乳腺癌（BC）是高度恶性的肿瘤，其病死率仍然很高。免疫疗法的发展逐渐改善了患者的预后和生存率。因此，识别与BC免疫相关的分子标志物对于该疾病的治疗非常重要。本研究以癌基因组图谱-乳腺浸润性癌（TCGA-BRCA）作为训练集，以来自基因表达全景数据库的乳腺癌表达数据集作为验证集。使用加权基因共表达网络分析结合皮尔逊分析和肿瘤免疫评估资源（TIMER）获取免疫细胞相关的关键基因模块。对该模块进行基因本体论分析（GO）和京都基因与基因组百科全书（KEGG）富集分析。然后，结合Kaplan-Meier的受试者工作特征曲线用于评估模型的有效性。通过单样本基因集富集分析和CIBERSORT分析差异的免疫特征，并通过GenVisR分析基因突变频率的差异。最后，通过定量逆转录聚合酶链式反应（qRT-PCR）验证了乳腺癌细胞中预后特征基因的表达水平。本研究成功挖掘了TCGA-BRCA中的免疫相关基因模块，并建立了一个由五个基因（TNFRSF14、NFKBIA、DLG3、IRF2和CYP27A1）组成的预后模型。这个预后模型能够有效预测BC患者的预后和生存率。结果显示，高风险组中，人类白细胞抗原相关蛋白和巨噬细胞M2评分明显高表达，而CD8+ T细胞、自然杀伤细胞、M1和其他抗肿瘤细胞低表达。该模型可以作为一个独立的预后因子，用于预测BC患者的预后。qRT-PCR验证结果与数据库中的结果一致，即除了DLG3外，其他四个特征基因在BC中表达较低。本研究建立的五基因模型可以有效预测乳腺癌患者的预后和免疫模式，预计将成为乳腺癌治疗的可行分子靶点。

Breast cancer (BC) is highly malignant and its mortality rate remains high. The development of immunotherapy has gradually improved the prognosis and survival rate of patients. Therefore, identifying molecular markers concerned with BC immunity is of great importance for the treatment of this disease. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) was utilized as the training set while the BC expression dataset from the gene expression omnibus database was taken as the validation set here. Weighted gene co-expression network analysis combined with Pearson analysis and Tumor immune estimation resource (TIMER) was used to obtain immune cell-related hub gene module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on this module. Then, receiver operating characteristic curves combining Kaplan-Meier was used to evaluate the effectiveness of the model. Feature genes were screened and the independence of risk score was evaluated by univariate and multivariate Cox analyses. Differences in immune characteristics were analyzed via single-sample gene set enrichment analysis and CIBERSORT, and differences in gene mutation frequency were assessed via GenVisR analysis. Finally, the expression levels of prognostic feature genes in BC cells were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). In this study, cell immune-related gene modules in TCGA-BRCA were successfully excavated, and a five-gene (TNFRSF14, NFKBIA, DLG3, IRF2, and CYP27A1) prognostic model was established. The prognostic model could effectively forecast the prognosis and survival rate of BC patients. The result showed that human leukocyte antigen-related proteins and macrophage M2 scores were remarkably highly expressed in the high-risk group, whereas CD8+ T cells, natural killer cells, M1, and other anti-tumor cells were lowly expressed. The model could be used as an independent prognostic factor to predict the prognosis of BC patients. The results of qRT-PCR validation were consistent with the results in the database, that is, except DLG3, the other four feature genes were lowly expressed in BC. The five-gene model established in this study can predict the prognostic and immune mode of BC patients effectively, which is anticipated to become a feasible molecular target for BC therapy.