运用机器学习和实验方法评估血管生成相关基因对前列腺腺癌预后和免疫治疗反应的预测价值。
Evaluating the predictive value of angiogenesis-related genes for prognosis and immunotherapy response in prostate adenocarcinoma using machine learning and experimental approaches.
机构信息
Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.
出版信息
Front Immunol. 2024 May 16;15:1416914. doi: 10.3389/fimmu.2024.1416914. eCollection 2024.
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.
背景
血管生成是指从预先存在的血管中形成新血管的过程，在癌症的发展和进展中起着至关重要的作用。尽管抑制血管生成在治疗不同类型的实体瘤方面已取得成功，但它在前列腺腺癌（PRAD）中的相关性尚未得到彻底研究。
方法
本研究利用 WGCNA 方法鉴定血管生成相关基因，并通过聚类分析评估其在 PRAD 患者中的诊断和预后价值。使用多种机器学习技术构建诊断模型，同时使用 LASSO 算法开发预后模型，强调血管生成相关基因在 PRAD 中的相关性。进一步分析使用多变量 Cox 回归分析和各种机器学习算法确定 MAP7D3 是血管生成相关基因中最重要的预后基因。该研究还探讨了 MAP7D3 与 PRAD 中免疫浸润和药物敏感性之间的相关性。进行分子对接分析以评估 MAP7D3 与血管生成药物的结合亲和力。对 60 个 PRAD 组织样本进行免疫组织化学分析证实了 MAP7D3 的表达和预后价值。
结果
总体而言，本研究通过 WGCNA 鉴定了 10 个关键的血管生成相关基因，并证明了它们在 PRAD 患者中的潜在预后和免疫相关意义。MAP7D3 与 PRAD 的预后密切相关，并且与免疫治疗的反应相关。通过分子对接研究表明，MAP7D3 与血管生成药物具有高结合亲和力。此外，实验数据证实了 MAP7D3 在 PRAD 中的上调与预后较差相关。
结论
本研究证实了血管生成相关基因在 PRAD 中的重要作用，并确定了一个新的血管生成相关靶标 MAP7D3。
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