Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches.

Abstract

Angiogenesis, the process of forming new blood vessels from pre-existing ones, plays a crucial role in the development and advancement of cancer. Although blocking angiogenesis has shown success in treating different types of solid tumors, its relevance in prostate adenocarcinoma (PRAD) has not been thoroughly investigated. This study utilized the WGCNA method to identify angiogenesis-related genes and assessed their diagnostic and prognostic value in patients with PRAD through cluster analysis. A diagnostic model was constructed using multiple machine learning techniques, while a prognostic model was developed employing the LASSO algorithm, underscoring the relevance of angiogenesis-related genes in PRAD. Further analysis identified MAP7D3 as the most significant prognostic gene among angiogenesis-related genes using multivariate Cox regression analysis and various machine learning algorithms. The study also investigated the correlation between MAP7D3 and immune infiltration as well as drug sensitivity in PRAD. Molecular docking analysis was conducted to assess the binding affinity of MAP7D3 to angiogenic drugs. Immunohistochemistry analysis of 60 PRAD tissue samples confirmed the expression and prognostic value of MAP7D3. Overall, the study identified 10 key angiogenesis-related genes through WGCNA and demonstrated their potential prognostic and immune-related implications in PRAD patients. MAP7D3 is found to be closely associated with the prognosis of PRAD and its response to immunotherapy. Through molecular docking studies, it was revealed that MAP7D3 exhibits a high binding affinity to angiogenic drugs. Furthermore, experimental data confirmed the upregulation of MAP7D3 in PRAD, correlating with a poorer prognosis. Our study confirmed the important role of angiogenesis-related genes in PRAD and identified a new angiogenesis-related target MAP7D3.