Table_1_Identification and analysis of cellular senescence-associated signatures in diabetic kidney disease by integrated bioinformatics analysis and machine learning.docx

BackgroundDiabetic kidney disease (DKD) is a common complication of diabetes that is clinically characterized by progressive albuminuria due to glomerular destruction. The etiology of DKD is multifactorial, and numerous studies have demonstrated that cellular senescence plays a significant role in its pathogenesis, but the specific mechanism requires further investigation.
MethodsThis study utilized 5 datasets comprising 144 renal samples from the Gene Expression Omnibus (GEO) database. We obtained cellular senescence-related pathways from the Molecular Signatures Database and evaluated the activity of senescence pathways in DKD patients using the Gene Set Enrichment Analysis (GSEA) algorithm. Furthermore, we identified module genes related to cellular senescence pathways through Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm and used machine learning algorithms to screen for hub genes related to senescence. Subsequently, we constructed a cellular senescence-related signature (SRS) risk score based on hub genes using the Least Absolute Shrinkage and Selection Operator (LASSO), and verified mRNA levels of hub genes by RT-PCR in vivo. Finally, we validated the relationship between the SRS risk score and kidney function, as well as their association with mitochondrial function and immune infiltration.
ResultsThe activity of cellular senescence-related pathways was found to be elevated among DKD patients. Based on 5 hub genes (LIMA1, ZFP36, FOS, IGFBP6, CKB), a cellular senescence-related signature (SRS) was constructed and validated as a risk factor for renal function decline in DKD patients. Notably, patients with high SRS risk scores exhibited extensive inhibition of mitochondrial pathways and upregulation of immune cell infiltration.
ConclusionCollectively, our findings demonstrated that cellular senescence is involved in the process of DKD, providing a novel strategy for treating DKD.

背景
糖尿病肾病（Diabetic kidney disease, DKD）是糖尿病的常见并发症，临床以肾小球破坏引发的进行性白蛋白尿为典型特征。DKD的病因呈多因素特性，诸多研究已证实细胞衰老在其发病机制中发挥关键作用，但具体调控机制仍有待深入探究。

方法
本研究采用来自基因表达综合数据库（Gene Expression Omnibus, GEO）的5组数据集，共计144份肾脏样本。我们从分子特征数据库（Molecular Signatures Database）获取细胞衰老相关通路，并通过基因集富集分析（Gene Set Enrichment Analysis, GSEA）算法评估DKD患者体内衰老通路的激活活性。进一步借助加权基因共表达网络分析（Weighted Gene Co-Expression Network Analysis, WGCNA）算法筛选与细胞衰老通路相关的模块基因，并结合机器学习算法挖掘得到衰老相关核心基因。随后，我们基于核心基因，通过最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）构建细胞衰老相关特征（Senescence-Related Signature, SRS）风险评分模型，并通过体内实时荧光定量PCR（RT-PCR）验证核心基因的mRNA表达水平。最后，我们验证了SRS风险评分与肾功能的关联，以及其与线粒体功能、免疫浸润的相关性。

结果
本研究发现，DKD患者体内细胞衰老相关通路的激活活性显著升高。我们基于LIMA1、ZFP36、FOS、IGFBP6、CKB这5个核心基因构建了细胞衰老相关特征（SRS）模型，并验证该模型可作为DKD患者肾功能下降的风险预测因子。值得注意的是，SRS风险评分较高的患者呈现出线粒体通路广泛受抑制以及免疫细胞浸润上调的特征。

结论
综上，本研究证实细胞衰老参与了DKD的发生发展进程，为DKD的临床治疗提供了全新的潜在策略。