首页|Guangzhou University of Chinese Medicine Reports Findings in Bioinformatics (Apo ptosis and NETotic cell death affect diabetic nephropathy independently: An stud y integrative study encompassing bioinformatics, machine learning, and experimen tal ...)
Guangzhou University of Chinese Medicine Reports Findings in Bioinformatics (Apo ptosis and NETotic cell death affect diabetic nephropathy independently: An stud y integrative study encompassing bioinformatics, machine learning, and experimen tal ...)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
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."
GuangzhouPeople's Republic of ChinaA siaApoptosisBioinformaticsBiomarkersBiotechnologyCellular PhysiologyCyborgsDiabetes ComplicationsDiabetes MellitusDiabetic NephropathyDiagno stics and ScreeningEmerging TechnologiesGeneticsHealth and MedicineInfor mation TechnologyKidney Diseases and ConditionsMachine LearningNephrologyNephropathyNutritional and Metabolic Diseases and ConditionsRisk and Preven tion
NETL
NSTL
