Multi-omics analysis and experimental validation of the value of monocyte-associated features in prostate cancer prognosis and immunotherapy

YaXuan Wang, Chao Li, JiaXing He, QingYun Zhao, Yu Zhou, HaoDong Sun, HaiXia Zhu, Bei-chen Ding, Ming-hua Ren
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Abstract

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.
单核细胞相关特征在前列腺癌预后和免疫疗法中的价值的多组学分析和实验验证
单核细胞在肿瘤的发生和发展过程中起着至关重要的作用,但它们对前列腺癌(PRAD)的影响尚未完全明了。本研究旨在确定与单核细胞相关的关键基因,并阐明它们在前列腺癌中的作用机制。利用 TCGA-PRAD 数据集,使用 CIBERSORT 评估了免疫细胞浸润水平,并分析了它们与患者预后的相关性。WGCNA方法确定了14个关键的单核细胞相关基因。使用机器学习算法组合开发了一个以单核细胞为重点的诊断模型,同时使用 LASSO 算法创建了一个预后模型,这两个模型都经过了验证。随机森林和梯度提升机将 CCNA2 挑选为与单核细胞预后相关的最重要基因,并通过基因富集分析进一步研究了其功能。孟德尔随机分析了HLA-DR高表达单核细胞与PRAD的关联。通过分子对接评估了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鉴定出的14个单核细胞相关基因,我们结合多种机器学习算法建立了PRAD的诊断模型。此外,我们还利用 LASSO 算法构建了一个预后模型,这两种算法都表现出了卓越的预测能力。随机森林和梯度提升机器算法的分析进一步支持了CCNA2在PRAD中的潜在预后价值。基因富集分析表明,CCNA2与PRAD中细胞周期和细胞衰老的调控有关。孟德尔随机化分析证实,表达高水平HLA-DR的单核细胞可能会促进PRAD。分子对接结果表明,CCNA2 与针对 PRAD 的药物有很强的亲和力。此外,免疫组化实验验证了CCNA2在PRAD中的表达上调及其与患者预后的相关性。此外,CCNA2有望成为治疗PRAD的新型靶向药物。
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