Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Diabetic Nephropathy01:28

Diabetic Nephropathy

Definition Diabetic nephropathy is a chronic kidney complication that results from prolonged hyperglycemia.Prevalence It is the most common cause of chronic kidney disease (CKD) and end-stage renal disease (ESRD) worldwide, affecting up to half of individuals with diabetes.Pathophysiology • Sustained hyperglycemia triggers multiple hemodynamic and metabolic changes in the kidney. • Early in the disease, increased renal blood flow and glomerular hyperfiltration occur due to afferent arteriolar...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An IHC-derived TLS-CD8-macrophage immune niche score predicts major pathological response to neoadjuvant chemoimmunotherapy in resectable NSCLC.

Frontiers in immunology·2026
Same author

Spectroscopic evidence for a first-order transition to a possible orbital Fulde-Ferrell-Larkin-Ovchinnikov state.

Nature communications·2026
Same author

Orbital-effect-induced finite-momentum pairing and Josephson vortex lattice melting in layered Ising superconductors.

National science review·2026
Same author

Anisotropic Upper Critical Field beyond the Pauli Limit in a Nickelate Superconductor: Evidence for a Quantum Fluctuation Driven State.

Physical review letters·2026
Same author

Diagnostic and therapeutic potential of serum fatty acids in hyperlipidemia: evidence from three cohorts of patients with coronary atherosclerotic heart disease.

Metabolomics : Official journal of the Metabolomic Society·2026
Same author

Nonreciprocal second harmonic resistance in superconducting films induced by asymmetric pinning of vortices.

Journal of physics. Condensed matter : an Institute of Physics journal·2025

Related Experiment Video

Updated: May 14, 2026

Comparative Proteomic Analysis of Whole Kidney, Medulla, and Cortical Tubules in Diabetic Pathogenesis of Kidney Injury in Mice
10:31

Comparative Proteomic Analysis of Whole Kidney, Medulla, and Cortical Tubules in Diabetic Pathogenesis of Kidney Injury in Mice

Published on: May 2, 2025

Transcriptomic Profiling Combined with Machine Learning and Mendelian Randomization Identifies Diagnostic Biomarkers

Haiwen Liu1, Qiang Fu1, Jing Chen1

  • 1School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin 150040, China.

Molecules (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

This study identifies five key genes (SPP1, CD44, VCAM1, C3, TIMP1) as potential diagnostic biomarkers for diabetic kidney disease (DKD). A diagnostic model using these genes showed high accuracy, suggesting new therapeutic targets for DKD.

Keywords:
Mendelian randomizationWGCNAbiomarkerdiabetic kidney diseasediagnostic modelimmune infiltrationmachine learningtranscriptomics

More Related Videos

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Related Experiment Videos

Last Updated: May 14, 2026

Comparative Proteomic Analysis of Whole Kidney, Medulla, and Cortical Tubules in Diabetic Pathogenesis of Kidney Injury in Mice
10:31

Comparative Proteomic Analysis of Whole Kidney, Medulla, and Cortical Tubules in Diabetic Pathogenesis of Kidney Injury in Mice

Published on: May 2, 2025

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
09:16

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Area of Science:

  • Genomics and Bioinformatics
  • Nephrology
  • Computational Biology

Background:

  • Diabetic kidney disease (DKD) affects 40% of diabetes patients, leading to end-stage renal disease.
  • Current diagnostic methods for DKD are limited, highlighting the need for novel biomarkers.
  • Identifying therapeutic targets is crucial for improving DKD patient outcomes.

Purpose of the Study:

  • To identify reliable diagnostic biomarkers for diabetic kidney disease (DKD).
  • To explore potential therapeutic targets for DKD using integrated bioinformatics approaches.
  • To develop and validate a diagnostic model for DKD based on identified biomarkers.

Main Methods:

  • Integrated transcriptomic data from GEO (GSE96804, GSE30528, GSE142025) with machine learning (LASSO, RF, SVM-RFE, XGBoost) and Mendelian randomization (MR).
  • Utilized WGCNA to identify key gene modules and performed feature selection for hub genes.
  • Employed CIBERSORT for immune infiltration analysis and two-sample MR for causal association analysis.

Main Results:

  • Identified five hub genes (SPP1, CD44, VCAM1, C3, TIMP1) as potential DKD biomarkers.
  • A diagnostic model achieved high cross-validated AUC (0.938) and external validation AUCs (0.917, 0.889).
  • SPP1, C3, and TIMP1 showed potential causal links to estimated glomerular filtration rate decline; hub genes correlated with M1 macrophage infiltration.

Conclusions:

  • The identified hub genes serve as promising diagnostic biomarkers for DKD.
  • The study provides a computational framework for discovering DKD biomarkers and therapeutic targets.
  • Further experimental validation is required for clinical application of these findings.