摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-生物技术的新研究-生物信息学是一篇报道的主题。根据NewsRx记者从广州发回的新闻报道，研究表明：“虽然细胞死亡(PCD)与糖尿病肾病(DN)之间存在内在联系，但各种PCD形式之间的相互作用仍然难以捉摸。本研究旨在识别独立的DN相关PCD途径和与相关发病机制相关的生物标志物。”新闻记者引用广州中医药大学的一篇研究报道:“我们从GEO数据库中获取DN相关数据,通过单样本基因集富集分析(ssGSEA)以及单变量和多因素Logistic回归分析,确定与DN(DN-PCDs)独立相关的PCD,随后应用差分expression分析、加权基因共表达网络分析(WGCNA)和Mfuzz C luster分析。”我们筛选了与DN发病和进展相关的DN-PCD，各种机器学习技术的融合最终聚焦了HUB基因，通过数据集荟萃分析和实验验证，从而证实了HUB基因和相关通路表达的一致性。我们协调了批效应去除后的四个DN相关数据集(GSE1009，GSE142025，GSE30528和GSE30529)，我们的差异表达分析得到709个差异表达基因(DEGs)，在ssGSEA及单因素和多因素Logistic回归分析的基础上,将凋亡和凋亡细胞死亡作为DN的独立危险因素(优势比>1,P<0.05),通过WGCNA和Mfuzz分析进一步细化了588个凋亡和凋亡相关基因。结合蛋白-蛋白相互作用(PPI)网络分析、网络拓扑和机器学习，我们确定hub基因(如IL33、RPL11和CX3CR1)是糖尿病肾病的重要危险因素，其表达在随后的荟萃分析和实验验证中得到证实。我们的GSEA富集分析发现糖尿病肾病和对照样品在IL2/STAT5、il6/jak/stat3、tnf-α通过nf-kb氧化磷酸化、apopopsis等途径中的差异富集。本研究创新性地从PCD内相互作用中发现PCD对DN具有独立影响：凋亡和细胞凋亡，进一步筛选出与DN进化和进展相关的生物标志物，即IL33、RPL11和C3C1.本研究不仅为DN研究提供了以PCD为中心的视角，也为PCD介导的I细胞浸润在DN调控中的探索提供了证据。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Biotechnology-Bioinf ormatics is the subject of a report. According to news reporting from Guangzhou, People's Republic of China, by NewsRx journalists, research stated, "Although p rogrammed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conne ted, the interplay among various PCD forms remains elusive. In this study, We ai med at identifying independently DN-associated PCD pathways and biomarkers relev ant to the related pathogenesis." The news correspondents obtained a quote from the research from the Guangzhou Un iversity of Chinese Medicine, "We acquired DN-related datasets from the GEO data base and identified PCDs independently correlated with DN (DN-PCDs) through sing le-sample Gene Set Enrichment Analysis (ssGSEA) as well as, univariate and multi variate logistic regression analyses. Subsequently, applying differential expres sion analysis, weighted gene co-expression network analysis (WGCNA), and Mfuzz c luster analysis, we filted the DN-PCDs pertinent to DN onset and progression. Th e convergence of various machine learning techniques ultimately spotlighted hub genes, substantiated through dataset meta-analyses and experimental validations, thereby confirming hub genes and related pathways expression consistencies. We harmonized four DN-related datasets (GSE1009, GSE142025, GSE30528, and GSE30529) post-batch-effect removal for subsequent analyses. Our differential expression analysis yielded 709 differentially expressed genes (DEGs), comprising 446 upreg ulated and 263 downregulated DEGs. Based on our ssGSEA as well as univariate and multivariate logistic regressions, apoptosis and NETotic cell death were apprai sed as independent risk factors for DN (Odds Ratio > 1, p<0.05). Next, we further refined 588 apoptosis- and NETot ic cell death-associated genes through WGCNA and Mfuzz analysis, resulting in th e identification of 17 DN-PCDs. Integrating protein-protein interaction (PPI) ne twork analyses, network topology, and machine learning, we pinpointed hub genes (e.g., IL33, RPL11, and CX3CR1) as significant DN risk factors with expressions corroborating in subsequent meta-analyses and experimental validations. Our GSEA enrichment analysis discerned differential enrichments between DN and control s amples within pathways such as IL2/STAT5, IL6/JAK/STAT3, TNF-a via NF-kB, apopto sis, and oxidative phosphorylation, with related proteins such as IL2, IL6, and TNFa, which we subsequently submitted to experimental verification. Innovatively stemming from from intra-PCD interactions, in this study, we discerned PCDs wit h an independent impact on DN: apoptosis and NETotic cell death. We further scre ened DN evolution- and progression-related biomarkers, i.e. IL33, RPL11, and CX3 CR1, all of which we empirically validated. This study not only poroposes a PCD- centric perspective for DN studies but also provides evidence for PCD-mediated i mmune cell infiltration exploration in DN.regulation."
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