Genes
Identification and In Vitro Validation of Metabolic Reprogramming-Related Hub Genes for the Diagnosis of Subarachnoid Hemorrhage
By Jin Xue, Zhaoxiang Zhang, Wuting Wei and 6 others.
Abstract
Background
Metabolic reprogramming plays a vital role in subarachnoid hemorrhage (SAH). This study aims to identify biomarkers related to metabolic reprogramming in SAH.
Methods
Transcriptome datasets (GSE36791 and GSE73378) were downloaded from the GEO database, and the metabolic reprogramming-related genes were retrieved from the Molecular Signatures Database. Candidate genes were identified by intersecting differentially expressed genes, WGCNA module genes, and metabolic genes. Hub genes were screened via CytoHubba, machine learning, and Mendelian randomization. ROC curves were generated to evaluate the diagnostic performance of the hub genes. Additionally, functional enrichment analysis, circulating immune-cell composition analysis, and drug prediction were performed. A cellular SAH model (U251 cells) and clinical samples were used for RT-qPCR validation.