Background:
The Transforming Growth Factor-Beta (TGF-β) signaling pathway plays a crucial
role in the pathogenesis of diseases. This study aimed to identify differentially expressed TGF-β-related genes
in liver cancer patients and to correlate these findings with clinical features and immune signatures.
background:
The Transforming Growth Factor-Beta (TGF-β) signaling pathway plays a crucial role in the pathogenesis of diseases.
Methods:
The TCGA-STAD and LIRI-JP cohorts were utilized for a comprehensive analysis of TGF-β-
related genes. Differential gene expression, functional enrichment, survival analysis, and machine learning
techniques were employed to develop a prognostic model based on a TGF-β-related gene signature
(TGFBRS).
Results:
We developed a prognostic model for liver cancer based on the expression levels of nine TGF-β-
related genes. The model indicates that higher TGFBRS values are associated with poorer prognosis, higher
tumor grades, more advanced pathological stages, and resistance to chemotherapy. Additionally, the
TGFBRS-High subtype was characterized by elevated levels of immune-suppressive cells and increased expression
of immune checkpoint molecules. Using a Gradient Boosting Decision Tree (GBDT) machine learning
approach, the FKBP1A gene was identified as playing a significant role in liver cancer. Notably, knocking
down FKBP1A significantly inhibited the proliferation and metastatic capabilities of liver cancer cells both in
vitro and in vivo.
Conclusion:
Our study highlights the potential of TGFBRS in predicting chemotherapy responses and in
shaping the tumor immune microenvironment in liver cancer. The results identify FKBP1A as a promising
molecular target for developing preventive and therapeutic strategies against liver cancer. Our findings could
potentially guide personalized treatment strategies to improve the prognosis of liver cancer patients.
