借力推动前列腺癌 CAR-T 细胞治疗进展
Piggybacking toward Progress for CAR T-Cell Therapy in Prostate Cancer.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
识别与 NK/T 细胞通讯相关的基因特征以评估前列腺腺癌患者的肿瘤微环境和预后结局
英文原题：Identification of the gene signatures related to NK/T cell communication to evaluate the tumor microenvironment and prognostic outcomes of patients with prostate adenocarcinoma.
查看英文原题
Identification of the gene signatures related to NK/T cell communication to evaluate the tumor microenvironment and prognostic outcomes of patients with prostate adenocarcinoma.
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研究概要
当前研究揭示了 PRAD 中的关键通讯基因，为探索新的治疗靶点提供了新的可能性。
研究思路结论见上方概要
背景
前列腺腺癌（PRAD）是男性死亡的主要原因，NK/T细胞通讯是研究的关键领域。
方法
使用Seurat包对单细胞数据进行标准化和降维，并采用CellMarker 2.0进行细胞注释。利用CellChat构建细胞亚群的配体-受体相互作用网络。通过limma包筛选差异表达基因（DEGs）。使用单因素Cox回归和glmnet包中的LASSO回归获取生物标志物并构建风险模型。采用survminer包计算最佳阈值，将患者分为高风险组和低风险组，然后进行Kaplan-Meier（KM）生存分析。使用单样本GSEA（ssGSEA）、TIMER和ESTIMATE包进行免疫浸润分析。通过GSEA对低风险组和高风险组进行通路分析。采用TIDE方法评估免疫治疗反应。此外，实施细胞学验证（定量实时PCR、CCK-8、Transwell和划痕实验）以确认特征基因对PRAD的影响。
结果
与良性组相比，NK/T细胞是肿瘤组中变化最大的细胞类型，其通讯强度在所有细胞类型中相对较高。构建的RiskScore模型如下：0.579 * FOXS1 + 0.345 * GPC6 + 0.385 * ISYNA1 + 0.418 * ITGAX + 0.792 * MGAT4B + 0.368 * PRR7 + 0.458 * REXO2。对两个风险组之间的差异分析显示，高风险组的免疫浸润水平更高，并且显著富集于免疫相关通路，而低风险组主要富集于代谢相关通路。TIDE分析表明，高风险组具有更高的免疫逃逸潜力。细胞验证实验揭示，七种生物标志物在PRAD组中表达更高。此外，ISYNA1敲低抑制了PRAD细胞的增殖、迁移和侵袭能力。
展开英文摘要原文
BACKGROUND
Prostate adenocarcinoma (PRAD) is a leading cause of male mortality, with NK/T cell communication being key areas of the research.
METHODS
The Seurat package was utilized to normalize and reduce the dimensionality of the single-cell data, and CellMarker 2.0 was employed for cell annotation. CellChat was utilized to construct the ligand-receptor interaction network of cell subsets. Differentially expressed genes (DEGs) were filtered by the limma package. Univariate Cox and the LASSO regression in the glmnet package were used to obtain biomarkers and develop a risk model. The survminer package was used to calculate the optimal threshold for dividing patients into high-risk and low-risk groups, and then Kaplan-Meier (KM) survival analysis was performed. Single-sample GSEA (ssGSEA), TIMER, and ESTIMATE packages were employed for immune infiltration analysis. Pathway analysis was conducted for the low- and high-risk groups using GSEA. Immunotherapy responses were evaluated by adopting TIDE method. Additional cellular validation (quantitative real-time PCR, CCK-8, Transwell, and scratch assay) was implemented to confirm the effects of feature genes on PRAD.
RESULTS
Compared with the benign group, NK/T cells were the cell type with the greatest changes in the tumor group, and their communication intensity was relatively high among all cell types. A RiskScore model was constructed as follows: 0.579 * F O X S 1 + 0.345 * G P C 6 + 0.385 * I S Y N A 1 + 0.418 * I T G A X + 0.792 * M G A T 4 B + 0.368 * P R R 7 + 0.458 * R E X O 2 . Analysis of the differences between the two risk groups showed that the level of immune infiltration was higher in the high-risk group, and it was significantly enriched in immune-correlated pathways, while the low-risk group was mainly enriched in metabolism-related pathways. TIDE analysis indicated that the high-risk group had higher immune escape potential. The cellular validation assays have revealed the higher expression of seven biomarkers in PRAD groups. Further, ISYNA1 knockdown inhibited the proliferation, migration, and invasion ability of PRAD cells.
CONCLUSION
The current research reveals key communication genes in PRAD, offering new possibilities for the exploration of new therapeutic targets.
论文信息
- 作者
- Zhang K、Xie H、Zhao F、Huang Y
- 单位
- Department of Urology, Affiliated Hospital of Nantong University, Nantong, China.China
- 期刊
- Frontiers in immunology2025
- 原文标识
- PubMed 40308606 · DOI 10.3389/fimmu.2025.1564784
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