Integrated bioinformatics analysis of ac4C RNA modification and macrophages reveals common molecular mechanisms in ulcerative colitis and major depressive disorder

Abstract

Ulcerative colitis (UC) and major depressive disorder (MDD) are two chronic diseases with high comorbidity rates, and existing therapies have limitations such as insufficient efficacy and significant side effects. This study aims to reveal the common mechanisms of ac4C RNA modification and macrophages in both diseases by integrating multi-omics data. In this research, we identified 346 differentially expressed genes between UC and MDD, with functional enrichment analysis primarily focusing on immune and inflammatory response signaling pathways. We intersected these with ac4C RNA modification-related genes to obtain 39 co-expressed genes, whose functional enrichment analysis mainly concentrated on redox reactions, extracellular vesicle biology, and metabolic signaling pathways. Additionally, we analyzed the relationship between UC and MDD and macrophage infiltration, conducting differential analysis of macrophage infiltration values between the disease group and the normal group. Furthermore, we intersected the 39 co-expressed genes obtained from the intersection of both diseases with the WGCNA key module genes of the two diseases, resulting in three key genes. Four machine learning algorithms were employed to predict the diagnostic performance of the models constructed from the key genes of both diseases, with the highest diagnostic performance being RF, and AUC values all above 0.9. Subsequently, differential expression validation of the key genes was conducted in the training and validation sets of both diseases, and ROC curve analysis of the obtained key genes was performed, with AUC values all greater than 0.6. A further correlation analysis of key gene expression levels and key macrophage infiltration values confirmed a significant positive correlation between the key genes of both diseases and the macrophage infiltration values under the ssGSEA algorithm. In addition, the relationship between key genes and gut microbiota and metabolites was analyzed, along with the GSEA enrichment of key genes, and potential microRNAs and TFs regulating the key genes were predicted. Reverse pharmacology analysis, molecular docking, and molecular dynamics simulations were used to predict therapeutic drugs for UC and MDD. Finally, single-cell sequencing analysis was conducted on both diseases, yielding relevant cell clustering results, and the expression of key genes was analyzed in the single-cell datasets of both diseases. Ultimately, FKBP5 was identified as a common biomarker of ac4C RNA modification and macrophages in both diseases. Five potential most significantly related Chinese Materia Medica for treating UC and MDD were identified. Based on molecular docking and molecular dynamics simulation analysis, isoacteoside targeting FKBP5 was predicted to be the most effective compound. These findings greatly enhance our understanding of the regulatory mechanisms of ac4C modification and macrophages in brain-gut axis diseases. Key biomarkers have been identified, and potential therapeutic drugs predicted can effectively target UC and MDD. This study systematically elucidates the comorbidity mechanisms of ac4C modification and macrophages in UC and MDD for the first time, providing new targets for developing cross-disease therapeutic strategies. Future studies should further validate intervention strategies targeting ac4C modification and macrophages through organoid models and clinical trials, and explore their bidirectional regulatory effects on the gut-brain axis.