International journal of biological macromolecules · 2025
Integrative analysis using machine learning and RNA sequencing identifies TGFBI as a therapeutic target in HIV-associated osteonecrosis of the femoral head.
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Yuan Wei, Zhong Chunyan, Liu Yicheng, Zhou Xiaoshu
Аннотация
BACKGROUND: This study investigates the molecular mechanisms linking Osteonecrosis of the femoral head (ONFH) and HIV, aiming to identify therapeutic targets and small molecule drugs for treating HIV-infected ONFH patients.
METHODS: We retrieved ONFH and HIV datasets from the Gene Expression Omnibus. Differential expression analysis and weighted gene co-expression network analysis were conducted to identify shared genes, followed by Gene Ontology enrichment analysis. A protein-protein interaction (PPI) network was constructed based on these genes. The LASSO and Boruta algorithms were then employed to filter key hub genes, and immune infiltration analysis was performed using CIBERSORT. Experimental validation of the analysis results was also conducted.
RESULTS: Our study identified a total of 21 shared genes. The PPI network was used to examine the relationships among these genes. The LASSO and Boruta algorithms highlighted TGFBI and PTAFR as key shared genes. Based on TGFBI, we predicted five small molecule drugs for treating ONFH secondary to HIV.
CONCLUSION: The findings from this study lay a solid foundation for enhancing treatments for patients with ONFH secondary to HIV, offering potential targets for future research and therapy development.
METHODS: We retrieved ONFH and HIV datasets from the Gene Expression Omnibus. Differential expression analysis and weighted gene co-expression network analysis were conducted to identify shared genes, followed by Gene Ontology enrichment analysis. A protein-protein interaction (PPI) network was constructed based on these genes. The LASSO and Boruta algorithms were then employed to filter key hub genes, and immune infiltration analysis was performed using CIBERSORT. Experimental validation of the analysis results was also conducted.
RESULTS: Our study identified a total of 21 shared genes. The PPI network was used to examine the relationships among these genes. The LASSO and Boruta algorithms highlighted TGFBI and PTAFR as key shared genes. Based on TGFBI, we predicted five small molecule drugs for treating ONFH secondary to HIV.
CONCLUSION: The findings from this study lay a solid foundation for enhancing treatments for patients with ONFH secondary to HIV, offering potential targets for future research and therapy development.