It is well known that bipolar disorder (BD) and epilepsy (EP) are common neurological diseases. The objective of this study was to screen for potential biomarkers applicable to the diagnosis of EP and BD. The gene expression profiles from both the BD and EP datasets were sourced from the Gene Expression Omnibus database. To pinpoint the core shared genes, we conducted differential expression analysis as well as weighted gene co-expression network analysis. Additionally, we leveraged protein-protein interaction, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment to uncover the pathogenic genes of BD and EP, as well as their underlying mechanisms. Using least absolute shrinkage and selection operator regression, support vector machine-recursive feature elimination, and random forest, hub genes were determined via rigorous examination. Subsequently, predictive nomograms and receiver operating characteristic curves were crafted to forecast BD and EP. A single-gene set enrichment analysis was executed meticulously on every diagnostic gene, aiming to identify shared signaling pathways. To round things off, the cell-type identification by estimating relative subsets of RNA transcripts algorithm analysis explored immune cell infiltration within BD and EP samples. After analyzing the intersection of weighted gene co-expression network analysis significant module genes and the differentially expressed genes, we pinpointed 113 genes of interest. Our protein-protein interaction analysis revealed 3 pivotal modules, each harboring 14 genes, which are considered pivotal for diagnosing BD and EP. The machine learning models consistently highlighted 2 genes - Regulators of G-protein signaling 4 and gamma-aminobutyric acid type A receptor subunit alpha1 - as universal diagnostic biomarkers. Furthermore, the immune infiltration analysis disclosed that activated M2 macrophages and mast cells are integral players in the onset of BD and EP.
Zhang et al. (Fri,) studied this question.