前列腺癌（PCa）是一种全球流行的恶性肿瘤，其上皮细胞内表现出复杂的异质性，与疾病进展和免疫调节密切相关。然而，与这些细胞相关的基因和生物标志物的临床意义仍未得到充分探索。为了解决这一差距，本研究旨在全面研究上皮细胞相关基因在 PCa 中的作用和临床价值。利用 GS​​E176031 的单细胞测序数据，我们进行了广泛的分析，以确定上皮细胞标记基因 (ECMG)。采用共识聚类分析，我们评估了 PCa 中 ECMG、预后和免疫反应之间的相关性。随后，我们通过对 5 个独立队列中使用 10 种机器学习算法的 101 个模型进行协同分析，开发并验证了一种最佳预后特征，称为上皮细胞标记基因预后特征 (ECMGPS)。此外，我们还从文献中收集了临床特征和先前发表的签名以进行比较分析。此外，我们利用多组学分析和 IMvigor 队列探讨了 ECMGPS 在免疫治疗和药物选择中的临床效用。最后，我们利用公共数据库和实验研究了 PCa 中的枢纽基因跨膜 p24 运输蛋白 3 (TMED3) 的生物学功能。我们鉴定了一组全面的 543 个 ECMG，并建立了 ECMG 与预后评估和预后评估之间的强相关性。 PCa 的免疫分类。值得注意的是，ECMGPS 表现出了强大的预测能力，在五个队列的独立性和准确性方面超越了传统的临床特征和 80 个已发布的签名。值得注意的是，ECMGPS 在识别可能受益于免疫疗法和个性化医疗的潜在 PCa 患者方面表现出了巨大的前景，从而使我们更接近于为个体量身定制的治疗方法。此外，TMED3 在促进 PCa 细胞恶性增殖中的作用得到了验证。我们的研究结果强调了 ECMGPS 作为改善 PCa 患者预后的强大工具，并为深入检查 PCa 复杂性提供了强大的概念框架。同时，我们的研究有可能开发出一种新颖的 PCa 诊断和预测替代方案。© 2023。作者。

Prostate cancer (PCa), a globally prevalent malignancy, displays intricate heterogeneity within its epithelial cells, closely linked with disease progression and immune modulation. However, the clinical significance of genes and biomarkers associated with these cells remains inadequately explored. To address this gap, this study aimed to comprehensively investigate the roles and clinical value of epithelial cell-related genes in PCa.Leveraging single-cell sequencing data from GSE176031, we conducted an extensive analysis to identify epithelial cell marker genes (ECMGs). Employing consensus clustering analysis, we evaluated the correlations between ECMGs, prognosis, and immune responses in PCa. Subsequently, we developed and validated an optimal prognostic signature, termed the epithelial cell marker gene prognostic signature (ECMGPS), through synergistic analysis from 101 models employing 10 machine learning algorithms across five independent cohorts. Additionally, we collected clinical features and previously published signatures from the literature for comparative analysis. Furthermore, we explored the clinical utility of ECMGPS in immunotherapy and drug selection using multi-omics analysis and the IMvigor cohort. Finally, we investigated the biological functions of the hub gene, transmembrane p24 trafficking protein 3 (TMED3), in PCa using public databases and experiments.We identified a comprehensive set of 543 ECMGs and established a strong correlation between ECMGs and both the prognostic evaluation and immune classification in PCa. Notably, ECMGPS exhibited robust predictive capability, surpassing traditional clinical features and 80 published signatures in terms of both independence and accuracy across five cohorts. Significantly, ECMGPS demonstrated significant promise in identifying potential PCa patients who might benefit from immunotherapy and personalized medicine, thereby moving us nearer to tailored therapeutic approaches for individuals. Moreover, the role of TMED3 in promoting malignant proliferation of PCa cells was validated.Our findings highlight ECMGPS as a powerful tool for improving PCa patient outcomes and supply a robust conceptual framework for in-depth examination of PCa complexities. Simultaneously, our study has the potential to develop a novel alternative for PCa diagnosis and prognostication.© 2023. The Author(s).