Identification and analysis of cellular senescence-associated signatures in diabetic kidney disease by integrated bioinformatics analysis and machine learning - Luo, Yuanyuan; Zhang, Lingxiao; Zhao, Tongfeng (Frontiers in Endocrinology, 2023)
Diabetic kidney disease (DKD) is one of the most common complications of diabetes and is clinically featured by progressive albuminuria, consequent to glomerular destruction that involves the senescence of renal parenchymal cells. Therefore, this study aims to probe into the pathological mechanism of DKD from cellular senescence and investigate the correlation between cellular senescence and immune characteristics, mitochondrial function based on bioinformatics combined with machine learning strategy, so as to elucidate the underlying pathological mechanisms of DKD, and to explore novel treatment strategies. This study enrolled 5 datasets consisting of 144 renal samples from the Gene Expression Omnibus (GEO) database, a cellular senescence-related signature (SRS) was constructed and validated as a risk factor for renal function decline for DKD patients. Notably, patients with high SRS risk scores exhibited upregulation of immune cell infiltration and extensive inhibition of mitochondrial pathways. Collectively, our findings demonstrated that SRS is involved in the process of DKD, providing a novel strategy for treating DKD.