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利用单细胞 RNA 测序和批量 RNA 测序探索前列腺癌中 T 细胞的关键功能及免疫微环境的调控

英文原题:Exploring the key functions of T cells and the regulation of the immune microenvironment in prostate cancer using single-cell RNA sequencing and bulk RNA sequencing.

查看英文原题

Exploring the key functions of T cells and the regulation of the immune microenvironment in prostate cancer using single-cell RNA sequencing and bulk RNA sequencing.

PubMed 2025/12/31(内容时间) Autoimmunity Q2 · IF 4.3(JCR 2025)

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中文摘要

前列腺癌的发病率持续上升,使其成为全球男性中第二常见的恶性肿瘤。免疫治疗已成为治疗肿瘤的关键策略。大量研究已证实,肿瘤免疫治疗的疗效与肿瘤微环境和T细胞亚群密切相关。

然而,某些T细胞亚群在前列腺癌中的具体功能仍未完全阐明。因此,本研究旨在系统探讨前列腺癌患者肿瘤微环境中T细胞亚群的分布模式及其与临床病理参数的相关性。

因此,我们在单细胞水平上研究了T细胞对前列腺癌肿瘤微环境的影响。我们采用了多种分析方法来揭示T细胞的功能,包括细胞相互作用分析、时间序列分析、富集分析、免疫浸润分析及其他分析方法。通过整合bulk RNA-seq数据,我们构建并验证了基于T细胞标记基因的预后风险模型。

最后,我们利用ssGSEA和ESTIMATE算法探讨了预后风险模型与免疫治疗之间的关系。经过质量控制后,单细胞数据中的16,999个细胞被保留用于下游分析。

我们的研究聚焦于T细胞,揭示了多种细胞类型与T细胞之间的通讯。拟时序分析显示,不同的T细胞标记基因在不同时间点表现出差异性表达,对应不同的生物学过程。富集分析表明,T细胞标记基因富集于多个免疫相关通路。通过我们的分析,最终确定BCAS2、EIF2S2、RIOK3和ATP6V1E1为预后标志物。免疫浸润分析显示,与低风险患者相比,高风险患者的免疫评分、基质评分和ESTIMATE评分较低,肿瘤纯度较高。

我们从多个角度分析了前列腺癌中涉及T细胞的机制,构建了预后模型,并进行了免疫浸润分析。我们的发现有助于理解前列腺癌及其预后,为前列腺癌的未来研究和预后评估提供了有价值的见解。

展开英文摘要原文

The incidence of prostate cancer continues to increase, making it the second most common malignant tumor among men worldwide. Immunotherapy has emerged as a key therapeutic strategy for treating tumors. Numerous studies have established that the efficacy of tumor immunotherapy is closely associated with the tumor microenvironment and T cell subsets.

However, the specific functions of certain T cell subsets in prostate cancer remain incompletely characterized.

Therefore, this study aimed to systematically investigate the distribution patterns of T cell subsets within the tumor microenvironment of prostate cancer patients and their correlations with clinicopathological parameters.

Therefore, we investigated the impact of T cells on the tumor microenvironment of prostate cancer at the single-cell level.

We employed a variety of analytical methods to reveal the functions of T cells, including cell interaction analysis, time-series analysis, enrichment analysis, immune infiltration analysis, and other analytical approaches. By integrating bulk RNA-seq data, we constructed and validated a prognostic risk model based on T cell marker genes.

Finally, we utilized the ssGSEA and ESTIMATE algorithms to explore the relationship between the prognostic risk model and immunotherapy. After quality control, 16,999 cells from the single-cell data were retained for downstream analysis.

Our study focused on T cells, revealing the communication between various cell types and T cells. Pseudotime analysis showed that different T cell marker genes exhibited differential expression at various time points, corresponding to distinct biological processes. Enrichment analysis indicated that T cell marker genes were enriched in several immune-related pathways.

From our analysis, BCAS2, EIF2S2, RIOK3, and ATP6V1E1 were ultimately identified as prognostic markers. Immune infiltration analysis revealed that high-risk patients had lower immune scores, stromal scores, and ESTIMATE scores and greater tumor purity compared to low-risk patients.

We analyzed the mechanisms involving T cells in prostate cancer from multiple perspectives, constructed a prognostic model, and conducted immune infiltration analysis. Our findings contribute to the understanding of prostate cancer and its prognosis, providing valuable insights for future research and prognostic assessments in prostate cancer.

论文信息

作者
Wang Z、Xing Y、Shang D、Jin X
单位
2nd Inpatient Area of Urology Department, China-Japan Union Hospital of Jilin University, Changchun, China.China
期刊
Autoimmunity2026 Dec 31
原文标识
PubMed 41474172 · DOI 10.1080/08916934.2025.2596700