Evidence map›Paper›PMID 42278229›Full record

ArticleInternational journal of molecular sciences2026

Immune Infiltration and Mitochondrial Function in Diabetic Kidney Disease: WGCNA and Machine Learning Identified Hub Genes with Clinical Validation.

Suyan Duan, Qian Zhou, Ying Shi, Yuyou Ye, Hujia Hua, Dehui Liu, Yuqian Xue, Chengning Zhang, Yanggang Yuan, Changying Xing and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Suyan DuanDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Qian ZhouDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Ying ShiDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Yuyou YeDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Hujia HuaDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Dehui LiuDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Yuqian XueDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Chengning ZhangDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Yanggang YuanDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Changying XingDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
Huijuan MaoDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.ORCID 0000-0001-7410-1486
Bo ZhangDepartment of Nephrology, The First Affiliated Hospital with Nanjing Medical University, Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.ORCID 0000-0002-6902-9093

Funding

National Natural Science Foundation of China 82100767
6 · The paper itself

Abstract

Diabetic kidney disease (DKD) lacks specific biomarkers reflecting the interplay between mitochondrial dysfunction and immune microenvironment remodeling. To address this, we integrated multi-dataset transcriptomics (GEO, MitoCarta 3.0, GeneCards) with Weighted Gene Co-expression Network Analysis, protein-protein interaction networks, and machine learning algorithms to identify key diagnostic genes. Single-nucleus RNA sequencing was utilized to map cell-type distributions. Subsequently, a single-center cohort of 70 biopsy-confirmed DKD patients was enrolled for validation of the key hub gene,

Indexed as

Diabetic NephropathiesMachine LearningMitochondriaBiomarkersEpidermal Growth FactorFemaleGene Expression ProfilingGene Regulatory NetworksHistone Deacetylase 6HumansMaleProtein Interaction MapsTranscriptomeTropomyosinVascular Cell Adhesion Molecule-1BiomarkersEpidermal Growth FactorHistone Deacetylase 6TropomyosinVascular Cell Adhesion Molecule-1bioinformatics analysisclinical cohortdiabetic kidney diseaseimmune infiltrationmitochondria

Identifiers

PMID42278229
PMCPMC13256419

What Socratic 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 Socratic graph.