TLE4 downregulation identified by WGCNA and machine learning algorithm promotes papillary thyroid carcinoma...

Area of Science:

• Oncology
• Genetics
• Bioinformatics

Background:

• Papillary Thyroid Carcinoma (PTC) pathogenesis remains incompletely understood.
• This study investigates key genetic factors and pathways in PTC, focusing on the Transducin-Like Enhancer of Split 4 (TLE4) gene.

Purpose of the Study:

• To identify key genetic components and pathways in PTC using bioinformatics and empirical methods.
• To evaluate TLE4 as a potential diagnostic biomarker and therapeutic target for PTC.

Main Methods:

• Utilized public databases (TCGA, GEO) for differential gene expression analysis.
• Employed Weighted Gene Co-expression Network Analysis (WGCNA) for hub gene identification.
• Applied machine learning (Random Forest, LASSO, SVM-RFE) for biomarker discovery and validated findings with in vivo xenograft mouse models.

Main Results:

• Identified 244 differentially expressed genes and linked specific gene modules to PTC via WGCNA.
• TLE4 emerged as a key biomarker, with lower expression in PTC associated with immune cell infiltration and activated JAK-STAT signaling.
• Decreased TLE4 expression enhanced PTC cell growth, migration, and invasion; TLE4 overexpression inhibited tumor growth and metastasis.

Conclusions:

• TLE4 plays a critical role in PTC pathogenesis and serves as a potential biomarker and therapeutic target.
• Integrating multi-omics data offers a robust framework for understanding PTC molecular mechanisms and guiding personalized treatments.