Unraveling the genetic and molecular landscape of sepsis and acute kidney injury: A comprehensive GWAS and machine learning approach

作者：Sha Yang, Jing Guo, Yunbiao Xiong, Guoqiang Han, Tao Luo, Shuo Peng, Jian Liu, Tieyi Hu, Yan Zha, Xin Lin, Ying Tan, Jiqin Zhang · 发表于：International Immunopharmacology · 年份：2024 · DOI：10.1016/j.intimp.2024.112420 · 被引用次数：6 · 研究领域：Acute Kidney Injury Research、Sepsis Diagnosis and Treatment、S100 Proteins and Annexins

• 1. Genetic Insights into Sepsis-Associated AKI (SA-AKI): Through GWAS analysis, this study unveils the genetic relationship between acute kidney injury (AKI) and sepsis, shedding light on the underlying mechanisms of sepsis-associated AKI (SA-AKI), a critical complication in critically ill patients. • 2. Predictive Power of Signature Genes: Employing advanced machine learning algorithms, the study identifies six signature genes with exceptional predictive performance for sepsis, AKI, and SA-AKI. These genes demonstrate near-perfect AUCs in both human datasets and a sepsis mouse model, suggesting their potential as reliable biomarkers. • 3. Therapeutic Insights and CeRNA Networks: Beyond diagnosis, the research uncovers 62 potential drug treatments for sepsis and AKI, offering promising pharmacological targets. Additionally, the study constructs ceRNA networks, providing insights into the complex regulatory mechanisms underlying sepsis and AKI pathogenesis. This study aimed to explore the underlying mechanisms of sepsis and acute kidney injury (AKI), including sepsis-associated AKI (SA-AKI), a frequent complication in critically ill sepsis patients. GWAS data was analyzed for genetic association between AKI and sepsis. Then, we systematically applied three distinct machine learning algorithms (LASSO, SVM-RFE, RF) to rigorously identify and validate signature genes of SA-AKI, assessing their diagnostic and prognostic value through ROC curves and survival analysis. The study a...