Evidence mapPaperPMID 40243723Full record

ArticleInternational journal of molecular sciences2025

PTEN: A Novel Diabetes Nephropathy Protective Gene Related to Cellular Senescence.

Kang Li, Huidi Tang, Xiaoqing Cao, Xiaoli Zhang, Xiaojie Wang

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Chronic Kidney Disease and Cellular Senescence.International journal of molecular sciences · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Review
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

5 authors.

Kang LiDepartment of Pharmacology, School of Basic Medical Sciences, Shandong University, Jinan 250012, China.ORCID 0009-0007-0610-1864
Huidi TangDepartment of Pharmacology, School of Basic Medical Sciences, Shandong University, Jinan 250012, China.
Xiaoqing CaoDepartment of Cardiology, Shandong Public Health Clinical Center, Shandong University, Jinan 250013, China.
Xiaoli ZhangKey Laboratory of the Ministry of Education for Experimental Teratology, Department of Histology and Embryology, School of Basic Medical Sciences, Shandong University, Jinan 250012, China.ORCID 0000-0002-5092-4565
Xiaojie WangDepartment of Pharmacology, School of Basic Medical Sciences, Shandong University, Jinan 250012, China.

Funding

Shandong Provincial Natural Science Foundation ZR202102240178The National Nature Science Foundation of China 82170734
6 · The paper itself

Abstract

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease (ESRD). The current diagnostic and therapeutic approaches need to be improved. Cellular senescence has been implicated in the pathogenesis of DN, but its precise role remains unclear. This study aimed to identify key pathogenic genes related to cellular senescence in DN and explore their potential as diagnostic biomarkers. Using transcriptomic data from GEO datasets (GSE96804, GSE30122, GSE142025, and GSE104948) and cellular senescence-related genes sourced from the GenAge database, we integrated multiple bioinformatics approaches, including differential expression analysis, weighted gene co-expression network analysis (WGCNA), machine learning and protein-protein interaction (PPI), to identify diagnostic genes.

Indexed as

Cellular SenescenceDiabetic NephropathiesPTEN PhosphohydrolaseAnimalsBiomarkersComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansMesangial CellsMiceProtein Interaction MapsBiomarkersPTEN PhosphohydrolasePTEN protein, humanclustering analysisdiabetic nephropathymachine learningPTENWGCNA

Identifiers

PMID40243723
PMCPMC11988946

What Socratic holds

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LicenceCC BY
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Registered trials

None linked

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.