Department of Urology, Urology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
Department of Urology, National Region Medical Centre, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, China.
Cancer Immunol Immunother. 2024 Feb 13;73(3):41. doi: 10.1007/s00262-024-03633-5.
The tumor microenvironment (TME) encompasses a variety of cells that influence immune responses and tumor growth, with tumor-associated macrophages (TAM) being a crucial component of the TME. TAM can guide prostate cancer in different directions in response to various external stimuli.
First, we downloaded prostate cancer single-cell sequencing data and second-generation sequencing data from multiple public databases. From these data, we identified characteristic genes associated with TAM clusters. We then employed machine learning techniques to select the most accurate TAM gene set and developed a TAM-related risk label for prostate cancer. We analyzed the tumor-relatedness of the TAM-related risk label and different risk groups within the population. Finally, we validated the accuracy of the prognostic label using single-cell sequencing data, qPCR, and WB assays, among other methods.
In this study, the TAM_2 cell cluster has been identified as promoting the progression of prostate cancer, possibly representing M2 macrophages. The 9 TAM feature genes selected through ten machine learning methods and demonstrated their effectiveness in predicting the progression of prostate cancer patients. Additionally, we have linked these TAM feature genes to clinical pathological characteristics, allowing us to construct a nomogram. This nomogram provides clinical practitioners with a quantitative tool for assessing the prognosis of prostate cancer patients.
This study has analyzed the potential relationship between TAM and PCa and established a TAM-related prognostic model. It holds promise as a valuable tool for the management and treatment of PCa patients.
肿瘤微环境（TME）包含多种影响免疫反应和肿瘤生长的细胞，肿瘤相关巨噬细胞（TAM）是 TME 的重要组成部分。TAM 可以根据各种外部刺激，将前列腺癌引导到不同的方向。
首先，我们从多个公共数据库下载了前列腺癌单细胞测序数据和第二代测序数据。从这些数据中，我们确定了与 TAM 簇相关的特征基因。然后，我们采用机器学习技术选择最准确的 TAM 基因集，并为前列腺癌开发了一个与 TAM 相关的风险标签。我们分析了 TAM 相关风险标签与人群中不同风险组的肿瘤相关性。最后，我们使用单细胞测序数据、qPCR 和 WB 等方法验证了预后标签的准确性。
在这项研究中，已经确定 TAM_2 细胞簇促进了前列腺癌的进展，可能代表 M2 巨噬细胞。通过十种机器学习方法选择的 9 个 TAM 特征基因，证明了它们在预测前列腺癌患者进展方面的有效性。此外，我们还将这些 TAM 特征基因与临床病理特征联系起来，构建了一个列线图。该列线图为临床医生提供了一种评估前列腺癌患者预后的定量工具。
本研究分析了 TAM 与 PCa 之间的潜在关系，并建立了一个与 TAM 相关的预后模型。它有望成为管理和治疗 PCa 患者的有价值的工具。
