Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
October 22, 2020Open Access

Potential Biomarkers of Diabetic Kidney Disease Based on Weighted Gene Co-expression Network Analysis

View Full Paper
Ask AI
Bookmark
Share

Key result

Weighted gene co-expression network analysis identified turquoise (r = -0.69) and purple (r = 0.65) modules as having the highest significant correlations with the large proteinuria stage in diabetic kidney disease.

Population

23 peripheral blood samples from patients with Diabetic Kidney Disease (DKD)

Design

Other

Authors

YCYang ChenKXKangli XiaoNSNingjie Shi

Discussion

Loading...

Member takes

Overview

WGCNA-identified DKD genes are hypothesis-generating; validation studies needed before any clinical application.

Study Design

Type

Cross-Sectional (n=23)

Multicenter

No

Structured PICO

P
Population
23 patients with diabetic kidney disease categorized by proteinuria stage for RNA-seq and weighted gene co-expression network analysis.
E
Exposure
Weighted gene co-expression network analysis (WGCNA) of RNA-seq data
O
Outcome
Identification of key genes and pathways correlated with large proteinuria stage in DKDsurrogate

Main Result

Effect estimate: r = -0.69

p-value: p=4x10^-4

WGCNA identified DCN, F2R, LTBP2, B2M, PSMB8, and NLRC5 as potential diagnostic and therapeutic biomarker targets for diabetic kidney disease.

Limitations

  • Additional experiments are necessary to elucidate the role of the identified pathways in diabetic kidney disease.

Cite This Study

Chen et al. (2020) conducted a cross-sectional in Diabetic kidney disease (n=23). Gene co-expression modules was evaluated on Correlation of module eigengene with large proteinuria stage (Group C) (r = -0.69, p=4x10^-4). Weighted gene co-expression network analysis identified turquoise (r = -0.69) and purple (r = 0.65) modules as having the highest significant correlations with the large proteinuria stage in diabetic kidney disease.

synapsesocial.com/papers/6ab77396e5c01a8dcddc3152https://doi.org/10.21203/rs.3.rs-94431/v1
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Identification of diagnostic markers for diabetic kidney disease by weighted gene co‑expression network analysis and machine learning2026
  2. 2Identification of Hub Genes and Potential ceRNA Networks of Diabetic Nephropathy by Weighted Gene Co-Expression Network Analysis2021 · 25 citations
  3. 3Integrative transcriptomic and genomic insights into diabetic kidney disease: evidence from multi-omics analysis and experimental validation2025 · 1 citations
  4. 4Transcriptomic Profiling Combined with Machine Learning and Mendelian Randomization Identifies Diagnostic Biomarkers and Immune Infiltration Patterns in Diabetic Kidney Disease2026
  5. 5Immune Infiltration and Mitochondrial Function in Diabetic Kidney Disease: WGCNA and Machine Learning Identified Hub Genes with Clinical Validation2026