Lu Bingnan, Liu Yifan, Ji Guo, Yao Yuntao, Yang Zhao, Zhu Bolin, Wang Lei, Dong Keqin, Li Yuanan, Shi Jiaying, He Junzhe, Huang Runzhi, Zhou Wang, Cui Xinming, Pan Xiuwu, Cui Xingang
Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China.
Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
J Transl Med. 2025 Jul 1;23(1):717. doi: 10.1186/s12967-025-06661-6.
BACKGROUND: Prostate adenocarcinoma (PRAD) is a biologically heterogeneous disease threatening the health of elderly males worldwide, and some PRAD subtypes with certain molecular landscape are always associated with poor prognosis. A more precise molecular classification system for PRAD is urgently needed. METHODS: Through spatial transcriptome analysis, we identified different malignant cell/spot types in PRAD. Then, Monocle 2 analysis was applied to identify malignant cell fates and differentiation-related genes. Together with the prognosis-related genes identified through Kaplan-Meier analysis and univariate Cox regression in TCGA-PRAD cohort, we defined malignant cell differentiation-related prognostic genes (MDPGs). Based on MDPGs, we constructed a malignant cell differentiation-based PRAD classification (MDPC) using the ConsensusClusterPlus algorithm. Then, we explored multi-omics correlations of MDPC, and constructed the regulation networks of MDPC as well as the prognostic prediction model. Finally, we validated the prognostic prediction value of MDPC through immunohistochemical staining and follow-up of a retrospective cohort. RESULTS: Three malignant spot types were identified through spatial transcriptome analysis. Then, we defined 33 MDPGs and successfully constructed MDPC with three different subtypes (DPP4MSMB MDPC, NHP2NVL MDPC, COL1A1MYLK MDPC). Apart from the correlations with tumor genomics, immunomics, MDPC also harbored convincing prognostic prediction value. In our cohort, COL1A1MYLK MDPC served as an independent risk factor of OS (hazard ratio (HR) = 20.720, P-value = 0.0018) and PFS (HR = 117.00, P-value = 0.0036), and was closely correlated with Gleason grade, WHO/ISUP grade, radiotherapy, chemotherapy, endocrinotherapy, bone metastasis before treatment, and progression after treatment. CONCLUSION: We successfully constructed MDPC with validated prognostic prediction value. This classification system provided clinicians with an effective tool to stratify PRAD patients, identifying high-risk individuals, recognizing patients prone to develop bone metastasis, and offering opportunities for early intervention to improve patients' prognosis.
背景：前列腺腺癌（PRAD）是一种生物学上异质性的疾病，威胁着全球老年男性的健康，一些具有特定分子特征的PRAD亚型总是与不良预后相关。迫切需要一种更精确的PRAD分子分类系统。 方法：通过空间转录组分析，我们在PRAD中鉴定出不同的恶性细胞/斑点类型。然后，应用Monocle 2分析来鉴定恶性细胞命运和分化相关基因。结合通过TCGA-PRAD队列中的Kaplan-Meier分析和单变量Cox回归鉴定出的预后相关基因，我们定义了恶性细胞分化相关预后基因（MDPG）。基于MDPG，我们使用ConsensusClusterPlus算法构建了基于恶性细胞分化的PRAD分类（MDPC）。然后，我们探索了MDPC的多组学相关性，并构建了MDPC的调控网络以及预后预测模型。最后，我们通过免疫组织化学染色和对一个回顾性队列的随访验证了MDPC的预后预测价值。 结果：通过空间转录组分析鉴定出三种恶性斑点类型。然后，我们定义了33个MDPG，并成功构建了具有三种不同亚型（DPP4MSMB MDPC、NHP2NVL MDPC、COL1A1MYLK MDPC）的MDPC。除了与肿瘤基因组学、免疫组学的相关性外，MDPC还具有令人信服的预后预测价值。在我们的队列中，COL1A1MYLK MDPC是总生存期（OS）（风险比（HR）=20.720，P值=0.0018）和无进展生存期（PFS）（HR=117.00，P值=0.0036）的独立危险因素，并且与Gleason分级、WHO/ISUP分级、放疗、化疗、内分泌治疗、治疗前骨转移以及治疗后进展密切相关。 结论：我们成功构建了具有验证预后预测价值的MDPC。该分类系统为临床医生提供了一种有效的工具，用于对PRAD患者进行分层，识别高危个体，识别易发生骨转移的患者，并为早期干预提供机会以改善患者预后。
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