RESUMEN
Background: Mania has caused incalculable economic losses for patients, their families, and even society, but there is currently no effective treatment plan for this disease without side effects. Methods: Using bioinformatics and Mendelian randomization methods, potential drug target genes and key substances associated with mania were explored at the mRNA level. We used the chip expression profile from the GEO database to screen differential genes and used the eQTL and mania GWAS data from the IEU database for two-sample Mendelian randomization (MR) to determine core genes by colocalization. Next, we utilized bioinformatics analysis to identify key substances involved in the mechanism of action and determined related gene targets as drug targets. Results: After differential expression analysis and MR, a causal relationship between the expression of 46 genes and mania was found. Colocalization analysis yielded six core genes. Five key substances were identified via enrichment analysis, immune-related analysis, and single-gene GSVA analysis of the core genes. MR revealed phenylalanine to be the only key substance that has a unidirectional causal relationship with mania. In the end, SBNO2, PBX2, RAMP3, and QPCT, which are significantly associated with the phenylalanine metabolism pathway, were identified as drug target genes. Conclusion: SBNO2, PBX2, RAMP3, and QPCT could serve as potential target genes for mania treatment and deserve further basic and clinical research. Medicinal target genes regulate the phenylalanine metabolism pathway to achieve the treatment of mania. Phenylalanine is an important intermediate substance in the treatment of mania that is regulated by drug target genes.
RESUMEN
BACKGROUND: Ischemic stroke is a disease in which cerebral blood flow is blocked due to various reasons, leading to ischemia, hypoxia, softening, and even necrosis of brain tissues. The level of cortisol is related to the occurrence and progression of ischemic stroke. However, the mechanism governing their interrelationship is still unclear. The main objective of this study was to identify and understand the molecular mechanism between cortisol and IS. METHODS: The common cortisol-related biological processes were screened by mutual verification of two data sets and the cortisol-related hub biomarkers were identified. Modular analysis of protein interaction networks was performed, and the differential pathway analysis of individual genes was conducted by GSVA and GSEA. Drug and transcription factor regulatory networks of hub genes were excavated, and the diagnostic potential of hub genes was analyzed followed by the construction of a diagnostic model. RESULTS: By screening the two data sets by GSVA, three biological processes with common differences were obtained. After variation analysis, four cortisol-related hub biomarkers (CYP1B1, CDKN2B, MEN1, and USP8) were selected. Through the modular analysis of the protein-protein interaction network and double verification of GSVA and GSEA, a series of potential molecular mechanisms of hub genes were discovered followed by a series of drug regulatory networks and transcription factor regulatory networks. The hub biomarkers were found to have a high diagnostic value by ROC; thus, a diagnostic model with high diagnostic efficiency was constructed. The diagnostic value was mutually confirmed in the two data sets. CONCLUSION: Four cortisol-related hub biomarkers are identified in this study, which provides new ideas for the key changes of cortisol during the occurrence of IS.
