单核细胞在肿瘤的发生和进展中发挥着关键作用，其对前列腺腺癌（PRAD）的影响尚未完全了解。本研究旨在鉴定关键的单核细胞相关基因并阐明其在 PRAD 中的机制。利用 TCGA-PRAD 数据集，使用 CIBERSORT 评估免疫细胞浸润水平，并分析其与患者预后的相关性。 WGCNA 方法精确定位了 14 个与单核细胞相关的关键基因。使用机器学习算法的组合开发了专注于单核细胞的诊断模型，同时使用 LASSO 算法创建了预后模型，两者均经过验证。随机森林和梯度增强机选出CCNA2作为单核细胞中与预后相关的最重要基因，并通过基因富集分析进一步研究其功能。 HLA-DR 高表达单核细胞与 PRAD 关联的孟德尔随机分析。采用分子对接评估CCNA2与PRAD靶向药物的结合亲和力，实验验证证实了CCNA2在PRAD中的表达和预后价值。基于WGCNA鉴定的14个单核细胞相关基因，我们建立了PRAD的诊断模型PRAD 使用多种机器学习算法的组合。此外，我们使用 LASSO 算法构建了预后模型，这两种算法都表现出了出色的预测能力。随机森林和梯度增强机算法的分析进一步支持了 CCNA2 在 PRAD 中的潜在预后价值。基因富集分析揭示了 CCNA2 与 PRAD 中细胞周期和细胞衰老调节的关联。孟德尔随机化分析证实，表达高水平 HLA-DR 的单核细胞可能促进 PRAD。分子对接结果表明 CCNA2 对 PRAD 药物具有很强的亲和力。此外，免疫组织化学实验验证了 PRAD 中 CCNA2 表达的上调及其与患者预后的相关性。我们的研究结果为单核细胞异质性及其在 PRAD 中的作用提供了新的见解。此外，CCNA2 具有作为 PRAD 新型靶向药物的潜力。版权所有 © 2024 Wang、Li、He、Zhao、Zhou、Sun、Zhu、Ding 和 Ren。

Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD.Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD.Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis.Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.Copyright © 2024 Wang, Li, He, Zhao, Zhou, Sun, Zhu, Ding and Ren.