Screening Targets for Diagnosis and Treatment of Cognitive Dysfunction in Stroke through Transcriptome Combined with Machine Learning

Post-Stroke Cognitive Impairment (PSCI) is one of the major complications after stroke. The evaluation of PSCI usually depends on neuropsychology tests, but the results of these tests are subjective and inaccurate. Need to find more objective indicators as identification markers of PSCI. In this study, we use machine learning to find biomakers of PSCI, and established regulatory networks at transcriptional level. Several gene such as ORC1, TOMM40L and SHISAL2A are identified biomakers, and several miRNA such as hsa-mir-130b-3p and hsa-mir-484 are interacted most tight with this biomakers genes. The results of this study help to better distinguish patients with PS and PSCI in clinical practice, and identifying relevant biomarker genes and miRNAs that can serve as potential therapeutic sites.