The Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
Front Immunol. 2022 Aug 18;13:927565. doi: 10.3389/fimmu.2022.927565. eCollection 2022.
This study aimed to construct a tumor microenvironment (TME)-related risk model to predict the overall survival (OS) of patients with breast cancer.
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).
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.
The novel and robust TME-related risk model had a strong implication for breast cancer patients in OS, immune response, and therapeutic efficiency.
本研究旨在构建一个与肿瘤微环境（TME）相关的风险模型，以预测乳腺癌患者的总生存期（OS）。
使用癌症基因组图谱（TCGA）的基因表达数据作为训练集。进行差异表达基因分析、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以及 Wald 逐步 Cox 回归分析，以筛选与 TME 相关的风险模型。三个基因表达综合数据库用于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。通过肿瘤免疫功能障碍和排除（TIDE）评分和免疫表型评分（IPS）评估患者对化疗和免疫治疗的反应。
筛选出 5 个与 TME 相关的基因，用于构建预后特征。在训练集和验证集中，较高的 TME 风险评分与更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分期乳腺癌中的预测效率。此外，在低 TME 风险组中发现免疫检查点表达和免疫细胞浸润上调。通过 GSEA 分析证明，低 TME 风险组富集了与免疫反应功能相关的生物学过程。肿瘤突变分析以及 TIDE 和 IPS 分析表明，高 TME 风险组具有更高的肿瘤突变负担，对免疫治疗的反应更好。
该新的稳健的 TME 相关风险模型对 OS、免疫反应和治疗效率方面的乳腺癌患者具有重要意义。
