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Characterizing hedgehog pathway features in senescence associated osteoarthritis through Integrative multi-omics and machine learning analysis.
Wang, Tao; Li, Zhengrui; Zhao, Shijian; Liu, Ying; Guo, Wenliang; Alarcòn Rodrìguez, Raquel; Wu, Yinteng; Wei, Ruqiong.
Afiliación
  • Wang T; Department of Orthopedic Joint, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Li Z; Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Zhao S; Department of Cardiology, The Affiliated Cardiovascular Hospital of Kunming Medical University (Fuwai Yunnan Cardiovascular Hospital), Kunming, Yunnan, China.
  • Liu Y; Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Guo W; Department of Rehabilitation Medicine, The Eighth Affiliated Hospital of Guangxi Medical University, Guigang, Guangxi, China.
  • Alarcòn Rodrìguez R; Faculty of Health Sciences, University of Almerìa, Almeria, Spain.
  • Wu Y; Department of Orthopedic and Trauma Surgery, the First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
  • Wei R; Department of Rehabilitation Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, China.
Front Genet ; 15: 1255455, 2024.
Article en En | MEDLINE | ID: mdl-38444758
ABSTRACT

Purpose:

Osteoarthritis (OA) is a disease of senescence and inflammation. Hedgehog's role in OA mechanisms is unclear. This study combines Bulk RNA-seq and scRNA-seq to identify Hedgehog-associated genes in OA, investigating their impact on the pathogenesis of OA. Materials and

methods:

Download and merge eight bulk-RNA seq datasets from GEO, also obtain a scRNA-seq dataset for validation and analysis. Analyze Hedgehog pathway activity in OA using bulk-RNA seq datasets. Use ten machine learning algorithms to identify important Hedgehog-associated genes, validate predictive models. Perform GSEA to investigate functional implications of identified Hedgehog-associated genes. Assess immune infiltration in OA using Cibersort and MCP-counter algorithms. Utilize ConsensusClusterPlus package to identify Hedgehog-related subgroups. Conduct WGCNA to identify key modules enriched based on Hedgehog-related subgroups. Characterization of genes by methylation and GWAS analysis. Evaluate Hedgehog pathway activity, expression of hub genes, pseudotime, and cell communication, in OA chondrocytes using scRNA-seq dataset. Validate Hedgehog-associated gene expression levels through Real-time PCR analysis.

Results:

The activity of the Hedgehog pathway is significantly enhanced in OA. Additionally, nine important Hedgehog-associated genes have been identified, and the predictive models built using these genes demonstrate strong predictive capabilities. GSEA analysis indicates a significant positive correlation between all seven important Hedgehog-associated genes and lysosomes. Consensus clustering reveals the presence of two hedgehog-related subgroups. In Cluster 1, Hedgehog pathway activity is significantly upregulated and associated with inflammatory pathways. WGCNA identifies that genes in the blue module are most significantly correlated with Cluster 1 and Cluster 2, as well as being involved in extracellular matrix and collagen-related pathways. Single-cell analysis confirms the significant upregulation of the Hedgehog pathway in OA, along with expression changes observed in 5 genes during putative temporal progression. Cell communication analysis suggests an association between low-scoring chondrocytes and macrophages.

Conclusion:

The Hedgehog pathway is significantly activated in OA and is associated with the extracellular matrix and collagen proteins. It plays a role in regulating immune cells and immune responses.
Palabras clave

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Front Genet Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Idioma: En Revista: Front Genet Año: 2024 Tipo del documento: Article País de afiliación: China