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Identification of Key Genes and Exploration of Therapeutic Targets for Chronic Tendon Injury Based on Bioinformatics
Zishen Cheng1, Weina Ren1, Yuqing Wang1
1Department of Orthopaedics, Bethune International Peace Hospital, Shijiazhuang, 050082, People's Republic of China.
Background:
Chronic tendon injury (CTI) is a common musculoskeletal disorder with complex molecular mechanisms, and currently lacks effective targeted therapeutic strategies. A comprehensive analysis of its key pathogenic genes and regulatory networks is crucial for the precise diagnosis and treatment of CTI.
Methods:
Differentially expressed genes (DEGs) in CTI and normal tendon tissue were identified using the GEO database, and intersected with genes derived from WGCNA to identify candidate genes. Subsequently, functional enrichment analysis was performed, and four machine learning algorithms were employed to further determine key genes. Finally, a systematic functional analysis of the key genes was performed, including assessments of diagnostic value, regulatory network construction, computational drug prediction and molecular docking.
Results:
A total of 271 candidate genes were identified, which were significantly enriched in focal adhesion, ECM-receptor interaction, and p53 signaling pathway. Subsequently, three key genes (FER, TUBA1B, and MICAL2) were prioritized through machine learning analysis, and their marked upregulation in CTI samples was verified by qRT-PCR and immunohistochemical analysis. Furthermore, their expression levels were positively correlate with natural killer T cell infiltration. TF-mRNA-miRNA regulatory network revealed the predicted TFs (such as STAT3, TFAP4, JUN, MYC) and the miRNAs that interact with the key genes. Ultimately, drug screening and molecular docking identified several potential lead compounds and confirmed their stable binding patterns.
Conclusion:
This study systematically revealed three key genes in CTI through comprehensive bioinformatics analysis. The diagnostic model, regulatory network, and predicted targeted drugs constructed based on these findings laid a solid theoretical foundation for subsequent translational medical research.
Insights
This study identified three key genes (FER, TUBA1B, MICAL2) involved in chronic tendon injury (CTI). These findings provide a foundation for developing targeted therapies for CTI.
Area of Science:
- Bioinformatics
- Molecular Biology
- Musculoskeletal Disorders
Background:
- Chronic tendon injury (CTI) is a prevalent musculoskeletal disorder with intricate molecular underpinnings.
- Current therapeutic strategies for CTI are limited, highlighting the need for identifying key pathogenic genes and regulatory networks for precise diagnosis and treatment.
Purpose of the Study:
- To identify key genes and regulatory networks implicated in chronic tendon injury (CTI).
- To establish a diagnostic model, regulatory network, and predict potential therapeutic drugs for CTI.
Main Methods:
- Differential gene expression analysis using GEO database and Weighted Gene Co-expression Network Analysis (WGCNA).
- Machine learning algorithms were employed to identify key genes from candidate genes.
- Functional enrichment analysis, diagnostic value assessment, regulatory network construction, and computational drug prediction with molecular docking were performed.
Main Results:
- 271 candidate genes were identified, enriched in focal adhesion, ECM-receptor interaction, and p53 signaling pathways.
- Three key genes (FER, TUBA1B, MICAL2) were prioritized and validated as upregulated in CTI samples.
- A regulatory network of transcription factors (TFs) and microRNAs (miRNAs) was constructed, and potential therapeutic compounds were identified.
Conclusions:
- This study systematically identified three key genes in CTI using comprehensive bioinformatics analysis.
- The developed diagnostic model, regulatory network, and predicted drugs offer a theoretical basis for future translational medical research in CTI.
