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

Abstract

Background: 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.

Method: 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.

Result: 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.

Conclusion: Our study confirmed the important role of angiogenesis-related genes in PRAD and identified a new angiogenesis-related target MAP7D3.

Keywords: PRAD; angiogenesis; biomarker; machine learning; prognosis.

Figure 1

WGCNA algorithm screens angiogenesis-related genes. (A) WGCNA Network Construction Parameters. (B) The upper part of the figure shows the gene clustering tree constructed on the weighted correlation coefficients, and the lower part of the figure is divided into the distribution of genes in each module. (C) Heatmap of trait module associations. (D) Scatterplot of Angiogenesis and Module Gene Association. (E) TCGA-PRAD dataset variance analysis volcano plot. (F) Venn diagrams to map angiogenic prognostic differential genes.

Figure 2

Cluster analysis of PRAD patients based on angiogenesis genes. (A) Cumulative distribution curve. (B) Clustering heatmap. (C) Evaluation of average consistency within clustered groups. (D) Differential expression of angiogenesis-related genes in clusters. (E, F) Differences in overall and disease- specific survival between clusters. (G, H) Analysis of different levels of immune cell infiltration between clusters. (I) Analysis of expression levels of different immunoinhibitors among clusters. (J) Analysis of differences in IC50 scores of different chemotherapeutic agents between clusters. (K, L) Analysis of gene enrichment between clusters. *p < 0.05, **p < 0.01 and ***p < 0.001.

Figure 3

Construction of diagnostic models based on integrated machine learning models. (A) Predictive effectiveness of different algorithm combinations for PRAD diagnosis. (B) Number of genes incorporated by different combinations of algorithms.

Figure 4

Constructing prognostic models based on angiogenesis genes. (A, B) 9 angiogenesis-related prognostic genes were included in the prognostic model. (C) The top represents the scatter plot of the Riskscore from low to high, the middle represents the scatter plot distribution of survival time and survival status corresponding to the Riskscore of different samples; the bottom represents the expression heat map of the genes included in the model. (D) Prognostic differences between high and low risk groups. (E–G) Prognostic modeling for predictive analysis of 1,3,5-year prognosis in patients with PRAD.

Figure 5

Prognostic models are strongly associated with PRAD chemotherapy and immunotherapy. (A) Analysis of the difference in IC50 scores of different chemotherapeutic drugs between high and low risk groups. (B, C) Analysis of immune cell infiltration levels between different groups. (D) Expression level analysis of different immunoinhibitors between high and low risk groups. (E) Network diagram of correlation between risk score and PRAD immune infiltration. *p < 0.05, **p < 0.01 and ***p < 0.001.

Figure 6

Multiple machine learning approaches to identify the best angiogenesis-related prognostic genes in PRAD. (A) Univariate COX regression analysis of prognostic differences in relevant indicators. (B) Multivariate COX regression to analyze prognostic differences in relevant indicators. (C–E) RF, XGBoost and GBM algorithms to screen prognostic genes. (F) Gene enrichment analysis based on MAP7D3 expression.

Figure 7

MAP7D3 was significantly associated with PRAD immunotherapy and chemotherapy. (A) Analysis of MAP7D3 correlation with PRAD immune cell infiltration based on XCELL algorithm. (B, C) Analysis of MAP7D3 correlation with PRAD immune cell infiltration based on single-cell dataset. (D) Analysis of MAP7D3 correlation with immunoinhibitor-related genes. (E) Analysis of the difference in IC50 scores of different chemotherapeutic drugs between high MAP7D3 expression and low MAP7D3 expression groups. (F) Network diagram of correlation between MAP7D3 expression and PRAD immune infiltration. *p < 0.05, **p < 0.01 and ***p < 0.001.

Figure 8

MAP7D3 is associated with angiogenesis drugs. (A–E) Molecular docking of MAP7D3 with the angiogenesis-targeting drugs sunitinib, Vandetanib, Thalidomide, Lenalidomide, and Cabozantinib.

Figure 9

MAP7D3 is highly expressed in PRAD and may serve as a prognostic marker. (A–E) MAP7D3 expression in PRAD and corresponding normal tissues. (F–H) Correlation between MAP7D3 expression and different pathologic parameters in PRAD patients. (I) MAP7D3 expression and prognostic KM curves in PRAD patients. (J) Predictive ability of MAP7D3 expression for prognosis in PRAD patients. (K) Predictive ability of MAP7D3 expression for the diagnosis of PRAD patients. ***p < 0.001.