Evidence map›Paper›PMID 40593053›Full record

ArticleScientific reports2025

Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics.

ChenDeng Lao, Wei Wei, JianWen Cheng, ShiJie Liao, XiaoLin Luo, Qian Huang, HengZhen Huang, JinMin Zhao

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

ChenDeng LaoThe First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, China.
Wei WeiThe Wu Ming Hospital of Guangxi Medical University, Nanning, China.
JianWen ChengThe First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, China.
ShiJie LiaoThe First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, China.
XiaoLin LuoThe First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, China.
Qian HuangThe First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, China.
HengZhen HuangThe First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, China. 202280251@sr.gxmu.edu.cn.
JinMin ZhaoThe First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, China. csgkswk@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cholesterol metabolism-related genes (CMRGs) have been associated with osteoarthritis (OA), but their specific regulatory mechanisms remain unclear. This study aimed to investigate the role of CMRGs in OA and provide new insights into its treatment. In this study, two OA datasets, GSE55457 and GSE55235, were applied, which contained the transcriptome data of 10 OA samples and 10 control samples (synovial tissue) respectively. Using these two OA datasets and CMRGs, 21 candidate genes were identified by overlapping CMRGs and differentially expressed genes (DEGs). Protein-protein interaction networks were constructed, revealing interactions among candidate genes. Three machine learning algorithms identified ATF3, CHKA, CLU, CTNNB1, and FASN as potential biomarkers. Further evaluation in two datasets confirmed ATF3, CLU, and FASN as biomarkers, with quantitative reverse transcription polymerase chain reaction (qRT-PCR) results showing elevated CLU and decreased ATF3 and FASN expression in OA. Receiver operating characteristic (ROC) curves and a nomogram model demonstrated high accuracy in predicting OA. Among them, in the GSE55457 dataset, the Area Under the Curve (AUC) value of ATF3 was 0.78 (95% CI 0.71-0.85), the AUC value of CLU was 0.82 (95% CI 0.75-0.89), and the AUC value of FASN was 0.76 (95% CI 0.69-0.83). The AUC value of the nomogram model based on these biomarkers in the training set was 0.90 (95% CI 0.80-0.90), and the slope of the calibration curve was close to 1. Immunocorrelation analysis revealed favorable correlations between ATF3, FASN, and immune cell activities. In conclusion, ATF3, CLU, and FASN were identified as cholesterol metabolism biomarkers in OA, offering new perspectives on the relationship between CMRGs and OA.

Indexed as

BiomarkersCholesterolOsteoarthritisTranscriptomeActivating Transcription Factor 3ClusterinFatty Acid Synthase, Type IFemaleGene Expression ProfilingHumansMachine LearningMaleProtein Interaction MapsROC CurveActivating Transcription Factor 3ATF3 protein, humanBiomarkersCholesterolCLU protein, humanClusterinFASN protein, humanFatty Acid Synthase, Type IBiomarkersCholesterol metabolismGEOImmune regulationOsteoarthritis

Identifiers

PMID40593053
PMCPMC12217744

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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.