Evidence map›Paper›PMID 40588704›Full record

ArticleInternational urology and nephrology2025

Machine learning and transcriptomic analysis identify tubular injury biomarkers in patients with chronic kidney disease.

Feifei Sun, Jiahui Cai, Qiaoyun Pan, Yunbo Sun, Shasha Zhao, Weiping Liu, Qiang Tan, Yanling Yan

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Article in International urology and nephrology, 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.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Feifei SunKey Labs Nanobiotech and Applied Chemistry, Department of Biotechnology and Engineering, College of Environmental and Chemistry Engineering, Yanshan University, Qinhuangdao, 066004, China.
Jiahui CaiKey Labs Nanobiotech and Applied Chemistry, Department of Biotechnology and Engineering, College of Environmental and Chemistry Engineering, Yanshan University, Qinhuangdao, 066004, China.
Qiaoyun PanKey Labs Nanobiotech and Applied Chemistry, Department of Biotechnology and Engineering, College of Environmental and Chemistry Engineering, Yanshan University, Qinhuangdao, 066004, China.
Yunbo SunKey Labs Nanobiotech and Applied Chemistry, Department of Biotechnology and Engineering, College of Environmental and Chemistry Engineering, Yanshan University, Qinhuangdao, 066004, China.
Shasha ZhaoKey Labs Nanobiotech and Applied Chemistry, Department of Biotechnology and Engineering, College of Environmental and Chemistry Engineering, Yanshan University, Qinhuangdao, 066004, China.
Weiping LiuDivisions of Nephrology and Cardiology, The First Hospital of Qinhuangdao, Qinhuangdao, China.
Qiang TanDivisions of Nephrology and Cardiology, The First Hospital of Qinhuangdao, Qinhuangdao, China.
Yanling YanKey Labs Nanobiotech and Applied Chemistry, Department of Biotechnology and Engineering, College of Environmental and Chemistry Engineering, Yanshan University, Qinhuangdao, 066004, China. yanyanl@ysu.edu.cn.

Funding

Hebei Provincial Department of Human Resources and Social Security E2020100007S&T Program of Hebei 236Z7721G
6 · The paper itself

Abstract

purposeChronic Kidney Disease (CKD) is emerging as a major public health problem, with a lack of precise diagnostic biomarkers in clinical settings. The primary objective is to discover biomarkers for early clinical detection of CKD and to gain a deeper understanding of its underlying pathophysiological processes.

methodsSamples from renal tubules of CKD patients and healthy controls were subjected to differential expression analysis. Weighted Gene Co-expression Network Analysis (WGCNA) was utilized to detect genes associated with renal tubular damage in CKD. Subsequently, Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms were employed to identify and validate potential biomarker candidates.

resultsFour key renal biomarkers, namely DUSP1, GADD45A, TSC22D3, and ZFAND5, were successfully identified. Receiver Operating Characteristic (ROC) curve analysis and nomogram construction demonstrated their remarkable diagnostic capabilities. These biomarkers were also found to affect the degree of immune cell infiltration in CKD and exhibited a notable correlation with Glomerular Filtration Rate (GFR) and serum creatinine (SCr) levels.

conclusionThese four identified biomarkers for renal tubular injury play important roles in immune function and inflammatory responses in CKD, potentially providing a theoretical foundation for dissecting molecular mechanisms and developing therapeutic strategies in CKD.

Indexed as

Gene Expression ProfilingKidney TubulesMachine LearningRenal Insufficiency, ChronicAdultBiomarkersCase-Control StudiesCell Cycle ProteinsDual Specificity Phosphatase 1FemaleHumansMaleMiddle AgedNuclear ProteinsTranscriptomeBiomarkersCell Cycle ProteinsDual Specificity Phosphatase 1DUSP1 protein, humanNuclear ProteinsBiomarkersChronic kidney diseaseMachine learningTranscriptomic

Identifiers

PMID40588704

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.