A tumor microenvironment-related risk model for predicting the prognosis and tumor immunity of breast cancer patients.
A tumor microenvironment-related risk model for predicting the prognosis and tumor immunity of breast cancer patients.
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预测乳腺癌患者预后和肿瘤免疫的肿瘤微环境相关风险模型
DOI：
10.3389/fimmu.2022.927565
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发表时间：
2022
影响因子：
7.3
通讯作者：
Wu, Kejin
中科院分区：
文献类型：
--
作者：
Geng, Shengkai;Fu, Yipeng;Fu, Shaomei;Wu, Kejin
背景本研究旨在构建一个肿瘤微环境（TME）相关风险模型来预测乳腺癌患者的总生存期（OS）。方法以癌症基因组图谱中的基因表达数据为训练集。采用差异表达基因分析、预后分析、加权基因共表达网络分析、最小绝对收缩和选择算子回归分析、Wald逐步考克斯回归分析等方法筛选TME相关风险模型。使用三个基因表达综合数据库来验证预后模型的预测效率。使用基因集富集分析（GSEA）方法研究TME风险相关的生物学功能。在低和高TME风险组之间分析肿瘤免疫和突变特征。采用肿瘤免疫功能障碍和排斥（TIDE）评分和免疫表型评分（IPS）评价患者对化疗和免疫治疗的反应。结果筛选出5个TME相关基因，构建TME预后标记。在训练集和验证集中，较高的TME风险评分与较差的临床结局显著相关。相关性和分层分析也证实了TME风险模型在不同亚型和阶段乳腺癌中的预测效率。此外，免疫检查点表达和免疫细胞浸润在低TME风险组中被发现上调。通过GSEA分析，证明与免疫应答功能相关的生物学过程在低TME风险组中富集。肿瘤突变分析以及TIDE和IPS分析显示，高TME风险组具有更多的肿瘤突变负荷，对免疫治疗的反应更好。结论新的TME相关风险模型对乳腺癌患者的OS、免疫应答和治疗效果具有重要意义。
Background This study aimed to construct a tumor microenvironment (TME)-related risk model to predict the overall survival (OS) of patients with breast cancer. Methods Gene expression data from The Cancer Genome Atlas was used as the training set. Differentially expressed gene analysis, prognosis analysis, weighted gene co-expression network analysis, Least Absolute Shrinkage and Selection Operator regression analysis, and Wald stepwise Cox regression were performed to screen for the TME-related risk model. Three Gene Expression Omnibus databases were used to validate the predictive efficiency of the prognostic model. The TME-risk-related biological function was investigated using the gene set enrichment analysis (GSEA) method. Tumor immune and mutation signatures were analyzed between low- and high-TME-risk groups. The patients’ response to chemotherapy and immunotherapy were evaluated by the tumor immune dysfunction and exclusion (TIDE) score and immunophenscore (IPS). Results Five TME-related genes were screened for constructing a prognostic signature. Higher TME risk scores were significantly associated with worse clinical outcomes in the training set and the validation set. Correlation and stratification analyses also confirmed the predictive efficiency of the TME risk model in different subtypes and stages of breast cancer. Furthermore, immune checkpoint expression and immune cell infiltration were found to be upregulated in the low-TME-risk group. Biological processes related to immune response functions were proved to be enriched in the low-TME-risk group through GSEA analysis. Tumor mutation analysis and TIDE and IPS analyses showed that the high-TME-risk group had more tumor mutation burden and responded better to immunotherapy. Conclusion The novel and robust TME-related risk model had a strong implication for breast cancer patients in OS, immune response, and therapeutic efficiency.
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三阴性乳腺癌的基因组和转录组景观：亚型和治疗策略
影响因子：
50.3
作者：
Jiang, Yi-Zhou;Ma, Ding;Shao, Zhi-Ming
通讯作者：
Shao, Zhi-Ming
影响因子：
7.3
作者：
Degen M;Scherberich A;Tucker RP
通讯作者：
Tucker RP
DOI：
10.1186/s13058-016-0740-2
发表时间：
2016-08-11
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Bussard KM;Mutkus L;Stumpf K;Gomez-Manzano C;Marini FC
通讯作者：
Marini FC
影响因子：
50.3
作者：
Hanahan, Douglas;Coussens, Lisa M.
通讯作者：
Coussens, Lisa M.
影响因子：
5.2
作者：
Kim, Mirang;Kim, Jong-Hwan;Kim, Yong Sung
通讯作者：
Kim, Yong Sung