前列腺癌（PCA）是全球癌症相关死亡的第五大原因，晚期的治疗选择有限。 PCA 的免疫抑制肿瘤微环境 (TME) 导致对免疫治疗的敏感性较低。尽管分子分型有望为PCA的精准治疗提供重要线索，但目前临床仍缺乏可靠有效的分子分型方法。因此，我们的目标是提出一种新的基于干性的分类方法来指导个性化临床治疗，包括免疫治疗。对PCA进行综合多组学分析来评估干性水平的异质性。无监督层次聚类用于根据干性特征基因对 PCA 进行分类。为了使基于干性的患者分类在临床上更适用，利用四个PCA数据集和76种机器学习算法联合开发了干性亚型预测器。我们识别了包含18条信号通路的PCA干性特征，通过该特征，我们将PCA样本分为三种干性亚型通过无监督层次聚类：低干性（LS）、中干性（MS）和高干性（HS）亚型。 HS患者对雄激素剥夺疗法、紫杉烷类药物和免疫疗法敏感，并且具有最高的干细胞性、恶性程度、肿瘤突变负荷（TMB）水平、最差的预后和免疫抑制。 LS患者对铂类化疗敏感，但对免疫治疗耐药，且干细胞性、恶性度和TMB水平最低，预后最好，免疫浸润最高。 MS 患者的干性、恶性和 TMB 水平处于中间状态，预后中等。我们进一步证明这三种干性亚型在泛肿瘤中是保守的。此外，我们开发的 9 基因干性亚型预测因子具有与 18 条信号通路相当的能力，可进行肿瘤诊断并预测肿瘤复发、转移、进展、预后和不同治疗的疗效。我们确定的三种干性亚型具有潜力成为 PCA 和泛癌临床肿瘤分子分类的有力工具，并指导肿瘤患者选择免疫疗法或其他敏感治疗方法。© 2023。作者。

Prostate cancer (PCA) is the fifth leading cause of cancer-related deaths worldwide, with limited treatment options in the advanced stages. The immunosuppressive tumor microenvironment (TME) of PCA results in lower sensitivity to immunotherapy. Although molecular subtyping is expected to offer important clues for precision treatment of PCA, there is currently a shortage of dependable and effective molecular typing methods available for clinical practice. Therefore, we aim to propose a novel stemness-based classification approach to guide personalized clinical treatments, including immunotherapy.An integrative multi-omics analysis of PCA was performed to evaluate stemness-level heterogeneities. Unsupervised hierarchical clustering was used to classify PCAs based on stemness signature genes. To make stemness-based patient classification more clinically applicable, a stemness subtype predictor was jointly developed by using four PCA datasets and 76 machine learning algorithms.We identified stemness signatures of PCA comprising 18 signaling pathways, by which we classified PCA samples into three stemness subtypes via unsupervised hierarchical clustering: low stemness (LS), medium stemness (MS), and high stemness (HS) subtypes. HS patients are sensitive to androgen deprivation therapy, taxanes, and immunotherapy and have the highest stemness, malignancy, tumor mutation load (TMB) levels, worst prognosis, and immunosuppression. LS patients are sensitive to platinum-based chemotherapy but resistant to immunotherapy and have the lowest stemness, malignancy, and TMB levels, best prognosis, and the highest immune infiltration. MS patients represent an intermediate status of stemness, malignancy, and TMB levels with a moderate prognosis. We further demonstrated that these three stemness subtypes are conserved across pan-tumor. Additionally, the 9-gene stemness subtype predictor we developed has a comparable capability to 18 signaling pathways to make tumor diagnosis and to predict tumor recurrence, metastasis, progression, prognosis, and efficacy of different treatments.The three stemness subtypes we identified have the potential to be a powerful tool for clinical tumor molecular classification in PCA and pan-cancer, and to guide the selection of immunotherapy or other sensitive treatments for tumor patients.© 2023. The Author(s).