Evidence map›Paper›PMID 37396184›Full record

ArticleFrontiers in endocrinology2023

Identification and analysis of cellular senescence-associated signatures in diabetic kidney disease by integrated bioinformatics analysis and machine learning.

Yuanyuan Luo, Lingxiao Zhang, Tongfeng Zhao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 7% of its field
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

19 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Renal IGFBP6 Interacts With THBS1 to Drive Renal Cellular Senescence and Fibrosis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  3. Article
  4. Review
  5. Chronic Kidney Disease and Cellular Senescence.International journal of molecular sciences · 2026
    Review
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Integrated multiomic analyses: An approach to improve understanding of diabetic kidney disease.Diabetic medicine : a journal of the British Diabetic Association · 2025
    Review
  14. Article
  15. Article
  16. Article
  17. Article
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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

3 authors at 2 institutions in 1 country.

Yuanyuan LuoDepartment of Endocrinology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Lingxiao ZhangDepartment of Endocrinology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Tongfeng ZhaoDepartment of Endocrinology, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
Sun Yat-sen University · CNThird Affiliated Hospital of Southern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Diabetic kidney disease (DKD) is a common complication of diabetes that is clinically characterized by progressive albuminuria due to glomerular destruction. The etiology of DKD is multifactorial, and numerous studies have demonstrated that cellular senescence plays a significant role in its pathogenesis, but the specific mechanism requires further investigation. Methods: This study utilized 5 datasets comprising 144 renal samples from the Gene Expression Omnibus (GEO) database. We obtained cellular senescence-related pathways from the Molecular Signatures Database and evaluated the activity of senescence pathways in DKD patients using the Gene Set Enrichment Analysis (GSEA) algorithm. Furthermore, we identified module genes related to cellular senescence pathways through Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm and used machine learning algorithms to screen for hub genes related to senescence. Subsequently, we constructed a cellular senescence-related signature (SRS) risk score based on hub genes using the Least Absolute Shrinkage and Selection Operator (LASSO), and verified mRNA levels of hub genes by RT-PCR in vivo. Finally, we validated the relationship between the SRS risk score and kidney function, as well as their association with mitochondrial function and immune infiltration. Results: The activity of cellular senescence-related pathways was found to be elevated among DKD patients. Based on 5 hub genes (LIMA1, ZFP36, FOS, IGFBP6, CKB), a cellular senescence-related signature (SRS) was constructed and validated as a risk factor for renal function decline in DKD patients. Notably, patients with high SRS risk scores exhibited extensive inhibition of mitochondrial pathways and upregulation of immune cell infiltration. Conclusion: Collectively, our findings demonstrated that cellular senescence is involved in the process of DKD, providing a novel strategy for treating DKD.

Indexed as

Diabetes MellitusDiabetic NephropathiesCellular SenescenceComputational BiologyCytoskeletal ProteinsHumansKidneyMachine LearningCytoskeletal ProteinsLIMA1 protein, humanbioinformatics analysiscellular senescencediabetic kidney diseaseimmune cell infiltrationmachine learningmitochondrial function

Identifiers

PMID37396184
PMCPMC10313062
OpenAlexW4380878352

What OpenQuestion 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 OpenQuestion graph.