ArticleInflammation2026
Multiomics Integration Analysis Reveals the Regulatory Mechanisms of Efferocytosis in Diabetic Kidney Disease.
Article in Inflammation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Diabetic kidney disease (DKD) is a prevalent complication in individuals with diabetes. Efferocytosis plays a pivotal role in chronic diseases; however, the precise mechanisms involved in DKD are still not fully understood. DKD-related datasets were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were screened. These DEGs subsequently intersected with efferocytosis-related genes (ERGs) to produce DKD efferocytosis‒related genes (DKD-ERGs). Potential hub genes were subsequently identified using protein‒protein interaction (PPI) network analysis in combination with machine learning (LASSO regression, Boruta algorithm, and random forest algorithm). Next, we employed transcriptomics, proteomics, and metabolomics analyses of DKD animal models, followed by validation with serum samples from patients with DKD. A nomogram was developed using hub genes to evaluate its predictive accuracy. Consensus clustering was utilized to categorize DKD patients and conduct immune infiltration analysis. A total of 15 DKD-related ERGs were identified. ANXA1, CASP3, IL33, and C3 were identified as potential hub genes. First, validation was performed using the GEO and Nephroseq databases. The hub genes were subsequently validated from multiple perspectives, including transcriptomics, metabolomics, and proteomics of DKD animal models, as well as serological analysis of DKD patients. A risk score model incorporating these 4 hub genes effectively predicted both the onset and progression of DKD. On the basis of these hub genes, DKD patients were classified into Cluster 1 and Cluster 2, with distinct subtypes and immune infiltration correlating with disease stages. This study reveals the potential diagnostic value of ERGs (ANXA1, CASP3, IL33, and C3) in DKD through multidimensional analysis. These genes may serve as promising biomarkers and therapeutic targets for DKD.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.