Bioinformatics, Machine Learning Reveal New Insights Into MG-Associated DCM

Findings from a recent study offer new insight into the coexistence of dilated cardiomyopathy (DCM) and myasthenia gravis (MG), 2 conditions that have recently been identified as having a potential association, despite apparent differences in their clinical manifestations and pathophysiological mechanisms.

The researchers of the study, detailing their findings in Heliyon, identified commonly shared differentially expressed genes among MG and DCM, as well as potential biomarkers for identifying the coexistence of the 2 conditions. These insights, explained the researchers, have important implications, if validated in further research, as challenges with early detection of the comorbid condition complicate prognosis in MG.

“The occurrence rate of MG combined with DCM is relatively low. However, once it occurs, the symptoms are often significantly aggravated. In addition to manifesting as progressive muscle weakness and easy fatigue, the involvement of the myocardium also leads to notable cardiovascular symptoms, such as palpitations and shortness of breath,” described the researchers. “Currently, the mechanisms underlying the development of MG-associated DCM are not fully understood, and there is significant confusion in its diagnosis.”

Study insights may have important implications, as challenges with early detection of dilated cardiomyopathy complicate prognosis in MG | Image Credit: sergey - stock.adobe.com

Drawing on 2 DCM datasets and 1 MG dataset from the Gene Expression Omnibus, the researchers identified 11 differentially expressed genes among both conditions, which they then used in an enrichment analysis. That analysis showed that these differentially expressed genes were related to inflammation and immune regulation. Immune receptor activity and chemokine binding were the most relevant functions among the set of genes.

Using machine learning, the researchers found potential diagnostic value of midline 1 interacting protein1 (MID1IP1) and PI3K-interacting protein 1 (PIK3IP1). MID1IP1 was shown to be downregulated in both MG and DCM, and PIK3IP1 was shown to be upregulated. Although C3AR1 was also found to be downregulated in both conditions, further screening of the 3 genes identified just MID1IP1 and PIK3IP1 as core genes.

MID1IP1 has previously been implicated in affecting the pathogenesis of diseases through regulation of both stability and subcellular localization of certain intracellular proteins. PIK3IP1, a core regulator in the PI3K signaling pathway, has been shown to affect cell proliferation and other biological processes through the pathway.

“MID1IP1 and PIK3IP1, as important regulatory factors, play significant roles in cellular biological processes as well as the occurrence and development of various diseases,” commented the researchers. “Further research and exploration are needed to uncover the specific functions and regulatory mechanisms of these factors in MG-associated DCM. They hold the potential to become crucial targets for the diagnosis and treatment of MG-associated DCM in the future.”

Both MID1IP1 and PIK3IP1 were determined to have strong diagnostic value for MG-associated DCM based on a nomogram diagnostic model created by the researchers based on the genes, as well as receiver operating characteristic curve analysis. Both genes showed area under the curve (AUC) values above 0.82 and the nomogram showed higher AUC value compared with each core gene.

Analyses done by the researchers also revealed a potential therapeutic approach for MG-associated DM. Using the connectivity map database of small molecule drugs, the researchers determined that a selective inhibitor of glycogen synthase kinase-3 called SB-216763 may hold promise for patients with the comorbid condition. Data on SB-216763 have shown protective effects in cardiac and renal injury, among other conditions.

Reference

Zhou G, Wang S, Lin L, et al. Screening for immune-related biomarkers associated with myasthenia gravis and dilated cardiomyopathy based on bioinformatics analysis and machine learning. Heliyon. 2024;10(7):e28446. doi:10.1016/j.heliyon.2024.e28446