Single-cell sequencing and machine learning identify SLC25A45 and CADM1 as key regulators of lipid metabolism in intracerebral hemorrhage
摘要
Secondary injury following intracerebral hemorrhage (ICH) is a primary cause of patient mortality and disability. Its mechanisms involve disturbances in multiple metabolic processes, among which lipid metabolism is closely associated with cellular damage. This study aims to identify candidate biomarkers for ICH by focusing on lipid metabolism.
MethodsLipid metabolism activity was evaluated at the single-cell level using Single-Sample Gene Set Enrichment Analysis (ssGSEA). Key biomarkers were subsequently identified by integrating differential expression analysis with multiple machine learning algorithms. Their functional properties were systematically characterized through enrichment analysis, regulatory network construction, cell–cell communication, and drug prediction. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blot were performed on rat brain tissue to validate the expression of key biomarkers.
ResultsA total of 15 cell subpopulations belonging to 8 cell types were identified in the scRNA-seq data. Neuron-derived damaged/dying cells exhibited the most active expression of lipid metabolism genes. Solute carrier family 25 member 45 (SLC25A45) and cell adhesion molecule 1 (CADM1) were further identified as key biomarkers for lipid metabolism in ICH. Gene Set Enrichment Analysis (GSEA) revealed distinct functional associations for the two genes: CADM1 was predominantly linked to metabolic processes, whereas SLC25A45 was significantly associated with proteasomal activity, immune signaling pathways, and the pathogenesis of major neurodegenerative disorders. The high and low expression of these two genes both exhibited extensive cellular communication. By constructing a gene regulatory network, v-rel avian reticuloendotheliosis viral oncogene homolog A (RELA) and CCCTC-binding factor (CTCF) were identified as common transcription factors (TFs) for the two key biomarkers, and the long non-coding RNAs (lncRNAs), including nuclear paraspeckle assembly transcript 1 (NEAT1) and metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), have close regulatory relationships with CADM1. Drug sensitivity prediction based on the key biomarkers suggested Vancomycin Hydrochloride and Zinc Sulfate as potential therapeutic drugs for ICH.
ConclusionSLC25A45 and CADM1 are key biomarkers in lipid metabolism during ICH, representing promising candidates for further investigation.