Yang Wenqu, Liang Feng
Department of Anesthesiology, Shanxi Bethune Hospital, China.
Department of Neurology, The First Hospital of Tsinghua University, China.
Heliyon. 2025 Jan 1;11(1):e41442. doi: 10.1016/j.heliyon.2024.e41442. eCollection 2025 Jan 15.
Myasthenia gravis (MG) and idiopathic inflammatory myopathies (IIM) are autoimmune disorders that can co-occur, complicating diagnosis and treatment. The molecular mechanisms underlying this comorbidity are not well understood.
This study aims to identify common differentially expressed genes (co-DEGs) between MG and IIM to elucidate shared pathogenic pathways and potential therapeutic targets.
Transcriptomic data from the Gene Expression Omnibus (GEO) were analyzed using the "limma" package in RStudio. Functional enrichment analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. A nomogram prediction model was developed, and receiver operating characteristic (ROC) analysis was used to evaluate its diagnostic potential.
Four co-DEGs were identified between MG and IIM, associated with neurotransmitter transport and ion channel regulation. The nomogram model, incorporating three of these co-DEGs, showed high predictive accuracy for MG with IIM complications, with an area under the ROC curve of 0.94. Immune infiltration analysis revealed distinct patterns in MG and IIM, particularly involving gamma delta T cells and activated mast cells.
The study identifies key genetic intersections between MG and IIM, providing insights into their shared pathogenesis and highlighting potential diagnostic and therapeutic targets. Further experimental validation is required to confirm these findings.
重症肌无力(MG)和特发性炎性肌病(IIM)是可能同时出现的自身免疫性疾病,这使得诊断和治疗变得复杂。这种共病的分子机制尚不清楚。
本研究旨在识别MG和IIM之间的共同差异表达基因(co-DEGs),以阐明共同的致病途径和潜在的治疗靶点。
使用RStudio中的“limma”软件包分析来自基因表达综合数据库(GEO)的转录组数据。利用基因本体论(GO)和京都基因与基因组百科全书(KEGG)通路进行功能富集分析。开发了列线图预测模型,并使用受试者工作特征(ROC)分析来评估其诊断潜力。
在MG和IIM之间鉴定出四个co-DEGs,与神经递质转运和离子通道调节有关。包含其中三个co-DEGs的列线图模型对伴有IIM并发症的MG显示出较高的预测准确性,ROC曲线下面积为0.94。免疫浸润分析揭示了MG和IIM中的不同模式,特别是涉及γδT细胞和活化肥大细胞。
该研究确定了MG和IIM之间的关键基因交叉点,为它们的共同发病机制提供了见解,并突出了潜在的诊断和治疗靶点。需要进一步的实验验证来证实这些发现。