Guo Tao, Wang Jian, Meng Xiangyu, Wang Ye, Lou Yihaoyun, Ma Jianglei, Xu Shuang, Ni Xiangyu, Jia Zongming, Jin Lichen, Wang Chengyu, Chen Qingyang, Li Peng, Huang Yuhua, Ren Shancheng
Department of Urology, Changzheng Hospital, Naval Medical University, Shanghai, China.
Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Discov Oncol. 2024 Jun 4;15(1):207. doi: 10.1007/s12672-024-01006-z.
Dysregulation of zinc homeostasis is widely recognized as a hallmark feature of prostate cancer (PCa) based on the compelling clinical and experimental evidence. Nevertheless, the implications of zinc dyshomeostasis in PCa remains largely unexplored.
In this research, the zinc homeostasis pattern subtype (ZHPS) was constructed according to the profile of zinc homeostasis genes. The identified subtypes were assessed for their immune functions, mutational landscapes, biological peculiarities and drug susceptibility. Subsequently, we developed the optimal signature, known as the zinc homeostasis-related risk score (ZHRRS), using the approach won out in multifariously machine learning algorithms. Eventually, clinical specimens, Bayesian network inference and single-cell sequencing were used to excavate the underlying mechanisms of MT1A in PCa.
The zinc dyshomeostasis subgroup, ZHPS2, possessed a markedly worse prognosis than ZHPS1. Moreover, ZHPS2 demonstrated a more conspicuous genomic instability and better therapeutic responses to docetaxel and olaparib than ZHPS1. Compared with traditional clinicopathological characteristics and 35 published signatures, ZHRRS displayed a significantly improved accuracy in prognosis prediction. The diagnostic value of MT1A in PCa was substantiated through analysis of clinical samples. Additionally, we inferred and established the regulatory network of MT1A to elucidate its biological mechanisms.
The ZHPS classifier and ZHRRS model hold great potential as clinical applications for improving outcomes of PCa patients.
基于令人信服的临床和实验证据，锌稳态失调被广泛认为是前列腺癌（PCa）的一个标志性特征。然而，锌稳态失调在PCa中的影响在很大程度上仍未得到探索。
在本研究中，根据锌稳态基因的概况构建了锌稳态模式亚型（ZHPS）。对鉴定出的亚型进行免疫功能、突变图谱、生物学特性和药物敏感性评估。随后，我们使用在多种机器学习算法中胜出的方法开发了最佳特征，即锌稳态相关风险评分（ZHRRS）。最终，利用临床标本、贝叶斯网络推理和单细胞测序来挖掘MT1A在PCa中的潜在机制。
锌稳态失调亚组ZHPS2的预后明显比ZHPS1差。此外，ZHPS2表现出更明显的基因组不稳定性，并且对多西他赛和奥拉帕尼的治疗反应比ZHPS1更好。与传统临床病理特征和35个已发表的特征相比，ZHRRS在预后预测方面显示出显著提高的准确性。通过对临床样本的分析证实了MT1A在PCa中的诊断价值。此外，我们推断并建立了MT1A的调控网络以阐明其生物学机制。
ZHPS分类器和ZHRRS模型作为改善PCa患者预后的临床应用具有巨大潜力。
