Peng Hongying, Du Dan, Hu Zhonggui, Xia Zhiliang
Department of Oncology, Second People's Hospital of China Three Gorges University, 21 Xiling One Road, Yichang, 443000, Hubei Province, China.
Department of Urology, Second People's Hospital of China Three Gorges University, 21 Xiling One Road, Yichang, 443000, Hubei Province, China.
Discov Oncol. 2025 Jul 11;16(1):1311. doi: 10.1007/s12672-025-03045-6.
Prostate cancer (PCa) represents one of the most frequently diagnosed malignancies in men worldwide, with a high incidence and mortality rate. Although significant advances have been made in early detection, therapeutic strategies for advanced and metastatic PCa remain limited. The lack of reliable biomarkers and effective targeted therapies poses a critical challenge in clinical management. This study aims to elucidate the molecular mechanisms underlying PCa progression, focusing on identifying novel biomarkers and therapeutic targets through an integrative bioinformatics approach.
We performed a comprehensive analysis of publicly available gene expression datasets (GEO and TCGA) to identify differentially expressed genes (DEGs) associated with PCa. Using advanced computational techniques such as weighted gene co-expression network analysis (WGCNA), Lasso regression, and random forest algorithms, we pinpointed key genes involved in tumorigenesis. Further, molecular docking was employed to screen for small molecules that interact with these identified genes, followed by molecular dynamics (MD) simulations to evaluate the stability and binding affinity of the most promising compounds.
Our bioinformatics analysis revealed Hepsin (HPN) as a core gene strongly associated with PCa. We observed that HPN is closely linked to immune evasion mechanisms in the tumor microenvironment, where its expression correlates with altered immune cell infiltration, particularly T cells and macrophages. In silico screening identified Bentiromide as a potent small molecule that binds to HPN with high affinity. Molecular dynamics simulations confirmed the stability of the HPN-Bentiromide complex, showing strong non-covalent interactions, including van der Waals and electrostatic forces. The binding energy analysis further validated the potential of Bentiromide as a therapeutic candidate for PCa.
This study provides valuable insights into the molecular mechanisms of PCa, identifying HPN as a pivotal gene in cancer progression and immune evasion. We demonstrate the potential of HPN as a novel biomarker and therapeutic target for PCa. Moreover, Bentiromide emerges as a promising candidate for targeted therapy, with implications not only for PCa treatment but also for other malignancies involving immune escape mechanisms. Our findings pave the way for future experimental validation and clinical trials aimed at developing HPN-targeted therapies for cancer treatment.
前列腺癌（PCa）是全球男性中最常被诊断出的恶性肿瘤之一，发病率和死亡率都很高。尽管在早期检测方面取得了重大进展，但晚期和转移性PCa的治疗策略仍然有限。缺乏可靠的生物标志物和有效的靶向治疗方法给临床管理带来了严峻挑战。本研究旨在阐明PCa进展的分子机制，重点是通过综合生物信息学方法识别新的生物标志物和治疗靶点。
我们对公开可用的基因表达数据集（GEO和TCGA）进行了全面分析，以识别与PCa相关的差异表达基因（DEGs）。使用加权基因共表达网络分析（WGCNA）、套索回归和随机森林算法等先进计算技术，我们确定了参与肿瘤发生的关键基因。此外，采用分子对接筛选与这些已识别基因相互作用的小分子，随后进行分子动力学（MD）模拟，以评估最有前景化合物的稳定性和结合亲和力。
我们的生物信息学分析表明，Hepsin（HPN）是与PCa密切相关的核心基因。我们观察到，HPN与肿瘤微环境中的免疫逃逸机制密切相关，其表达与免疫细胞浸润的改变相关，特别是T细胞和巨噬细胞。计算机模拟筛选确定苯替米特是一种与HPN具有高亲和力结合的有效小分子。分子动力学模拟证实了HPN-苯替米特复合物的稳定性，显示出强烈的非共价相互作用，包括范德华力和静电力。结合能分析进一步验证了苯替米特作为PCa治疗候选药物的潜力。
本研究为PCa的分子机制提供了有价值的见解，确定HPN是癌症进展和免疫逃逸中的关键基因。我们证明了HPN作为PCa新的生物标志物和治疗靶点的潜力。此外，苯替米特成为靶向治疗的有前景候选药物，不仅对PCa治疗有意义，而且对涉及免疫逃逸机制的其他恶性肿瘤也有意义。我们的发现为未来旨在开发针对癌症治疗的HPN靶向疗法的实验验证和临床试验铺平了道路。
