Prostate cancer stemness (PCS) cells have been reported to drive tumor progression, recurrence and drug resistance. However, there is lacking systematical assessment of stemlike indices and associations with immunological properties in prostate adenocarcinoma (PRAD). We thus collected 7 PRAD cohorts with 1465 men and calculated the stemlike indices for each sample using one-class logistic regression machine learning algorithm. We selected the mRNAsi to quantify the stemlike indices that correlated significantly with prognosis and accordingly identified 21 PCS-related CpG loci and 13 pivotal signature. The 13-gene based PCS model possessed high predictive significance for progression-free survival (PFS) that was trained and validated in 7 independent cohorts. Meanwhile, we conducted consensus clustering and classified the total cohorts into 5 PCS clusters with distinct outcomes. Samples in PCScluster5 possessed the highest stemness fractions and suffered from the worst prognosis. Additionally, we implemented the CIBERSORT algorithm to infer the differential abundance across 5 PCS clusters. The activated immune cells (CD8+ T cell and dendritic cells) infiltrated significantly less in PCScluster5 than other clusters, supporting the negative regulations between stemlike indices and anticancer immunity. High mRNAsi was also found to be associated with up-regulation of immunosuppressive checkpoints, like PDL1. Lastly, we used the Connectivity Map (CMap) resource to screen potential compounds for targeting PRAD stemness, including the top hits of cell cycle inhibitor and FOXM1 inhibitor. Taken together, our study comprehensively evaluated the PRAD stemlike indices based on large cohorts and established a 13-gene based classifier for predicting prognosis or potential strategies for stemness treatment.

中文翻译：

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基于大型前列腺癌队列描述肿瘤干状特征以及与危害免疫浸润的潜在关系。

据报道，前列腺癌干细胞 (PCS) 可驱动肿瘤进展、复发和耐药性。然而，缺乏对前列腺腺癌 (PRAD) 中茎样指数和免疫学特性关联的系统评估。因此，我们收集了 1465 名男性的 7 个 PRAD 队列，并使用一类逻辑回归机器学习算法计算了每个样本的茎状指数。我们选择了 mRNAsi 来量化与预后显着相关的茎样指数，并因此确定了 21 个与 PCS 相关的 CpG 基因座和 13 个关键特征。基于 13 基因的 PCS 模型对无进展生存 (PFS) 具有高度预测意义，该模型在 7 个独立队列中进行了训练和验证。同时，我们进行了共识聚类，并将总队列分为 5 个具有不同结果的 PCS 集群。PCScluster5 中的样本具有最高的干性分数，并且预后最差。此外，我们实施了 CIBERSORT 算法来推断 5 个 PCS 集群的差异丰度。活化的免疫细胞（CD8 + T 细胞和树突状细胞）在 PCScluster5 中的浸润明显少于其他簇，支持干细胞样指数和抗癌免疫之间的负调控。还发现高 mRNAsi 与免疫抑制检查点（如 PDL1）的上调有关。最后，我们使用 Connectivity Map (CMap) 资源来筛选用于靶向 PRAD 干性的潜在化合物，包括细胞周期抑制剂和 FOXM1 抑制剂的热门化合物。综合起来，