Evidence map›Paper›PMID 40864881›Full record

ArticleJournal of cellular and molecular medicine2025

Integrating Multi-Omics Data Using Machine Learning to Explore New Therapeutic Targets for Acute Kidney Injury.

Qiming Gong, Yakun Wang, Fahui Liu, Tingting Zhou, Mengqin Tu, Junle Li, Wei Huang, Xu Lin, Wenjuan Sun

Abstract read
In one paragraph

Article in Journal of cellular and molecular medicine, 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. Article
  2. Article
  3. 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

9 authors.

Qiming GongDepartment of Nephrology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.ORCID 0000-0002-0526-7041
Yakun WangDepartment of Nephrology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Fahui LiuXiamen Cell Therapy Research Center, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
Tingting ZhouDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Mengqin TuDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Junle LiDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Wei HuangDepartment of Endocrinology and Metabolism, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China.
Xu LinDepartment of Nephrology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China.
Wenjuan SunDepartment of Nephrology, Shanghai Sixth People's Hospital, Shanghai, China.

Funding

The Fifth Academic Leader of Health System of Shanghai Qingpu District XD2023-7The Science and Technology Project of Development Committee of Shanghai Qingpu District QKY2023-13The Scientific Research Project of Shanghai Qingpu District Health and Wellness Committee QWJ2022-19The Shanghai Health Science Popularisation Youth Ability Promotion Project JKKPYC-2023-A19The Shanghai Magnolia Talent Program Pujiang Project 24PJD085The Youth Fund of Shanghai Sixth People's Hospital ynqn201313
6 · The paper itself

Abstract

Renal ischemia-reperfusion (I/R) injury is an unavoidable complication associated with renal transplantation, and currently, there are no targeted therapeutic interventions. The objective of this study was to explore the molecular mechanisms that contribute to I/R-induced acute kidney injury (I/R-AKI) and to discover potential targets for effective renal safeguarding. Bioinformatics techniques were employed to analyse critical genes regulating I/R-AKI at the single-cell level and to develop diagnostic models. Additionally, key pharmacological agents that inhibit the expression of target genes were identified for subsequent experimental validation. Pathological changes in the kidneys of I/R mice and patients with AKI were observed using immunofluorescence, western blotting, immunohistochemistry and transmission electron microscopy. We developed and validated a robust diagnostic model for I/R-AKI. The results suggest that ADAMTS1 acts as a promoter of renal I/R-AKI. In I/R-AKI, ADAMTS1 was significantly upregulated in renal tubular epithelial cells. Furthermore, apoptosis mediated by the mitochondrial pathway was a critical factor in the progression of renal I/R injury. In mouse models of I/R-AKI, the inhibition of ADAMTS1 with troglitazone significantly reduced both functional and histological damage. The diagnostic model can serve as a valuable instrument for diagnosing I/R-AKI. Furthermore, troglitazone can significantly contribute to managing I/R-AKI by inhibiting the expression of ADAMTS1. This study provides critical insights that may inform future research on therapeutic targets for renal ischaemia-reperfusion injury.

Indexed as

Acute Kidney InjuryMachine LearningReperfusion InjuryAnimalsApoptosisComputational BiologyDisease Models, AnimalHumansMaleMiceMice, Inbred C57BLMultiomicsADAMTS1apoptosisrenal ischemia–reperfusion injurysingle‐celltroglitazone

Identifiers

PMID40864881
PMCPMC12385122

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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.