Abstract

The development and progression of breast cancer (BC) depend heavily on the tumor microenvironment (TME), especially tumor infiltration leukocytes (TILs). TME-based classifications in BC remain largely unknown and need to be clarified. Using the bioinformatic analysis, we attempted to construct a prognostic nomogram based on clinical features and TME-related differentially expressed genes (DEGs). We also tried to investigate the association between the prognostic nomogram and clinical characteristics, TILs, possible signaling pathways, and response to immunotherapy in BC patients. DEGs for BC patients were identified from The Cancer Genome Atlas Breast Invasive Carcinoma database. TME-related genes were downloaded from the Immunology Database and Analysis Portal. After intersecting DEGs and TME-related genes, 3985 overlapping TME-related DEGs were selected for non-negative matrix factorization clustering, microenvironment cell populations-counter (MCP-counter), LASSO Cox regression, tumor immune dysfunction, and exclusion (TIDE) algorithm analyses. BC patients were divided into three clusters based on the TME-related DEGs and survival data, in which cluster 3 had the best overall survival (OS). Of note, cluster 3 exhibited the highest infiltration or lowest infiltration of CD³⁺ T-cells, CD⁸⁺ T-cells, cytotoxic lymphocytes, B-lymphocytes, monocytic lineage, and myeloid dendritic cells (MDCs). A total of 33 TME-related DEGs were identified as a prognostic gene signature by the LASSO regression analysis. The prognostic gene signature separated BC patients into low- and high-risk groups with significant differences in OS (p<0.01) and demonstrated powerful effectiveness (TCGA all group: 1-year area under the curve [AUC] = 0.773, 3-year AUC = 0.770, 5-year AUC = 0.792). By integrating demographic features, tumor-node metastasis (TNM) stages, and prognostic gene signature, we constructed a nomogram with better predictive value than other clinical features alone. TME-related DEGs in the low-risk BC patients (with better OS) were enriched in chemokine, cytokine–cytokine receptor interaction, and JAK-STAT and Toll-like receptor signaling pathways. BC patients in the low-risk group exhibited higher TIDE scores associated with worse immune checkpoint blockade response. A prognostic nomogram based on TME-related DEGs and clinical characteristics could predict prognosis and guide immunotherapy in BC patients.

中文翻译：

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基于肿瘤免疫微环境的簇在预测乳腺癌预后和指导免疫治疗中的作用

摘要

乳腺癌（BC）的发生和进展在很大程度上取决于肿瘤微环境（TME），尤其是肿瘤浸润白细胞（TIL）。BC 省基于 TME 的分类在很大程度上仍然未知，需要澄清。利用生物信息学分析，我们尝试根据临床特征和 TME 相关差异表达基因 (DEG) 构建预后列线图。我们还尝试研究 BC 患者的预后列线图与临床特征、TIL、可能的信号通路以及免疫治疗反应之间的关联。BC 患者的 DEG 是从癌症基因组图谱乳腺癌浸润性癌数据库中确定的。TME 相关基因是从免疫学数据库和分析门户下载的。将DEG和TME相关基因相交后，选择3985个重叠的TME相关DEG进行非负矩阵分解聚类、微环境细胞群体计数（MCP-counter）、LASSO Cox回归、肿瘤免疫功能障碍和排除（TIDE）算法分析。根据 TME 相关 DEG 和生存数据将 BC 患者分为三个组，其中第 3 组具有最佳的总生存期 (OS)。值得注意的是，簇 3 表现出 CD ³⁺ T 细胞、CD ⁸⁺ T 细胞、细胞毒性淋巴细胞、B 淋巴细胞、单核细胞谱系和骨髓树突细胞 (MDC) 的最高浸润或最低浸润。通过 LASSO 回归分析，总共 33 个 TME 相关 DEG 被鉴定为预后基因特征。预后基因特征将 BC 患者分为低风险组和高风险组，OS 差异显着 ( p <0.01)，并显示出强大的有效性（TCGA 所有组：1 年曲线下面积 [AUC] = 0.773，3 年AUC = 0.770，5 年 AUC = 0.792）。通过整合人口特征、肿瘤淋巴结转移 (TNM) 分期和预后基因特征，我们构建了一个比单独使用其他临床特征具有更好预测价值的列线图。低风险 BC 患者（OS 较好）中的 TME 相关 DEG 富含趋化因子、细胞因子-细胞因子受体相互作用以及 JAK-STAT 和 Toll 样受体信号通路。低风险组的 BC 患者表现出较高的 TIDE 评分，与较差的免疫检查点阻断反应相关。基于 TME 相关 DEG 和临床特征的预后列线图可以预测 BC 患者的预后并指导免疫治疗。