Performance improvement of perovskite light-emitting devices via transfer-printed nanostructured hole transport layers.

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.