Single-cell sequencing combined with machine learning reveals the mechanism of interaction between epilepsy and stress cardiomyopathy

Abstract

BackgroundEpilepsy is a disorder that can manifest as abnormalities in neurological or physical function. Stress cardiomyopathy is closely associated with neurological stimulation. However, the mechanisms underlying the interrelationship between epilepsy and stress cardiomyopathy are unclear. This paper aims to explore the genetic features and potential molecular mechanisms shared in epilepsy and stress cardiomyopathy.MethodsBy analyzing the epilepsy dataset and stress cardiomyopathy dataset separately, the intersection of the two disease co-expressed differential genes is obtained, the co-expressed differential genes reveal the biological functions, the network is constructed, and the core modules are identified to reveal the interaction mechanism, the co-expressed genes with diagnostic validity are screened by machine learning algorithms, and the co-expressed genes are validated in parallel on the epilepsy single-cell data and the stress cardiomyopathy rat model.ResultsEpilepsy causes stress cardiomyopathy, and its key pathways are Complement and coagulation cascades, HIF-1 signaling pathway, its key co-expressed genes include SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3. The key immune cell subpopulations localized by single-cell data are the T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup.ConclusionWe believe epilepsy causing stress cardiomyopathy results from a multi-gene, multi-pathway combination. We identified the core co-expressed genes (SPOCK2, CTSZ, HLA-DMB, ALDOA, SFRP1, ERBB3) and the pathways that function in them (Complement and coagulation cascades, HIF-1 signaling pathway, JAK-STAT signaling pathway), and finally localized their key cellular subgroups (T_cells subgroup, Microglia subgroup, Macrophage subgroup, Astrocyte subgroup, and Oligodendrocytes subgroup). Also, combining cell subpopulations with hypercoagulability as well as sympathetic excitation further narrowed the cell subpopulations of related functions.

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

“…5 ). Genes such as HLA-DMB 39 , HLA-B 40 , and GPX1 41 were found to be profoundly expressed in cardiomyopathy. While other biomarkers such as RN7SL2 42 , LILRA2 43 , GAS5 44 , TWF2 45 , EGLN2 46 , SNHG6 47 – 49 , and BRK1 50 have all been previously associated with phenotypic variations linked to CVD, there is limited literature associating protein-coding genes such as RPS28P7 and CTA-363E6.6 to other known CVDs.…”

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

“…Genes such as HLA-DMB [28], HLA-B [29], and GPX1 [30] were found to be profoundly expressed in cardiomyopathy. While other biomarkers such as RN7SL2 [31], LILRA2 [32], GAS5 [33], TWF2 [34], EGLN2 [35], SNHG6 [36, 37, 38], and BRK1 [39] have all been previously associated with phenotypic variations linked to CVD, there is limited literature associating protein-coding genes such as RPS28P7 and CTA-363E6.6 to other known CVDs.…”

A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases

“…In ALS, Sparse Canonical Correlation Analysis explored the role of genes in cognitive dysfunction using whole-genome sequencing [75,76]. For epilepsy, random forest and XGBoost identified co-expressed genes linked to the cardiac event risk [77]. The machine learning-driven metabolomic profiling of aneurysmal subarachnoid hemorrhage patients uncovered biomarkers for functional outcomes [78].…”