Bioinformatics Combining Machine Learning and Single-cell Sequencing Analysis to Identify Common Mechanisms and Biomarkers of Rheumatoid Arthritis and Ischemic Heart Failure

Patients with rheumatoid arthritis (RA) have an increased risk of ischemic heart failure (IHF), but the shared mechanisms are unclear. This study analyzed RNA sequencing data from five RA and IHF datasets to identify common biological mechanisms and significant biomarkers. One hundred and seventy-seven common differentially expressed genes (CDEGs) were identified, with enrichment analysis highlighting pathways related to sarcomere organization, ventricular myocardial tissue morphogenesis, chondrocyte differentiation, prolactin signaling, hematopoietic cell lineage, and protein methyltransferases. Five hub genes (CD2, CD3D, CCL5, IL7R, and SPATA18) were identified through protein-protein interaction (PPI) network analysis and machine learning. Co-expression and immune cell infiltration analyses underscored the importance of the inflammatory immune response, with hub genes showing significant correlations with plasma cells, activated CD4 T memory cells, monocytes, and T regulatory cells. Single-cell RNA sequencing (scRNA-seq) confirmed hub gene expression primarily in T cells, activated T cells, monocytes, and NK cells. The findings underscore the critical roles of sarcomere organization, prolactin signaling, protein methyltransferase activity, and immune responses in the progression of IHF in RA patients. These insights not only identify valuable biomarkers and therapeutic targets but also offer promising directions for early diagnosis, personalized treatments, and preventive strategies for IHF in the context of RA. Moreover, the results highlight opportunities for repurposing existing drugs and developing new therapeutic interventions, which could reduce the risk of IHF in RA patients and improve their overall prognosis.