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

被引:22
|
作者
Wang, YaXuan [1 ]
He, JiaXing [1 ]
Zhao, QingYun [1 ]
Bo, Ji [1 ]
Zhou, Yu [1 ]
Sun, HaoDong [1 ]
Ding, BeiChen [1 ]
Ren, MingHua [1 ]
机构
[1] Harbin Med Univ, Affiliated Hosp 1, Dept Urol, Harbin, Peoples R China
来源
FRONTIERS IN IMMUNOLOGY | 2024年 / 15卷
基金
中国国家自然科学基金;
关键词
prognosis; angiogenesis; machine learning; PRAD; biomarker; TUMOR ANGIOGENESIS;
D O I
10.3389/fimmu.2024.1416914
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
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.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] Identification of disulfidptosis-related subgroups and prognostic signatures in lung adenocarcinoma using machine learning and experimental validation
    Wang, Yuzhi
    Xu, Yunfei
    Liu, Chunyang
    Yuan, Chengliang
    Zhang, Yi
    FRONTIERS IN IMMUNOLOGY, 2023, 14
  • [42] Integrating machine learning to construct aberrant alternative splicing event related classifiers to predict prognosis and immunotherapy response in patients with hepatocellular carcinoma
    Liu, Wangrui
    Zhao, Shuai
    Xu, Wenhao
    Xiang, Jianfeng
    Li, Chuanyu
    Li, Jun
    Ding, Han
    Zhang, Hailiang
    Zhang, Yichi
    Huang, Haineng
    Wang, Jian
    Wang, Tao
    Zhai, Bo
    Pan, Lei
    FRONTIERS IN PHARMACOLOGY, 2022, 13
  • [43] Machine learning-based integration develops an immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy response in lung adenocarcinoma
    Sun, Jiazheng
    Guo, Hehua
    Zhang, Siyu
    Nie, Yalan
    Zhou, Sirui
    Zeng, Yulan
    Sun, Yalu
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [44] The role of mitophagy-related genes in prognosis and immunotherapy of cutaneous melanoma: a comprehensive analysis based on single-cell RNA sequencing and machine learning
    Tian, Jun
    Zhang, Lei
    Shi, Kexin
    Yang, Li
    IMMUNOLOGIC RESEARCH, 2025, 73 (01)
  • [45] Correction: Glutamine metabolism-related genes and immunotherapy in nonspecifc orbital infammation were validated using bioinformatics and machine learning
    Zixuan Wu
    Na Li
    Yuan Gao
    Liyuan Cao
    Xiaolei Yao
    Qinghua Peng
    BMC Genomics, 25
  • [46] A Prognostic Risk Score Based on Hypoxia-, Immunity-, and Epithelialto-Mesenchymal Transition-Related Genes for the Prognosis and Immunotherapy Response of Lung Adenocarcinoma
    Ouyang, Wenhao
    Jiang, Yupeng
    Bu, Shiyi
    Tang, Tiantian
    Huang, Linjie
    Chen, Ming
    Tan, Yujie
    Ou, Qiyun
    Mao, Luhui
    Mai, Yingjie
    Yao, Herui
    Yu, Yunfang
    Lin, Xiaoling
    FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY, 2022, 9
  • [47] Identification and Validation of Cuproptosis-Related LncRNA Signatures in the Prognosis and Immunotherapy of Clear Cell Renal Cell Carcinoma Using Machine Learning
    Bai, Zhixun
    Lu, Jing
    Chen, Anjian
    Zheng, Xiang
    Wu, Mingsong
    Tan, Zhouke
    Xie, Jian
    BIOMOLECULES, 2022, 12 (12)
  • [48] The integration of machine learning and multi-omics analysis provides a powerful approach to screen aging-related genes and predict prognosis and immunotherapy efficacy in hepatocellular carcinoma
    Shen, Jiahui
    Gao, Han
    Li, Bowen
    Huang, Yan
    Shi, Yinfang
    AGING-US, 2023, 15 (14): : 6848 - 6864
  • [49] A risk score model based on lipid metabolism-related genes could predict response to immunotherapy and prognosis of lung adenocarcinoma: a multi-dataset study and cytological validation
    Yangyang Lei
    Boxuan Zhou
    Xiangzhi Meng
    Mei Liang
    Weijian Song
    Yicheng Liang
    Yushun Gao
    Minghui Wang
    Discover Oncology, 14
  • [50] A risk score model based on lipid metabolism-related genes could predict response to immunotherapy and prognosis of lung adenocarcinoma: a multi-dataset study and cytological validation
    Lei, Yangyang
    Zhou, Boxuan
    Meng, Xiangzhi
    Liang, Mei
    Song, Weijian
    Liang, Yicheng
    Gao, Yushun
    Wang, Minghui
    DISCOVER ONCOLOGY, 2023, 14 (01)