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Background <p>Osteoarthritis (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.</p> Methods <p> We 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.</p> Results <p>GSEA 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.</p> Conclusions <p>This 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.</p>

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Discovering CRIP1: a novel core gene in osteoarthritis pathogenesis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月17日

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

摘要

Background

Osteoarthritis (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.

Methods

We 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.

Results

GSEA 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.

Conclusions

This 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.