Evidence map›Paper›PMID 36466094›Full record

ArticleOxidative medicine and cellular longevity2022

Identification of Markers for Diagnosis and Treatment of Diabetic Kidney Disease Based on the Ferroptosis and Immune.

JingYuan Ma, ChangYan Li, Tao Liu, Le Zhang, XiaoLing Wen, XiaoLing Liu, WenXing Fan

Open access · hybridAbstract read
In one paragraph

Article in Oxidative medicine and cellular longevity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
4.3field-weighted citation impact, top 4% 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

25 citing papers in PubMed, 32 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Nephroprotective Effect ofInternational journal of molecular sciences · 2025
    Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
  16. Review
  17. Article
  18. Article
  19. Review
  20. Article
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

7 authors at 2 institutions in 2 countries.

JingYuan MaDepartment of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.ORCID https://orcid.org/0000-0003-2252-5177
ChangYan LiDepartment of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.ORCID https://orcid.org/0000-0003-2565-7220
Tao LiuOrgan Transplantation Center, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.
Le ZhangInstitute for Integrative Genome Biology, University of California Riverside, Riverside, California 92521, USA.
XiaoLing WenKunming Medical University, Kunming, Yunnan 650500, China.
XiaoLing LiuDepartment of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.
WenXing FanDepartment of Nephrology, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, China.ORCID https://orcid.org/0000-0002-5526-6901
Kunming Medical University · CNUniversity of California, Riverside · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: In advanced diabetic kidney disease (DKD), iron metabolism and immune dysregulation are abnormal, but the correlation is not clear. Therefore, we aim to explore the potential mechanism of ferroptosis-related genes in DKD and their relationship with immune inflammatory response and to identify new diagnostic biomarkers to help treat and diagnose DKD. Methods: Download data from gene expression omnibus (GEO) database and FerrDb database, and construct random forest tree (RF) and support vector machine (SVM) model to screen hub ferroptosis genes (DE-FRGs). We used consistent unsupervised consensus clustering to cluster DKD samples, and enrichment analysis was performed by Gene Set Variation Analysis (GSVA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) and then assessed immune cell infiltration abundance using the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms. Ferroptosis scoring system was established based on the Boruta algorithm, and then, core compounds were screened, and binding sites were predicted by Coremine Medical database. Results: We finally established a 7-gene signature (DUSP1, PRDX6, PEBP1, ZFP36, GABARAPL1, TSC22D3, and RGS4) that exhibited good stability across different datasets. Consistent clustering analysis divided the DKD samples into two ferroptosis modification patterns. Meanwhile, autophagy and peroxisome pathways and immune-related pathways can participate in the regulation of ferroptosis modification patterns. The abundance of immune cell infiltration differs significantly across patterns. Further, molecular docking results showed that the core compound could bind to the protein encoded by the core gene. Conclusions: Our findings suggest that ferroptosis modification plays a crucial role in the diversity and complexity of the DKD immune microenvironment, and the ferroptosis score system can be used to effectively verify the relationship between ferroptosis and immune cell infiltration in DKD patients. Kaempferol and quercetin may be potential drugs to improve the immune and inflammatory mechanisms of DKD by affecting ferroptosis.

Indexed as

Diabetes MellitusDiabetic NephropathiesFerroptosisBiomarkersGene OntologyHumansMolecular Docking SimulationBiomarkers

Identifiers

PMID36466094
PMCPMC9712001
OpenAlexW4309905667

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.