Evidence map›Paper›PMID 40369940›Full record

ArticleRenal failure2025

Integration of mitochondrial gene expression and immune landscape in acute kidney injury prediction.

Xiaoping Xia, Renyang Liu, Xiaohui Jiang

Abstract read
In one paragraph

Article in Renal failure, 2025. 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
–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

1 citing paper in PubMed.

  1. 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

3 authors.

Xiaoping XiaDepartment of Intensive Care Unit, Taizhou Integrated Traditional Chinese and Western Medicine Hospital, Wenling, Zhejiang, China.
Renyang LiuEmergency and Critical Care Center, Intensive Care Unit, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.
Xiaohui JiangEmergency and Critical Care Center, Intensive Care Unit, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China.ORCID 0009-0009-0128-8142

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute kidney injury (AKI) is a life-threatening condition with limited early biomarkers. Mitochondrial dysfunction is central to AKI pathophysiology, yet its potential for predicting AKI remains underexplored.

methodsGene expression data from three publicly available AKI datasets (GSE30718, GSE61739, and GSE139061) were analyzed to identify differentially expressed genes (DEGs). A set of 11 mitochondrial-related genes was selected and used to construct a mitochondrial risk score (MRS) model

resultsEleven mitochondrial-related genes were consistently dysregulated across all datasets. PCA demonstrated a clear separation between AKI and normal samples. Functional enrichment analysis revealed that upregulated genes were linked to extracellular matrix remodeling and stress responses, while downregulated genes were associated with mitochondrial dysfunction. The MRS model showed strong predictive performance. We found that XRCC3 significantly promoted the activities of HK-2 cells and improved the integrity of mitochondrial structure and function

conclusionThe mitochondrial gene-based MRS model is a robust tool for predicting AKI. Our findings underscore the critical role of mitochondrial dysfunction and immune modulation in AKI, offering potential avenues for targeted therapeutic strategies.

Indexed as

Acute Kidney InjuryGenes, MitochondrialMitochondriaAnimalsBiomarkersCell LineGene Expression ProfilingHumansBiomarkersAcute kidney injurybioinformatics analysisimmune microenvironmentmitochondrialpredictive model

Identifiers

PMID40369940
PMCPMC12082736

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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.