Characterization of NLRP3 Inflammasome‐Associated Hub Genes in the Progression of Diabetic Nephropathy

BACKGROUND: Diabetic nephropathy (DN) is widely recognized as the primary cause of end-stage renal disease. However, the underlying mechanisms and pathogenesis of DN remain incompletely understood. Exploring novel biomarkers aiding in tracking the progression of DN has important clinical implications. METHODS: Human renal transcriptomic datasets (GSE142025 and GSE96804) from the Gene Expression Omnibus (GEO) were analyzed. Differentially expressed genes (DEGs) were identified using a staged analysis, which involved sequential comparisons between normal controls and early-stage DN, as well as between early DN (eDN) and advanced DN (aDN). Enrichment analysis of DEGs was performed using R software. WGCNA was utilized to construct gene co-expression networks and to identify the key genes. Then, Venn diagrams were generated using DEGs and key genes from both datasets to determine the final hub genes. ROC curves were then used to assess the diagnostic accuracy of hub genes for DN disease progression. Finally, we cross-validated the hub genes using the clinical kidney specimens. RESULTS: After analyzing the datasets, we identified 22 and 43 hub genes using the WGCNA and DEGs at the eDN and aDN stages. Further investigation of the literature on the hub genes led to the discovery of their association with the activity of NLRP3 inflammasome, including ZFP36, CLEC2D, and HCK, which were identified as NLRP3 inflammasome-associated hub genes (NIAHGs). We found that the AUC of all the NIAHGs can indicate their potential diagnostic value. Then, we validated the expression levels of NIAHGs in human renal tissues, which were consistent with those in both data sets. Importantly, immunoblot analysis indicated that NIAHGs expression was associated with NLRP3 inflammasome activation. Furthermore, NLRP3 inflammasome was significantly activated, leading to the release of large amounts of IL-1β and IL-18 in eDN, further initiating the inflammatory response in the diabetic kidney. CONCLUSIONS: Our findings identify critical hub genes associated with DN progression and NLRP3 inflammasome activity, providing a theoretical basis and candidate targets for subsequent research.

Authors

Institutions

Publication Details

Journal
Immunity Inflammation and Disease
Published
2026-04-01
DOI
https://doi.org/10.1002/iid3.70424
Primary Topic
Inflammasome and immune disorders
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Characterization of NLRP3 Inflammasome‐Associated Hub Genes in the Progression of Diabetic Nephropathy

Wenchuan Li, 连兴基, Lan Dong, Yuejiao Li et al.
Immunity Inflammation and Disease
Inflammasome and immune disorders
article

Characterization of NLRP3 Inflammasome‐Associated Hub Genes in the Progression of Diabetic Nephropathy

Wenchuan Li, 连兴基, Lan Dong, Yuejiao Li, Sheng Zhao, Feng He, Ru Lian, Qingqing Luo, Jianbo Li
article en

Abstract

BACKGROUND: Diabetic nephropathy (DN) is widely recognized as the primary cause of end-stage renal disease. However, the underlying mechanisms and pathogenesis of DN remain incompletely understood. Exploring novel biomarkers aiding in tracking the progression of DN has important clinical implications. METHODS: Human renal transcriptomic datasets (GSE142025 and GSE96804) from the Gene Expression Omnibus (GEO) were analyzed. Differentially expressed genes (DEGs) were identified using a staged analysis, which involved sequential comparisons between normal controls and early-stage DN, as well as between early DN (eDN) and advanced DN (aDN). Enrichment analysis of DEGs was performed using R software. WGCNA was utilized to construct gene co-expression networks and to identify the key genes. Then, Venn diagrams were generated using DEGs and key genes from both datasets to determine the final hub genes. ROC curves were then used to assess the diagnostic accuracy of hub genes for DN disease progression. Finally, we cross-validated the hub genes using the clinical kidney specimens. RESULTS: After analyzing the datasets, we identified 22 and 43 hub genes using the WGCNA and DEGs at the eDN and aDN stages. Further investigation of the literature on the hub genes led to the discovery of their association with the activity of NLRP3 inflammasome, including ZFP36, CLEC2D, and HCK, which were identified as NLRP3 inflammasome-associated hub genes (NIAHGs). We found that the AUC of all the NIAHGs can indicate their potential diagnostic value. Then, we validated the expression levels of NIAHGs in human renal tissues, which were consistent with those in both data sets. Importantly, immunoblot analysis indicated that NIAHGs expression was associated with NLRP3 inflammasome activation. Furthermore, NLRP3 inflammasome was significantly activated, leading to the release of large amounts of IL-1β and IL-18 in eDN, further initiating the inflammatory response in the diabetic kidney. CONCLUSIONS: Our findings identify critical hub genes associated with DN progression and NLRP3 inflammasome activity, providing a theoretical basis and candidate targets for subsequent research.

Immunity Inflammation and DiseaseVol. 14(4)
Sun Yat-sen University (CN), Second Affiliated Hospital of Guangzhou Medical University (CN), Guangzhou First People's Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Ministry of Health (TT), Guangzhou Medical University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province
Openalex Percentile: Top 23%
Inflammasome and immune disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.