Tumor Characterization in Breast Cancer Identifies Immune-Relevant Gene Signatures Associated With Prognosis
Tumor Characterization in Breast Cancer Identifies Immune-Relevant Gene Signatures Associated With Prognosis
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DOI：
10.3389/fgene.2019.01119
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发表时间：
2019-11-12
影响因子：
3.7
通讯作者：
Sun, Changgang
中科院分区：
文献类型：
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作者：
Li, Jie;Liu, Cun;Sun, Changgang
由于免疫肿瘤学在许多不同的恶性肿瘤中具有令人印象深刻的临床疗效，人们越来越关注免疫肿瘤学。然而，由于肿瘤的分子和遗传异质性，传统的临床和病理标准的活动远远不能令人满意。基于免疫的策略重新点燃了治疗和预防乳腺癌的希望。与肿瘤免疫微环境相关的预后或预测性生物标志物在指导患者管理、发现新的免疫相关分子标志物、建立个性化乳腺癌风险评估等方面具有重要的应用前景。因此，本研究通过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、最小绝对萎缩和选择算子（LASSO）、支持向量机递归特征消除（SVM-RFE）算法等一系列分析，确定了4个免疫相关基因（APOD、CXCL14、IL33和LIFR）是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的见解，或者可以作为进一步研究和验证生物标志物的参考。
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 (APOD, CXCL14, IL33, and LIFR) 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.