ArticleOpen life sciences2026
Heterogeneity characterization and key pathogenic genes screening based on diabetic nephropathy microenvironment.
Article in Open life sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Disulfidptosis: molecular mechanisms and therapeutic targets.Signal transduction and targeted therapy · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The inflammatory response is a direct factor leading to changes in the microenvironment of renal tissues. The immune cells and stromal cells infiltration were the essential characteristics of diabetic nephropathy (DN). Based on the differentiation of the microenvironment, describing the heterogeneity of DN may provide a new approach to explore the mechanisms of disease progression. This study was aimed to classify DN samples based on the infiltration levels of immune cells and stromal cells, describe the microenvironment heterogeneity of DN samples, explore the potential mechanisms of phenotypic differentiation, screen key pathogenic genes, construct a quantitative scoring model to describe the microenvironment, and investigate the role of key pathogenic genes of DN. We downloaded RNA sequencing datasets of DN tissue and normal kidney tissue (GSE142025 and GSE96804) from the Gene Expression Omnibus (GEO) database. The RNA sequencing data was transformed into immune cell and stromal cell infiltration data using the xCell algorithm. Ward's method was used for consensus clustering to identify different phenotypes of DN. We screened out the key pathogenic genes associated with phenotypes, and established a scoring model through principal component analysis which was tested the reliability and accuracy in the training cohort and the validation cohort. We explored the influence of key pathogenic genes on the biological behavior of human mesangial cells. Based on the heterogeneity of the diabetic nephropathy (DN) microenvironment, this study identified 15 key pathogenic genes: FN1, EGR1, TPM1, CCND2, COL1A2, TGFB2, COL6A3, ITGA11, ABCC9, THBS2, TNC, COL3A1, C7, C1QC, and ITGB6. The PCA score constructed from these genes (i.e., their first principal component, PC1) demonstrated good efficacy in distinguishing normal samples from DN samples (AUC = 0.90, 95 % CI [0.82-0.97]), differentiating DN subtypes with distinct microenvironments (AUC = 0.99, 95 % CI [0.97-1.00]), and stratifying DN samples at different disease stages. This score showed significant positive correlations with immune score (r = 0.75,
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.