Mapping the Central Genes in Bipolar Disorder Pathways: From Data to Diagnosis

Abstract

Background: Diseases related to the brain and nervous system lead to various complications in patients. One of these diseases is bipolar disorder (BD). Bipolar disorder is a multifaceted disorder that is influenced by various factors, including environment and genetics. Objectives: For this reason, this study was conducted with the aim of bioinformatically determining gene biomarkers in BD using biomarker study methods. Methods: The methodology began by identifying candidate genes through the triangulation of evidence from animal models, in vitro cell cultures, and in silico predictions. Subsequently, high-throughput gene expression data for these candidates were systematically retrieved from public bioinformatics databases. A crucial data processing step involved rigorous standardization: Expression values from case cohorts were precisely normalized against those from healthy controls. This normalization was essential to mitigate batch effects and ensure that observed differences were reliably attributable to the disease state. This allowed for the robust investigation of the primary research hypotheses. Results: The core of the analysis involved assessing the topological importance of these genes within the inferred biological networks. Utilizing five complementary network centrality criteria—namely degree, closeness, radius, betweenness, and most neighboring—a highly conserved set of essential genes was successfully isolated. Crucially, four specific genes: CI3RN, BDNF, PPARGC1A, and TP53, exhibited the highest degree of recurrence and centrality when evaluated across the entirety of the five distinct network analysis methods. Conclusions: These findings offer a clear and data-driven mandate to suggest and prioritize complementary validation studies at both the fundamental laboratory/molecular level and the translational clinical level, focusing on their efficacy as refined diagnostic biomarkers for BD.