Machine learning algorithms integrate bulk and single-cell RNA data to unveil oxidative stress following intracerebral hemorrhage

Abstract

BackgroundIncreased oxidative stress (OS) activity following intracerebral hemorrhage (ICH) had significantly impacting patient prognosis. Identifying optimal genes associated with OS could enhance the understanding of OS after ICH. MethodsWe employed single-cell RNA sequencing (scRNA-seq) to investigate the heterogeneity of OS across various cellular tiers following ICH, aiming to acquire biological insights into ICH. We utilized AUCell, Ucell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, to identify hub genes influencing high OS post-ICH. Furthermore, we employed four machine learning algorithms, eXtreme Gradient Boosting, Boruta, Random Forest, and Least Absolute Shrinkage and Selection Operator, to identify the optimal feature genes. To validate the accuracy of our analysis, we conducted validation in ICH animal experiments. ResultsAfter analyzing the scRNA-seq dataset using various algorithms, we found that OS activity exhibited heterogeneity across different cellular layers following ICH, with particularly heightened activity observed in monocytes. Further integration of bulk data and machine learning algorithms revealed that ANXA2 and COTL1 were closely associated with high OS after ICH. Our animal experiments demonstrated an increase in OS expression post-ICH. Additionally, the protein expression of ANXA2 and COTL1 was significantly elevated and co-localized with microglia. Pearson correlation coefficient analysis revealed a significant correlation between ANXA2 and OS, indicating strong consistency (r = 0.84, p < 0.05). Similar results were observed for COTL1 and OS (r = 0.69, p < 0.05). ConclusionsFollowing ICH, ANXA2 and COTL1 might penetrate the brain via monocytes, localize within microglia, and enhance OS activity. This might help us better understand OS after ICH.