Evidence mapPaperPMID 40615901Full record

ArticleJournal of translational medicine2025

Uncovering key markers and therapeutic targets for renal fibrosis in diabetic kidney disease through bulk and single-cell RNA sequencing.

Lijuan Li, Mi Tao, Xueyun Gao, Quan Cao, Zejing Liao, Feng Chen, Ayinigaer Yusufu, Haihang Nie, Ziyue Zeng, Kai Huang and 3 more

Abstract read
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Lijuan Li *Department of Hematology, Zhongnan Hospital, Wuhan University, Wuhan, China.
Mi Tao *Department of Nephrology, Zhongnan Hospital, Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China.
Xueyun Gao *Department of Nephrology, Zhongnan Hospital, Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China.
Quan CaoDepartment of Nephrology, Zhongnan Hospital, Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China.
Zejing LiaoDepartment of Nephrology, The First People's Hospital of Changde, Hunan, China.
Feng ChenDepartment of Nephrology, Zhongnan Hospital, Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China.
Ayinigaer YusufuDepartment of Nephrology, Zhongnan Hospital, Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China.
Haihang NieDepartment of Gastroenterology, Zhongnan Hospital, Wuhan University, Wuhan, China.
Ziyue ZengDepartment of Cardiology, Zhongnan Hospital, Wuhan University, Wuhan, China.
Kai HuangDepartment of Gastroenterology, Central Theatre General Hospital, Wuhan, China.
Xuan DengDepartment of Nephrology, Zhongnan Hospital, Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China. 761237742@qq.com.
Ping GaoDepartment of Nephrology, Zhongnan Hospital, Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China. tgzy1017@163.com.
Xiaoyan WuDepartment of Nephrology, Zhongnan Hospital, Wuhan University, No. 169 Donghu Road, Wuchang District, Wuhan, 430071, China. wuxiaoyan2k6@whu.edu.cn.

Funding

National Natural Science Foundation of China 82370696
6 · The paper itself

Abstract

backgroundDiabetic kidney disease (DKD) is the major cause of chronic kidney failure, with tubulointerstitial fibrosis playing a crucial role in disease development. Identifying fibrosis-related genes is crucial for improving diagnosis and developing novel therapies due to the necessity for early detection and effective treatments.

methodsGenes associated with fibrosis were identified by WGCNA, and a FibrosisScore model was constructed based on ssGSEA scores from two DKD datasets. Essential genes were subsequently confirmed by machine learning and single-cell RNA sequencing (scRNA-seq). Potential therapeutic compounds were identified by screening the ZINC database and confirmed via molecular docking. Critical genes involved in renal fibrosis were analyzed in a streptozotocin (STZ)-induced mouse model of DKD, alongside clinical data from the Nephroseq V5 database.

resultsThe FibrosisScore model exhibited strong predictive accuracy in both training and validation datasets (AUCs: 0.803, 0.992, 0.891). Patients classified as high-risk demonstrated an increase in M2 macrophages, whereas those identified as low-risk presented a higher prevalence of pro-inflammatory cells. PROM1 and THY1 were recognized as key genes associated with fibrosis. Single-cell RNA analysis revealed that PROM1 is predominantly expressed in proximal tubule cells, while THY1 is enriched in fibroblasts, indicating their distinct roles in fibrosis progression, with both genes exhibiting high diagnostic accuracy (AUC > 0.9). Immune infiltration analysis of PROM1 was primarily associated with a pro-fibrotic, immunosuppressive environment, while THY1 demonstrated antifibrotic properties. ZINC402830 and ZINC3830400 were screened from the ZINC database and validated through molecular docking. In the STZ mouse model, PROM1 correlated with fibrosis and diminished renal function, whereas THY1 exhibited protective effects.

conclusionPROM1 and THY1 were critical diagnostic biomarkers for renal fibrosis in DKD, with PROM1 promoting kidney fibrosis and THY1 providing protective effects. The FibrosisScore model demonstrated robust predictive performance, and molecular docking revealed potential therapeutic modulators for these targets.

Indexed as

BiomarkersDiabetic NephropathiesKidneyMolecular Targeted TherapySequence Analysis, RNASingle-Cell AnalysisAnimalsFibrosisGene Regulatory NetworksHumansMaleMiceMice, Inbred C57BLMolecular Docking SimulationReproducibility of ResultsBiomarkersBiomarkersDiabetic kidney diseaseMolecular dockingPROM1Renal fibrosisSingle-cell RNA sequencingSTZ modelTHY1

Identifiers

PMID40615901
PMCPMC12228302

What Socratic holds

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Registered trials

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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.