Table3_Comprehensive analysis of the progression mechanisms of CRPC and its inhibitor discovery based on machine learning algorithms.XLSX

Background: Almost all patients treated with androgen deprivation therapy (ADT) eventually develop castration-resistant prostate cancer (CRPC). Our research aims to elucidate the potential biomarkers and molecular mechanisms that underlie the transformation of primary prostate cancer into CRPC.
Methods: We collected three microarray datasets (GSE32269, GSE74367, and GSE66187) from the Gene Expression Omnibus (GEO) database for CRPC. Differentially expressed genes (DEGs) in CRPC were identified for further analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA). Weighted gene coexpression network analysis (WGCNA) and two machine learning algorithms were employed to identify potential biomarkers for CRPC. The diagnostic efficiency of the selected biomarkers was evaluated based on gene expression level and receiver operating characteristic (ROC) curve analyses. We conducted virtual screening of drugs using AutoDock Vina. In vitro experiments were performed using the Cell Counting Kit-8 (CCK-8) assay to evaluate the inhibitory effects of the drugs on CRPC cell viability. Scratch and transwell invasion assays were employed to assess the effects of the drugs on the migration and invasion abilities of prostate cancer cells.
Results: Overall, a total of 719 DEGs, consisting of 513 upregulated and 206 downregulated genes, were identified. The biological functional enrichment analysis indicated that DEGs were mainly enriched in pathways related to the cell cycle and metabolism. CCNA2 and CKS2 were identified as promising biomarkers using a combination of WGCNA, LASSO logistic regression, SVM-RFE, and Venn diagram analyses. These potential biomarkers were further validated and exhibited a strong predictive ability. The results of the virtual screening revealed Aprepitant and Dolutegravir as the optimal targeted drugs for CCNA2 and CKS2, respectively. In vitro experiments demonstrated that both Aprepitant and Dolutegravir exerted significant inhibitory effects on CRPC cells (p < 0.05), with Aprepitant displaying a superior inhibitory effect compared to Dolutegravir.
Discussion: The expression of CCNA2 and CKS2 increases with the progression of prostate cancer, which may be one of the driving factors for the progression of prostate cancer and can serve as diagnostic biomarkers and therapeutic targets for CRPC. Additionally, Aprepitant and Dolutegravir show potential as anti-tumor drugs for CRPC.

背景：几乎所有接受雄激素剥夺治疗（androgen deprivation therapy, ADT）的患者最终都会发展为去势抵抗性前列腺癌（castration-resistant prostate cancer, CRPC）。本研究旨在阐明原发性前列腺癌向去势抵抗性前列腺癌转化的潜在生物标志物及分子机制。
方法：我们从基因表达综合数据库（Gene Expression Omnibus, GEO）中获取了3个针对CRPC的微阵列数据集，分别为GSE32269、GSE74367与GSE66187。对CRPC中的差异表达基因（differentially expressed genes, DEGs）进行鉴定以开展后续分析，包括基因本体论（Gene Ontology, GO）、京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析及基因集富集分析（gene set enrichment analysis, GSEA）。采用加权基因共表达网络分析（weighted gene coexpression network analysis, WGCNA）与两种机器学习算法筛选CRPC潜在生物标志物。基于基因表达水平与受试者工作特征（receiver operating characteristic, ROC）曲线分析评估筛选出的生物标志物的诊断效能。使用AutoDock Vina进行药物虚拟筛选。采用细胞计数试剂盒-8（Cell Counting Kit-8, CCK-8）实验检测药物对CRPC细胞活力的抑制作用。通过划痕实验与Transwell侵袭实验评估药物对前列腺癌细胞迁移及侵袭能力的影响。
结果：本研究共鉴定出719个差异表达基因，其中上调基因513个，下调基因206个。生物功能富集分析结果显示，差异表达基因主要富集于细胞周期与代谢相关通路。结合WGCNA、LASSO逻辑回归、支持向量机递归特征消除（SVM-RFE）及韦恩图分析，筛选出CCNA2与CKS2作为具有应用前景的生物标志物。上述潜在生物标志物经验证后展现出较强的预测能力。虚拟筛选结果显示，阿瑞匹坦（Aprepitant）与多替拉韦（Dolutegravir）分别为CCNA2与CKS2的最优靶向药物。体外实验结果表明，阿瑞匹坦与多替拉韦均对CRPC细胞具有显著的抑制作用（p < 0.05），且阿瑞匹坦的抑制效果优于多替拉韦。
讨论：CCNA2与CKS2的表达水平随前列腺癌进展而上调，这可能是前列腺癌进展的驱动因素之一，可作为CRPC的诊断生物标志物及治疗靶点。此外，阿瑞匹坦与多替拉韦有望成为CRPC的抗肿瘤药物。