Evidence map›Paper›PMID 41107987›Full record

ArticleHereditas2025

Discovering CRIP1: a novel core gene in osteoarthritis pathogenesis.

Qifan Chen, Mengliang Luo, Wenhao Kuang, Xianfang Guo, Hao Wu, Shiqi Wu, Sanmao Liu, Yueliang Wen, Chushong Zhou, Maolin He

Abstract read
In one paragraph

Article in Hereditas, 2025. 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
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0citing papers in PubMed
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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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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

10 authors.

Qifan Chen *Department of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.
Mengliang Luo *Department of Orthopedic, Center for Joint Surgery, The Second Affiliated Hospital of Chongqing Medical University, Yuzhong District, Chongqing, 400010, China.
Wenhao KuangDepartment of Spinal Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
Xianfang GuoDepartment of Neurosurgery, The Second Affiliated Hospital of Guangxi Medical University, Nanning, 530005, Guangxi Zhuang Autonomous Region, China.
Hao WuDepartment of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.
Shiqi WuDepartment of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.
Sanmao LiuDepartment of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.
Yueliang WenDepartment of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, 530021, Guangxi Zhuang Autonomous Region, China.
Chushong ZhouDepartment of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, 530021, Guangxi Zhuang Autonomous Region, China. mdzcs28@163.com.
Maolin HeDepartment of Spinal Surgery, The First Affiliated Hospital of Guangxi Medical University, Shuangyong Road 6, Nanning, 530021, Guangxi Zhuang Autonomous Region, China. hemaolin@stu.gxmu.edu.cn.

Funding

National Natural Science Foundation of China 82160536
6 · The paper itself

Abstract

backgroundOsteoarthritis (OA) is a prevalent chronic degenerative joint disease characterized by complex pathological mechanisms. This study aims to investigate core genes and their associated pathways in OA cartilage.

methodsWe integrated multiple transcriptome datasets, comprising four microarray datasets and two high-throughput datasets. Key pathways related to OA were identified through differential gene analysis, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and Gene Set Enrichment Analysis (GSEA). Subsequently, immune infiltration analysis was conducted to explore infiltration characteristics in cartilage tissue, and 113 machine learning algorithms were utilized to identify core genes. The expression of these genes was subsequently verified by qRT-PCR, and an OA diagnostic model was constructed.

resultsGSEA analysis demonstrated significant activation of the ECM-receptor interaction pathway in OA. Utilizing machine learning analysis, we identified APOD, CRIP1, and S100A4 as core genes, with APOD significantly down-regulated and CRIP1 and S100A4 significantly up-regulated. The diagnostic model based on these three genes exhibited robust predictive ability and clinical applicability.

conclusionsThis study highlights the critical role of the ECM-receptor interaction pathway in OA development and identifies APOD, CRIP1, and S100A4 as key regulatory factors. Notably, the potential role of CRIP1 warrants further investigation, providing a novel direction and theoretical foundation for future OA research.

Indexed as

OsteoarthritisGene Expression ProfilingGene OntologyHumansMachine LearningTranscriptomeBiomarkerCore geneCRIP1Diagnostic modelOsteoarthritis (OA)

Identifiers

PMID41107987
PMCPMC12535056

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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.