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3D 胶原纤维浓度调节三阴性乳腺癌中的 Treg 细胞浸润。

3D Collagen Fiber Concentration Regulates Treg Cell Infiltration in Triple Negative Breast Cancer.

机构信息

Department of Medical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.

出版信息

Front Immunol. 2022 Jun 14;13:904418. doi: 10.3389/fimmu.2022.904418. eCollection 2022.

DOI:10.3389/fimmu.2022.904418
PMID:35774776
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9237245/
Abstract

BACKGROUND

Triple negative breast cancer (TNBC) is characterized by poor prognosis and a lack of effective therapeutic agents owing to the absence of biomarkers. A high abundance of tumor-infiltrating regulatory T cells (Tregs) was associated with worse prognosis in malignant disease. Exploring the association between Treg cell infiltration and TNBC will provide new insights for understanding TNBC immunosuppression and may pave the way for developing novel immune-based treatments.

MATERIALS AND METHODS

Patients from TCGA were divided into Treg-high (Treg-H) and Treg-low (Treg-L) groups based on the abundance of Tregs according to CIBERSORT analysis. The association between expression level of Tregs and the clinical characteristics as well as prognosis of breast cancer were evaluated. Next, a Treg-related prognostic model was established after survival-dependent univariate Cox and LASSO regression analysis, companied with an external GEO cohort validation. Then, GO, KEGG and GSEA analyses were performed between the Treg-H and Treg-L groups. Masson and Sirius red/Fast Green staining were applied for ECM characterization. Accordingly, Jurkat T cells were encapsulated in 3D collagen to mimic the ECM microenvironment, and the expression levels of CD4, FOXP3 and CD25 were quantified according to immunofluorescence staining.

RESULTS

The expression level of Tregs is significantly associated with the clinical characteristics of breast cancer patients, and a high level of Treg cell expression indicates a poor prognosis in TNBC. To further evaluate this, a Treg-related prognostic model was established that accurately predicted outcomes in both TCGA training and GEO validation cohorts of TNBC patients. Subsequently, ECM-associated signaling pathways were identified between the Treg-H and Treg-L groups, indicating the role of ECM in Treg infiltration. Since we found increasing collagen concentrations in TNBC patients with distant migration, we encapsulated Jurkat T cells within a 3D matrix with different collagen concentrations and observed that increasing collagen concentrations promoted the expression of Treg biomarkers, supporting the regulatory role of ECM in Treg infiltration.

CONCLUSION

Our results support the association between Treg expression and breast cancer progression as well as prognosis in the TNBC subtype. Moreover, increasing collagen density may promote Treg infiltration, and thus induce an immunosuppressed TME.

摘要

背景

三阴性乳腺癌(TNBC)由于缺乏生物标志物,其预后较差,且缺乏有效的治疗药物。大量肿瘤浸润调节性 T 细胞(Tregs)与恶性疾病的预后较差相关。探索 Treg 细胞浸润与 TNBC 之间的关联,将为理解 TNBC 免疫抑制提供新的见解,并可能为开发新的免疫治疗方法铺平道路。

材料和方法

根据 CIBERSORT 分析,TCGA 中的患者根据 Treg 的丰度分为 Treg-高(Treg-H)和 Treg-低(Treg-L)组。评估 Tregs 表达水平与乳腺癌临床特征和预后的关系。然后,在生存相关单因素 Cox 和 LASSO 回归分析后,建立 Treg 相关预后模型,并结合外部 GEO 队列验证。然后,对 Treg-H 和 Treg-L 组进行 GO、KEGG 和 GSEA 分析。Masson 和 Sirius red/Fast Green 染色用于 ECM 特征描述。相应地,将 Jurkat T 细胞包埋在 3D 胶原中以模拟 ECM 微环境,并根据免疫荧光染色定量 CD4、FOXP3 和 CD25 的表达水平。

结果

Tregs 的表达水平与乳腺癌患者的临床特征显著相关,高水平的 Treg 细胞表达表明 TNBC 的预后不良。为了进一步评估这一点,建立了一个 Treg 相关的预后模型,该模型准确预测了 TCGA 训练队列和 GEO 验证队列中 TNBC 患者的结局。随后,在 Treg-H 和 Treg-L 组之间鉴定了与 ECM 相关的信号通路,表明 ECM 在 Treg 浸润中的作用。由于我们发现具有远处迁移的 TNBC 患者中的胶原浓度增加,因此我们将 Jurkat T 细胞包埋在具有不同胶原浓度的 3D 基质中,并观察到胶原浓度增加促进了 Treg 生物标志物的表达,支持 ECM 在 Treg 浸润中的调节作用。

结论

我们的研究结果支持 Treg 表达与乳腺癌进展以及 TNBC 亚型预后之间的关联。此外,增加胶原密度可能会促进 Treg 浸润,从而诱导免疫抑制的 TME。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/2947c640b4b7/fimmu-13-904418-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/2265cfc540af/fimmu-13-904418-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/0cb020d401ca/fimmu-13-904418-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/88d4f1a0f4bb/fimmu-13-904418-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/faaf3bf82c0e/fimmu-13-904418-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/1d7b74170c71/fimmu-13-904418-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/5942c01e7ed0/fimmu-13-904418-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/a2b8becb0f43/fimmu-13-904418-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/2947c640b4b7/fimmu-13-904418-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/2265cfc540af/fimmu-13-904418-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/0cb020d401ca/fimmu-13-904418-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/88d4f1a0f4bb/fimmu-13-904418-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/faaf3bf82c0e/fimmu-13-904418-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/1d7b74170c71/fimmu-13-904418-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/5942c01e7ed0/fimmu-13-904418-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/a2b8becb0f43/fimmu-13-904418-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/20bd/9237245/2947c640b4b7/fimmu-13-904418-g008.jpg

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