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生物信息学和机器学习的整合，以识别 CD8+T 细胞相关的预后特征，预测乳腺癌患者的临床结局和治疗反应。

Integration of Bioinformatics and Machine Learning to Identify CD8+ T Cell-Related Prognostic Signature to Predict Clinical Outcomes and Treatment Response in Breast Cancer Patients.

机构信息

Institute of Physical Education and Sport, Shanxi University, Taiyuan 030006, China.

Capital University of Physical Education and Sports, Beijing 100191, China.

出版信息

Genes (Basel). 2024 Aug 19;15(8):1093. doi: 10.3390/genes15081093.

DOI:10.3390/genes15081093
PMID:39202452
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11353403/
Abstract

UNLABELLED: The incidence of breast cancer (BC) continues to rise steadily, posing a significant burden on the public health systems of various countries worldwide. As a member of the tumor microenvironment (TME), CD8+ T cells inhibit cancer progression through their protective role. This study aims to investigate the role of CD8+ T cell-related genes (CTRGs) in breast cancer patients. METHODS: We assessed the abundance of CD8+ T cells in the TCGA and METABRIC datasets and obtained CTRGs through WGCNA. Subsequently, a prognostic signature (CTR score) was constructed from CTRGs screened by seven machine learning algorithms, and the relationship between the CTR score and TME, immunotherapy, and drug sensitivity was analyzed. Additionally, CTRGs' expression in different cells within TME was identified through single-cell analysis and spatial transcriptomics. Finally, the expression of CTRGs in clinical tissues was verified via RT-PCR. RESULTS: The CD8+ T cell-related prognostic signature consists of two CTRGs. In the TCGA and METABRIC datasets, the CTR score appeared to be negatively linked to the abundance of CD8+ T cells, and BC patients with higher risk score show a worse prognosis. The low CTR score group exhibits higher immune infiltration levels, closely associated with inhibiting the tumor microenvironment. Compared with the high CTR score group, the low CTR score group shows better responses to chemotherapy and immune checkpoint therapy. Single-cell analysis and spatial transcriptomics reveal the heterogeneity of two CTRGs in different cells. Compared with the adjacent tissues, CD163L1 and KLRB1 mRNA are downregulated in tumor tissues. CONCLUSIONS: This study establishes a robust CD8+ T cell-related prognostic signature, providing new insights for predicting the clinical outcomes and treatment responses of breast cancer patients.

摘要

目的：评估 CD8+T 细胞在 TCGA 和 METABRIC 数据集中的丰度，并通过 WGCNA 获得 CD8+T 细胞相关基因（CTRGs）。随后，我们使用七种机器学习算法筛选 CTRGs 构建预后评分（CTR 评分），并分析 CTR 评分与 TME、免疫治疗和药物敏感性的关系。此外，通过单细胞分析和空间转录组学鉴定 CTRGs 在 TME 不同细胞中的表达。最后，通过 RT-PCR 验证 CTRGs 在临床组织中的表达。

结果：CD8+T 细胞相关预后评分由两个 CTRGs 组成。在 TCGA 和 METABRIC 数据集中，CTR 评分与 CD8+T 细胞的丰度呈负相关，风险评分较高的 BC 患者预后较差。低 CTR 评分组的免疫浸润水平较高，与抑制肿瘤微环境密切相关。与高 CTR 评分组相比，低 CTR 评分组对化疗和免疫检查点治疗的反应更好。单细胞分析和空间转录组学揭示了两个 CTRGs 在不同细胞中的异质性。与相邻组织相比，肿瘤组织中 CD163L1 和 KLRB1mRNA 的表达下调。

结论：本研究建立了一个稳健的 CD8+T 细胞相关预后评分，为预测乳腺癌患者的临床结局和治疗反应提供了新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4646/11353403/2cbd2f0b5834/genes-15-01093-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4646/11353403/2cbd2f0b5834/genes-15-01093-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4646/11353403/ae7b5a63e638/genes-15-01093-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4646/11353403/06ae3e30d592/genes-15-01093-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4646/11353403/37265b2ab10a/genes-15-01093-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4646/11353403/c514da579c82/genes-15-01093-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4646/11353403/2cbd2f0b5834/genes-15-01093-g011.jpg

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引用本文的文献

[1]
Spatial Transcriptomics Decodes Breast Cancer Microenvironment Heterogeneity: From Multidimensional Dynamic Profiling to Precision Therapy Blueprint Construction.

Biomolecules. 2025-7-24

[2]
Gelsolin (GSN) as a key regulator in estrogen receptor-positive breast cancer: implications for prognosis, chemotherapy sensitivity, and immune infiltration.

Discov Oncol. 2025-7-9

本文引用的文献

[1]
Development of a CD8+ T cell associated signature for predicting the prognosis and immunological characteristics of gastric cancer by integrating single-cell and bulk RNA-sequencing.

Sci Rep. 2024-2-24

[2]
Searching for the "Holy Grail" of breast cancer recurrence risk: a narrative review of the hunt for a better biomarker and the promise of circulating tumor DNA (ctDNA).

Breast Cancer Res Treat. 2024-6

[3]
Cancer statistics, 2024.

CA Cancer J Clin. 2024

[4]
Benefits of Breast Cancer Screening and Treatment on Mortality.

JAMA. 2024-1-16

[5]
Analysis of Breast Cancer Mortality in the US-1975 to 2019.

JAMA. 2024-1-16

[6]
Breast Cancer Circulating Tumor Cells: Current Clinical Applications and Future Prospects.

Clin Chem. 2024-1-4

[7]
Early breast cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up.

Ann Oncol. 2024-2

[8]
M2 macrophage-secreted exosomes promote metastasis and increase vascular permeability in hepatocellular carcinoma.

Cell Commun Signal. 2023-10-30

[9]
Identification of signature of tumor-infiltrating CD8 T lymphocytes in prognosis and immunotherapy of colon cancer by machine learning.

Clin Immunol. 2023-12

[10]
Establishing Molecular Subgroups of CD8+ T Cell-Associated Genes in the Ovarian Cancer Tumour Microenvironment and Predicting the Immunotherapy Response.

Biomedicines. 2023-8-28

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