single-sample GSEA (ssGSEA). Results: 1,579 differentially expressed genes (DEGs) were screened out. Based on WGCNA, three co-expression modules were obtained, with the MEskyblue module most strongly correlated with dysferlinopathy. 44 intersecting genes were recognized from the DEGs and the MEskyblue module. The six key genes MVP, GRN, ERP29, RNF128, NFYB and KPNA3 were discovered through LASSO analysis and experimentally verified later. In a receiver operating characteristic analysis (ROC) curve, the six hub genes were shown to be highly valuable for diagnostic purposes. Furthermore, functional enrichment analysis highlighted that these genes were enriched mainly along the ubiquitin-proteasome pathway (UPP). Ultimately, ssGSEA showed a significant immune-cell infiltrative microenvironment in dysferlinopathy patients, especially T cell, macrophage, and activated dendritic cell (DC). Conclusion: Six key genes are identified in dysferlinopathy with a bioinformatic approach used for the first time. The key genes are believed to be involved in protein degradation pathways and the activation of muscular inflammation. And several immune cells, such as T cell, macrophage and DC, are considered to be implicated in the progression of dysferlinopathy.