Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation.

OBJECTIVE: Although programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conneted, the interplay among various PCD forms remains elusive. In this study, We aimed at identifying independently DN-associated PCD pathways and biomarkers relevant to the related pathogenesis. METHODS: We acquired DN-related datasets from the GEO database and identified PCDs independently correlated with DN (DN-PCDs) through single-sample Gene Set Enrichment Analysis (ssGSEA) as well as, univariate and multivariate logistic regression analyses. Subsequently, applying differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Mfuzz cluster analysis, we filtered the DN-PCDs pertinent to DN onset and progression. The 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. RESULTS: 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 upregulated and 263 downregulated DEGs. Based on our ssGSEA as well as univariate and multivariate logistic regressions, apoptosis and NETotic cell death were appraised as independent risk factors for DN (Odds Ratio > 1, p < 0.05). Next, we further refined 588 apoptosis- and NETotic cell death-associated genes through WGCNA and Mfuzz analysis, resulting in the identification of 17 DN-PCDs. Integrating protein-protein interaction (PPI) network analyses, network topology, and machine learning, we pinpointed hub genes (e.g., IL33, RPL11, and CX3CR1) as significant DN risk factors with expression corroborating in subsequent meta-analyses and experimental validations. Our GSEA enrichment analysis discerned differential enrichments between DN and control samples within pathways such as IL2/STAT5, IL6/JAK/STAT3, TNF-α via NF-κB, apoptosis, and oxidative phosphorylation, with related proteins such as IL2, IL6, and TNFα, which we subsequently submitted to experimental verification. CONCLUSION: Innovatively stemming from from PCD interactions, in this study, we discerned PCDs with an independent impact on DN: apoptosis and NETotic cell death. We further screened DN evolution- and progression-related biomarkers, i.e. IL33, RPL11, and CX3CR1, 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 immune cell infiltration exploration in DN regulation. Our results could motivate further exploration of DN pathogenesis, such as how the inflammatory microenvironment mediates NETotic cell death in DN regulation, representing a promising direction for future research.

Exploring the evolutionary mechanism of hepatitis B and gastric cancer based on Mendelian randomization combined with bioinformatics analysis.

Chronic hepatitis B virus infection (HBV) infection appears to be associated with extrahepatic cancers. This study aims to evaluate the causality and evolutionary mechanism of chronic HBV infection and gastric cancer through Mendelian randomization (MR) analysis and bioinformatics analysis. We conducted 2-sample MR to investigate the causal relationship between chronic HBV infection and gastric cancer. We identified 5 independent genetic variants closely associated with exposure (chronic HBV infection) as instrumental variables in a sample of 1371 cases and 2938 controls of East Asian descent in Korea. The genome wide association study (GWAS) data for the outcome variable came from the Japanese Biobank. Bioinformatics analysis was used to explore the evolutionary mechanism of chronic HBV infection and gastric cancer. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify key targets that are commonly associated with both diseases, and their biological functions were investigated. Multiple machine-learning models were employed to select hub genes. The MR analysis showed a positive causal relationship between chronic HBV infection and gastric cancer (IVW: OR = 1.165, 95% CI = 1.085-1.250, P < .001), and the result was robust in sensitivity analysis. According to the bioinformatics analysis, the 5 key targets were mainly enriched in Toll-like receptor signaling and PI3K-Akt signaling. Two hub genes, CXCL9 and COL6A2, were identified, and a high-performing predictive model was constructed. Chronic HBV infection is positively associated with gastric cancer, and the evolutionary mechanism may be related to Toll-like receptor signaling. Prospective studies are still needed to confirm these findings.

Untargeted metabolomics in Anectocillus roxburghii with habitat heterogeneity and the key abiotic factors affecting its active ingredients.

Introduction: Anoectochilus roxburghii is a rare, endangered herb with diverse pharmacological properties. Understanding the main metabolite types and characteristics of wild A. roxburghii is important for efficiently utilizing resources and examining quality according to origin. Methods: Samples were collected from the main production areas across five regions in Fujian Province, China. An untargeted metabolomics analysis was performed on the entire plants to explore their metabolic profiles. We utilized UPLC-MS/MS to specifically quantify eight targeted flavonoids in these samples. Subsequently, correlation analysis was conducted to investigate the relationships between the flavonoids content and both the biological characteristics and geographical features. Results: A comprehensive analysis identified a total of 3,170 differential metabolites, with terpenoids and flavonoids being the most prevalent classes. A region-specific metabolite analysis revealed that the Yongchun (YC) region showed the highest diversity of unique metabolites, including tangeretin and oleanolic acid. Conversely, the Youxi (YX) region was found to have the smallest number of unique metabolites, with only one distinct compound identified. Further investigation through KEGG pathway enrichment analysis highlighted a significant enrichment in pathways related to flavonoid biosynthesis. Further examination of the flavonoid category showed that flavonols were the most differentially abundant. We quantified eight specific flavonoids, finding that, on average, the YX region exhibited higher levels of these compounds. Correlation analysis highlighted a significant association between flavonoids and habitat, especially temperature and humidity. Discussion: Untargeted metabolomics via LC-MS was suitable for identifying region-specific metabolites and their influence via habitat heterogeneity. The results of this study serve as a new theoretical reference for unique markers exclusively present in a specific sample group.