Identification of biomarkers of cerebral infarction related to ferroptosis and ubiquitin-proteasome system based on transcriptomics and the experimental validation

Cerebral infarction is a neurological disease with complex pathological mechanisms. This study aimed to explore the association between ferroptosis-related genes (FRGs), ubiquitin-proteasome system-related genes (UPSRGs) and cerebral infarction, and to find possible biomarkers for diagnosis. Gene expression data of cerebral infarction were downloaded. Module genes linked to both FRGs and UPSRGs were screened using Weighted Gene Co-expression Network Analysis (WGCNA). Candidate genes were obtained by intersecting Differentially expressed genes (DEGs) and module genes identified by WGCNA. Machine learning algorithms were employed to identify intersecting genes. Gene expression level analysis and receiver operating characteristic curve (ROC) analysis were performed to identify biomarkers, which were further validated via in vitro experiments. Gene Set Enrichment Analysis (GSEA) and immune cell infiltration assessment were also performed. WGCNA identified 1,304 module genes identified, and a total of 110 candidate genes were identified through the intersection of the 512 DEGs with the WGCNA - identified module genes. A total of seven intersecting genes were identified through the application of machine learning techniques, and CD19 and CCR7 were confirmed as biomarkers via expression and ROC analyses. GSEA indicated that the biomarkers were involved in mitochondrial function and ubiquitin-proteasome system pathways. Analysis of immune cell infiltration revealed that the identified biomarkers exhibited associations with various immune cell types. CD19 and CCR7 were identified as candidate diagnostic biomarkers for cerebral infarction, providing exploratory evidence for immune-related changes that require further mechanistic validation.