Li Jie, Liu Cun, Chen Yi, Gao Chundi, Wang Miyuan, Ma Xiaoran, Zhang Wenfeng, Zhuang Jing, Yao Yan, Sun Changgang
College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, China.
Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
Front Genet. 2019 Nov 12;10:1119. doi: 10.3389/fgene.2019.01119. eCollection 2019.
There has been increasing attention on immune-oncology for its impressive clinical benefits in many different malignancies. However, due to molecular and genetic heterogeneity of tumors, the activities of traditional clinical and pathological criteria are far from satisfactory. Immune-based strategies have re-ignited hopes for the treatment and prevention of breast cancer. Prognostic or predictive biomarkers, associated with tumor immune microenvironment, may have great prospects in guiding patient management, identifying new immune-related molecular markers, establishing personalized risk assessment of breast cancer. Therefore, in this study, weighted gene co-expression network analysis (WGCNA), single-sample gene set enrichment analysis (ssGSEA), multivariate COX analysis, least absolute shrinkage, and selection operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE) algorithm, along with a series of analyses were performed, and four immune-related genes (, , , and ) were identified as biomarkers correlated with breast cancer prognosis. The findings may provide different insights into prognostic monitoring of immune-related targets for breast cancer or can be served as reference for the further research and validation of biomarkers.
免疫肿瘤学因其在多种不同恶性肿瘤中令人瞩目的临床益处而受到越来越多的关注。然而,由于肿瘤的分子和基因异质性,传统临床和病理标准的有效性远不尽人意。基于免疫的策略重新点燃了乳腺癌治疗和预防的希望。与肿瘤免疫微环境相关的预后或预测生物标志物,在指导患者管理、识别新的免疫相关分子标志物、建立乳腺癌个性化风险评估方面可能具有广阔前景。因此,在本研究中,进行了加权基因共表达网络分析(WGCNA)、单样本基因集富集分析(ssGSEA)、多变量COX分析、最小绝对收缩和选择算子(LASSO)以及支持向量机递归特征消除(SVM-RFE)算法等一系列分析,鉴定出四个免疫相关基因(、、和)作为与乳腺癌预后相关的生物标志物。这些发现可能为乳腺癌免疫相关靶点的预后监测提供不同见解,或可为生物标志物的进一步研究和验证提供参考。