Evidence mapPaperPMID 39871959Full record

ArticleJournal of inflammation research2025

Identifying Key Biomarkers Related to Immune Response in the Progression of Diabetic Kidney Disease: Mendelian Randomization Combined With Comprehensive Transcriptomics and Single-Cell Sequencing Analysis.

Miao Hu, Yi Deng, Yujie Bai, Jiayan Zhang, Xiahong Shen, Lei Shen, Ling Zhou

Abstract read
In one paragraph

Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Ubiquitination-Related Diagnostic Biomarkers for Diabetic Nephropathy: Insights From Multiomic Analysis, Drug Docking, and Experimental Validation.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
    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

7 authors.

Miao HuDepartment of Nephrology, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Yi DengDepartment of Nephrology, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Yujie BaiDepartment of Nephrology, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Jiayan ZhangDepartment of Nephrology, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Xiahong ShenDepartment of Nephrology, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Lei ShenDepartment of Nephrology, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.
Ling ZhouDepartment of Nephrology, The First Affiliated Hospital of Soochow University, Suzhou, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Renal failure related death caused by diabetic kidney disease (DKD) is an inevitable outcome for most patients. This study aimed to identify the critical genes involved in the onset and progression of DKD and to explore potential therapeutic targets of DKD. Methods: We conducted a batch of protein quantitative trait loci (pQTL) Mendelian randomization analysis to obtain a group of proteins with causal relationships with DKD and then identified key proteins through colocalization analysis to determine correlations between variant proteins and disease outcomes. Subsequently, the specific mechanisms of key regulatory genes involved in disease progression were analyzed through transcriptome and single-cell analysis. Finally, we validated the mRNA expression of five key genes in the DKD mice model using reverse transcription quantitative PCR (RT-qPCR). Results: Five characteristic genes, known as protein kinase B beta (AKT2), interleukin-2 receptor beta (IL2RB), neurexin 3(NRXN3), slit homolog 3(SLIT3), and TATA box binding protein like protein 1 (TBPL1), demonstrated causal relationships with DKD. These key genes are associated with the infiltration of immune cells, and they are related to the regulatory genes associated with immunity. In addition, we also conducted gene enrichment analysis to explore the complex network of potential signaling pathways that may regulate these key genes. Finally, we identified the effectiveness and reliability of these selected key genes through RT-qPCR in the DKD mice model. Conclusion: Our results indicated that the AKT2, IL2RB, NRXN3, SLIT3, and TBPL1 genes are closely related to DKD, which may be useful in the diagnosis and therapy of DKD.

Indexed as

biomarkerclinical correlated genesdiabetic kidney diseaseimmune cell infiltrationMendelian randomization analysis

Identifiers

PMID39871959
PMCPMC11769850

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.