Jiang Jialing, Zhao Yi
Department of Oncology, Shengjing Hospital of China Medical University, Shenyang 110004, China.
J Oncol. 2021 Mar 27;2021:5533923. doi: 10.1155/2021/5533923. eCollection 2021.
To identify CD8+ T lymphocyte-related coexpressed genes that increase CD8+ T lymphocyte proportions in breast cancer and to elucidate the underlying mechanisms among relevant genes in the tumor microenvironment.
We obtained breast cancer expression matrix data and patient phenotype following information from TCGA-BRCA FPKM. Tumor purity, immune score, stromal score, and estimate score were calculated using the estimate package in R. The CD8 T lymphocyte proportions in each breast carcinoma sample were estimated using the CIBERSORT algorithm. The samples with < 0.05 were considered to be significant and were taken into the weighted gene coexpression network analysis. Based on the CD8 T lymphocyte proportion and tumor purity, we generated CD8 T lymphocyte coexpression networks and selected the most CD8 T lymphocyte-related module as our interested coexpression modules. We constructed a CD8+ T cell model based on the least absolute shrinkage and selection operator method (LASSO) regression model and robust model and evaluate the prediction ability in different subgroups.
A breast carcinoma CD8+ T lymphocyte proportion coexpression yellow module was determined. The coexpression genes in the yellow module were determined to increase the CD8+ T lymphocyte proportion levels in breast cancer patients. The yellow module was significantly enriched in the antigen presentation process, cellular response to interferon-gamma, and leukocyte proliferation. Subsequently, we generated CD8+ T cell-related genes lasso regression risk model and robust model, and eight genes were taken into the risk model. The risk score showed significant prognostic ability in various subgroups. Expression levels of proteins, encoded by CD74, were lower in the breast carcinoma samples than in normal tissue, suggesting expression differences at both the mRNA and the protein levels.
These eight CD8+ T lymphocyte proportion coexpression genes increase CD8 T lymphocyte in breast cancer by an antigen presentation process. The mechanism might suggest new pathways to improve outcomes in patients who do not benefit from immune therapy.
鉴定可增加乳腺癌中CD8 + T淋巴细胞比例的CD8 + T淋巴细胞相关共表达基因,并阐明肿瘤微环境中相关基因的潜在机制。
我们从TCGA - BRCA FPKM获得乳腺癌表达矩阵数据和患者表型后续信息。使用R中的estimate包计算肿瘤纯度、免疫评分、基质评分和估计评分。使用CIBERSORT算法估计每个乳腺癌样本中的CD8 T淋巴细胞比例。P < 0.05的样本被认为具有显著性,并纳入加权基因共表达网络分析。基于CD8 T淋巴细胞比例和肿瘤纯度,我们生成了CD8 T淋巴细胞共表达网络,并选择与CD8 T淋巴细胞相关性最高的模块作为我们感兴趣的共表达模块。我们基于最小绝对收缩和选择算子方法(LASSO)回归模型和稳健模型构建了CD8 + T细胞模型,并评估其在不同亚组中的预测能力。
确定了一个乳腺癌CD8 + T淋巴细胞比例共表达黄色模块。黄色模块中的共表达基因被确定可增加乳腺癌患者的CD8 + T淋巴细胞比例水平。黄色模块在抗原呈递过程、细胞对干扰素 - γ的反应和白细胞增殖方面显著富集。随后,我们生成了与CD8 + T细胞相关的基因LASSO回归风险模型和稳健模型,8个基因被纳入风险模型。风险评分在各个亚组中显示出显著的预后能力。CD74编码的蛋白质在乳腺癌样本中的表达水平低于正常组织,表明在mRNA和蛋白质水平上均存在表达差异。
这8个CD8 + T淋巴细胞比例共表达基因通过抗原呈递过程增加乳腺癌中的CD8 T淋巴细胞。该机制可能为改善未从免疫治疗中获益的患者的预后提示新的途径。