Breast Surgery Department, The First Affiliated Hospital of Dalian Medical University, Dalian, 116000, Liaoning, China.
Gynecology and Obstetrics Department, The Second Affiliated Hospital of Dalian Medical University, Zhongshan Road 467, Dalian, 116023, Liaoning, China.
Breast Cancer. 2021 Mar;28(2):513-526. doi: 10.1007/s12282-020-01191-z. Epub 2020 Nov 27.
Breast cancer (BC), which is the most common malignant tumor in females, is associated with increasing morbidity and mortality. Effective treatments include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular-targeted therapy. With the development of molecular biology, immunology and pharmacogenomics, an increasing amount of evidence has shown that the infiltration of immune cells into the tumor microenvironment, coupled with the immune phenotype of tumor cells, will significantly affect tumor development and malignancy. Consequently, immunotherapy has become a promising treatment for BC prevention and as a modality that can influence patient prognosis.
In this study, samples collected from The Cancer Genome Atlas (TCGA) and ImmPort databases were analyzed to investigate specific immune-related genes that affect the prognosis of BC patients. In all, 64 immune-related genes related to prognosis were screened, and the 17 most representative genes were finally selected to establish the prognostic prediction model of BC (the RiskScore model) using the Lasso and StepAIC methods. By establishing a training set and a test set, the efficiency, accuracy and stability of the model in predicting and classifying the prognosis of patients were evaluated. Finally, the 17 immune-related genes were functionally annotated, and GO and KEGG signal pathway enrichment analyses were performed.
We found that these 17 genes were enriched in numerous BC- and immune microenvironment-related pathways. The relationship between the RiskScore and the clinical characteristics of the sample and signaling pathways was also analyzed.
Our findings indicate that the prognostic prediction model based on the expression profiles of 17 immune-related genes has demonstrated high predictive accuracy and stability in identifying immune features, which can guide clinicians in the diagnosis and prognostic prediction of BC patients with different immunophenotypes.
乳腺癌（BC）是女性最常见的恶性肿瘤，其发病率和死亡率呈上升趋势。有效的治疗方法包括手术、化疗、放疗、内分泌治疗和分子靶向治疗。随着分子生物学、免疫学和药物基因组学的发展，越来越多的证据表明，免疫细胞浸润肿瘤微环境，加上肿瘤细胞的免疫表型，将显著影响肿瘤的发展和恶性程度。因此，免疫疗法已成为 BC 预防的一种有前途的治疗方法，并且可以影响患者的预后。
本研究分析了来自癌症基因组图谱（TCGA）和 ImmPort 数据库的样本，以研究影响 BC 患者预后的特定免疫相关基因。共筛选出 64 个与预后相关的免疫相关基因，最终选择 17 个最具代表性的基因，采用 Lasso 和 StepAIC 方法建立 BC 预后预测模型（RiskScore 模型）。通过建立训练集和测试集，评估模型预测和分类患者预后的效率、准确性和稳定性。最后，对 17 个免疫相关基因进行功能注释，并进行 GO 和 KEGG 信号通路富集分析。
我们发现这 17 个基因富集在许多 BC 和免疫微环境相关途径中。还分析了 RiskScore 与样本临床特征和信号通路的关系。
我们的研究结果表明，基于 17 个免疫相关基因表达谱的预后预测模型在识别免疫特征方面具有较高的预测准确性和稳定性，可以指导临床医生对不同免疫表型的 BC 患者进行诊断和预后预测。
