Evidence map›Paper›PMID 38968594›Full record

ArticleAging2024

Unveiling diabetic nephropathy: a novel diagnostic model through single-cell sequencing and co-expression analysis.

Guoyi Wang, Jinwen Zhao, Min Zhou, Haiyuan Lu, Fei Mao

Abstract read
In one paragraph

Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

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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

5 authors.

Guoyi WangDepartment of Nephrology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an 223300, People's Republic of China.
Jinwen ZhaoDepartment of Nephrology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an 223300, People's Republic of China.
Min ZhouDepartment of Nephrology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an 223300, People's Republic of China.
Haiyuan LuDepartment of Nephrology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an 223300, People's Republic of China.
Fei MaoDepartment of Urology, The Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huai'an 223300, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetic nephropathy (DN) is a severe complication of diabetes that affects the kidneys. Disulfidptosis, a newly defined type of programmed cell death, has emerged as a potential area of interest, yet its significance in DN remains unexplored.

methodsThis study utilized single-cell sequencing data GSE131882 from GEO database combined with bulk transcriptome sequencing data GSE30122, GSE30528 and GSE30529 to investigate disulfidptosis in DN. Single-cell sequencing analysis was performed on samples from DN patients and healthy controls, focusing on cell heterogeneity and communication. Weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA) were employed to identify disulfidptosis-related gene sets and pathways. A diagnostic model was constructed using machine learning techniques based on identified genes, and immunocorrelation analysis was conducted to explore the relationship between key genes and immune cells. PCR validation was performed on blood samples from DN patients and healthy controls.

resultsThe study revealed significant disulfidptosis heterogeneity and cell communication differences in DN. Specific targets related to disulfidptosis were identified, providing insights into the pathogenesis of DN. The diagnostic model demonstrated high accuracy in distinguishing DN from healthy samples across multiple datasets. Immunocorrelation analysis highlighted the complex interactions between immune cells and key disulfidptosis-related genes. PCR validation supported the differential expression of model genes VEGFA, MAGI2, THSD7A and ANKRD28 in DN.

conclusionThis research advances our understanding of DN by highlighting the role of disulfidptosis and identifying potential biomarkers for early detection and personalized treatment.

Indexed as

Diabetic NephropathiesSingle-Cell AnalysisCase-Control StudiesGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningTranscriptomebiomarkersdiabetic nephropathydiagnostic modelsingle-cell sequencingtranscriptome

Identifiers

PMID38968594
PMCPMC11272118

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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.