Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy DOI Creative Commons
Li Zhang,

Zhenpeng Sun,

Yuan Yao

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 15, 2025

Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public health. Among its various complications, diabetic nephropathy (DN) emerges as critical microvascular complication associated with high mortality rates. Despite the development of diverse therapeutic strategies targeting improvement, hemodynamic regulation, and fibrosis mitigation, precise mechanisms responsible for glomerular injury in DN are not yet fully elucidated. To explore these mechanisms, datasets (GSE30528, GSE104948, GSE96804) were obtained from GEO database. We merged GSE30528 GSE104948 identify differentially expressed genes (DEGs) between control groups using R software. Weighted gene co-expression network analysis (WGCNA) was subsequently employed discern key modules. utilized Venny software pinpoint co-expressed shared DEGs module genes. These underwent ontology (GO) Kyoto encyclopedia genomes (KEGG) enrichment analyses. Through LASSO, SVM, RF methods, we isolated five genes: FN1, C1orf21, CD36, CD48, SRPX2. further validated logistic model 10-fold cross-validation. The external dataset GSE96804 served validate identified biomarkers, while receiver operating characteristic (ROC) curve assessed their diagnostic efficacy DN. Additionally, facilitated comparison biomarker expression levels other kidney diseases, highlighting specificity biomarkers also enabled identification validation two molecular subtypes characterized by distinct immune profiles. Nephroseq v5 database corroborated correlation clinical data. Furthermore, GSigDB predict protein-drug interactions, docking confirming potential drug targets. Finally, mouse (BKS-db) constructed, RT-qPCR experiments reliability biomarkers. study robust predictive power Subtype classification based on revealed pathways cell infiltration profiles, underscoring close relationship functions Drug prediction analyses demonstrated excellent binding affinities candidate drugs target proteins. Differential diseases indicated all except highly Notably, lacks C1orf21 gene, confirmed upregulated This successfully value only offer insights into regulatory underlying but provide theoretical foundation targets related DN-associated injury.

Language: Английский

Disulfidptosis: A new type of cell death DOI Creative Commons
Fei Xiao, Huili Li, Bei Yang

et al.

APOPTOSIS, Journal Year: 2024, Volume and Issue: unknown

Published: June 17, 2024

Abstract Disulfidptosis is a novel form of cell death that distinguishable from established programmed pathways such as apoptosis, pyroptosis, autophagy, ferroptosis, and oxeiptosis. This process characterized by the rapid depletion nicotinamide adenine dinucleotide phosphate (NADPH) in cells high expression solute carrier family 7 member 11 (SLC7A11) during glucose starvation, resulting abnormal cystine accumulation, which subsequently induces andabnormal disulfide bond formation actin cytoskeleton proteins, culminating network collapse disulfidptosis. review aimed to summarize underlying mechanisms, influencing factors, comparisons with traditional pathways, associations related diseases, application prospects, future research directions

Language: Английский

Citations

9
Integrative analysis of COL6A3 in lupus nephritis: insights from single-cell transcriptomics and proteomics DOI Creative Commons
Lisha Mou, Fan Zhang,

Xingjiao Liu

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: May 24, 2024

Lupus nephritis (LN), a severe complication of systemic lupus erythematosus (SLE), presents significant challenges in patient management and treatment outcomes. The identification novel LN-related biomarkers therapeutic targets is critical to enhancing outcomes prognosis for patients.

Language: Английский

Citations

5
Proton pump inhibitors use and risk of type 2 diabetes mellitus: correlation analysis, prediction model construction, and key genes identification DOI Creative Commons
Cuilv Liang, Yin Zhang

Frontiers in Pharmacology, Journal Year: 2025, Volume and Issue: 16

Published: April 29, 2025

Prior cohort studies reported paradoxical results between proton pump inhibitor (PPI) usage and the risk of type 2 diabetes mellitus (T2DM). We investigated correlation use PPIs T2DM risk, constructed predictive models, identified key genes involved. In analysis, we extracted analyzed data from National Health Nutrition Examination Survey (NHANES) database FDA Adverse Event Reporting System (FAERS) to examine relationship risk. Then, a nomogram was estimate probability in patients treated with by using optimal predictors least absolute shrinkage selection operator logistic regression methods. Finally, modulated PPI combining various bioinformatics techniques such as network pharmacology, difference weighted gene co-expression analysis. NHANES database, regardless whether merely included or used adjust for covariates, binomial models indicated positive (all p < 0.001). FAERS signal significant (lower limit reporting odds ratio greater than 1). Sex, race, age, educational level, obesity, hypertension, high cholesterol were predict usage-induced 0.05). By intersecting cluster intersection usage-related T2DM-related genes, finally two crucial AGT JAK2, that may be involved Our findings revealed treatment can increase T2DM. Additionally, successful constructing new identify individuals at developing among completed preliminary exploration possible targets mechanisms. study will useful alerting clinicians allowing them take early prevention intervention measures.

Language: Английский

Citations

0
Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy DOI Creative Commons
Li Zhang,

Zhenpeng Sun,

Yuan Yao

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 15, 2025

Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public health. Among its various complications, diabetic nephropathy (DN) emerges as critical microvascular complication associated with high mortality rates. Despite the development of diverse therapeutic strategies targeting improvement, hemodynamic regulation, and fibrosis mitigation, precise mechanisms responsible for glomerular injury in DN are not yet fully elucidated. To explore these mechanisms, datasets (GSE30528, GSE104948, GSE96804) were obtained from GEO database. We merged GSE30528 GSE104948 identify differentially expressed genes (DEGs) between control groups using R software. Weighted gene co-expression network analysis (WGCNA) was subsequently employed discern key modules. utilized Venny software pinpoint co-expressed shared DEGs module genes. These underwent ontology (GO) Kyoto encyclopedia genomes (KEGG) enrichment analyses. Through LASSO, SVM, RF methods, we isolated five genes: FN1, C1orf21, CD36, CD48, SRPX2. further validated logistic model 10-fold cross-validation. The external dataset GSE96804 served validate identified biomarkers, while receiver operating characteristic (ROC) curve assessed their diagnostic efficacy DN. Additionally, facilitated comparison biomarker expression levels other kidney diseases, highlighting specificity biomarkers also enabled identification validation two molecular subtypes characterized by distinct immune profiles. Nephroseq v5 database corroborated correlation clinical data. Furthermore, GSigDB predict protein-drug interactions, docking confirming potential drug targets. Finally, mouse (BKS-db) constructed, RT-qPCR experiments reliability biomarkers. study robust predictive power Subtype classification based on revealed pathways cell infiltration profiles, underscoring close relationship functions Drug prediction analyses demonstrated excellent binding affinities candidate drugs target proteins. Differential diseases indicated all except highly Notably, lacks C1orf21 gene, confirmed upregulated This successfully value only offer insights into regulatory underlying but provide theoretical foundation targets related DN-associated injury.

Language: Английский

Citations

0