Evidence map›Paper›PMID 35372448›Full record

ArticleFrontiers in medicine2022

Machine Learning Reveals Ets2 as a Novel Target for Membranous Nephropathy Treatment and Its Role in Immune Infiltration.

Peng-Zhi Wan, Tian-Hua Xu, Bin-Yao Tian, Guang-Ying Guo, Xiao-Li Li, Li Yao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 55% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Cyclophosphamide ameliorates membranous nephropathy by upregulating miR-223 expression, promoting M2 macrophage polarization and inhibiting inflammation.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024
    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

6 authors at 2 institutions in 1 country.

Peng-Zhi WanDepartment of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, China.
Tian-Hua XuDepartment of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, China.
Bin-Yao TianDepartment of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, China.
Guang-Ying GuoDepartment of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, China.
Xiao-Li LiDepartment of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, China.
Li YaoDepartment of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang, China.
China Medical University · CNFirst Hospital of China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Membranous nephropathy (MN) is a common pathological phenotype for adult nephrotic syndrome (NS). The occurrence of MN is increasing across China, but diagnostic methods for MN still rely on kidney biopsy and PLA2R and THSD7A detection in plasma and kidney tissue, and there has been no new biomarker for MN discovered since 2014. Immune infiltration status in MN patients suffers from the dearth of associated studies. In the present study, we aimed to find new bio-markers for MN and evaluate the role of immune cells infiltration in MN pathology. Methods: We downloaded MN expression profile from the Gene Expression Omnibus database and used R-project to screen differentially expressed genes (DEGs) and performed functional correlation analysis. Least absolute shrinkage and selection operator (LASSO) logistic regression and Radom Forest algorithms were used to screen and verify the bio-markers of MN. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in MN tissues. Results: A total of 463 DEGs were screened from the MN tissue in this study. ETS2 was identified as bio-marker for MN. The CIBERSORT results showed that there were statistical differences in monocytes, plasma cells, regulatory T cells, and memory B cells. In addition, ETS2 was positively related to monocytes, M1 phase macrophages, and neutrophils and negatively correlated to plasma cells, CD4+ T memory cells, M2 macrophages, CD8+ T cells, memory B cells, and resting mast cells. Conclusion: (1) Machine learning algorithms reveals Ets2 as a novel target for membranous nephropathy patients. (2) Immune infiltration plays an important part in membranous nephropathy. (3) Ets2 expression is related to immune cells infiltration.

Indexed as

chronic kidney diseaseEts2immune infiltrationmachine learningmembranous nephropathy

Identifiers

PMID35372448
PMCPMC8971574
OpenAlexW4221028639

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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 OpenQuestion graph.