Evidence map›Paper›PMID 41024137›Full record

ArticleEuropean journal of medical research2025

Machine learning-enabled identification of nucleus pulposus senescence-associated genes as potential biomarkers for intervertebral disc degeneration.

Min Xiang, Qingping Peng, Feifei Dai, Yanjiao Wu, Ling Liu, Huan Liu

Abstract read
In one paragraph

Article in European journal of medical research, 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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Min Xiang *Department of Orthopedics, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, China.
Qingping Peng *Collage of Integrated Traditional Chinese and Western Medicine, Southwest Medical University, Luzhou, 646000, Sichuan, China.
Feifei DaiSchool of Medicine, Putian University, Putian, 351100, Fujian, China.
Yanjiao WuShunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China.
Ling LiuDepartment of Pediatrics, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524000, Guangdong, China. liul37@163.com.
Huan LiuDepartment of Orthopedics, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, 646000, Sichuan, China. 20016040@163.com.

Funding

Affiliated Hospital of Guangdong Medical University Clinical Research Program LCYJ2019B009 and LCYJ2020DL01Key research and development projects of Sichuan Science and Technology Plan Project 2024YFFK0135Sichuan Province central government guide local science and technology development project 2023ZYD0072Zhanjiang Science and Technology Plan Project 2021A05078
6 · The paper itself

Abstract

backgroundIntervertebral disc degeneration (IVDD) has risen to become a global health problem, and its mechanisms are not well understood. The aim of this study was to use machine learning methods to identify key medullary senescence genes associated with IVDD and to further screen for potential biomarkers, as well as to explore the pathways by which these genes can be analyzed in both IVDD and normal states.

methodsGene expression profiles (GSE70362) of IVDD were analyzed from the extensive Gene Expression Omnibus database, and senescence-related genes (SRGs) were obtained from the cell age database. We utilized the Kyoto Encyclopedia of Genes (KEGG) and Gene Ontology (GO) databases for comprehensive functional enrichment analysis. To identify Hub SRDEGs with highly correlated IVDD features (Hub IVDD-SRDEGs), Weighted Gene Co-expression Network Analysis (WGCNA) and two machine learning methods (random forest, and support vector machine recursive feature elimination) were used. Finally, external validation was performed using quantitative polymerase chain reaction (qPCR) and Western blot experiment, and clinic samples validation was also performed using quantitative polymerase chain reaction (qPCR) experiment.

resultsWe discovered 470 DEGs in normal and IVDD nucleus pulposus samples. According to functional enrichment, DEGs are primarily associated with positive regulation of transcription from RNA polymerase II promoter, canonical Wnt signaling pathway, mitotic spindle assembly, response to organic cyclic compound, and cardiac muscle cell apoptotic process. In vivo experiments (qPCR and WB) verified the expression of TAF13 protein. Subsequent qPCR of human nucleus pulposus tissue also showed the same trend of TAF13 gene expression. Hub IVDD-SRDEGs with excellent IVDD diagnostic ability were identified as TAF13.

conclusionsNucleus pulposus aging may promote the progression of IVDD. TAF13 can be used as a novel diagnostic biomolecular marker and for IVDD.

Indexed as

Cellular SenescenceIntervertebral Disc DegenerationMachine LearningNucleus PulposusAnimalsBiomarkersGene Expression ProfilingGene Regulatory NetworksHumansBiomarkersBiomarkersCellular senescenceIntervertebral disc degenerationMachine learningNucleus pulposusTAF13

Identifiers

PMID41024137
PMCPMC12482253

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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.