PDF(Pubmed)
Abstract：
UNASSIGNED: Costimulatory molecules are putative novel targets or potential additions to current available immunotherapy, but their expression patterns and clinical value in triple-negative breast cancer (TNBC) are to be clarified.
UNASSIGNED: The gene expression profiles datasets of TNBC patients were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Diagnostic biomarkers for stratifying individualized tumor immune microenvironment (TIME) were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms. Additionally, we explored their associations with response to immunotherapy via the multiplex immunohistochemistry (mIHC).
UNASSIGNED: A total of 60 costimulatory molecule genes (CMGs) were obtained, and we determined two different TIME subclasses (\"hot\" and \"cold\") through the K-means clustering method. The \"hot\" tumors presented a higher infiltration of activated immune cells, i.e., CD4 memory-activated T cells, resting NK cells, M1 macrophages, and CD8 T cells, thereby enriched in the B cell and T cell receptor signaling pathways. LASSO and SVM-RFE algorithms identified three CMGs (CD86, TNFRSF17 and TNFRSF1B) as diagnostic biomarkers. Following, a novel diagnostic nomogram was constructed for predicting individualized TIME status and was validated with good predictive accuracy in TCGA, GSE76250 and GSE58812 databases. Further mIHC conformed that TNBC patients with high CD86, TNFRSF17 and TNFRSF1B levels tended to respond to immunotherapy.
UNASSIGNED: This study supplemented evidence about the value of CMGs in TNBC. In addition, CD86, TNFRSF17 and TNFRSF1B were found as potential biomarkers, significantly promoting TNBC patient selection for immunotherapeutic guidance.
UNASSIGNED: The gene expression profiles datasets of TNBC patients were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Diagnostic biomarkers for stratifying individualized tumor immune microenvironment (TIME) were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithms. Additionally, we explored their associations with response to immunotherapy via the multiplex immunohistochemistry (mIHC).
UNASSIGNED: A total of 60 costimulatory molecule genes (CMGs) were obtained, and we determined two different TIME subclasses (\"hot\" and \"cold\") through the K-means clustering method. The \"hot\" tumors presented a higher infiltration of activated immune cells, i.e., CD4 memory-activated T cells, resting NK cells, M1 macrophages, and CD8 T cells, thereby enriched in the B cell and T cell receptor signaling pathways. LASSO and SVM-RFE algorithms identified three CMGs (CD86, TNFRSF17 and TNFRSF1B) as diagnostic biomarkers. Following, a novel diagnostic nomogram was constructed for predicting individualized TIME status and was validated with good predictive accuracy in TCGA, GSE76250 and GSE58812 databases. Further mIHC conformed that TNBC patients with high CD86, TNFRSF17 and TNFRSF1B levels tended to respond to immunotherapy.
UNASSIGNED: This study supplemented evidence about the value of CMGs in TNBC. In addition, CD86, TNFRSF17 and TNFRSF1B were found as potential biomarkers, significantly promoting TNBC patient selection for immunotherapeutic guidance.
摘要：
■共刺激分子是目前可用的免疫疗法的公认的新靶点或潜在补充。但其在三阴性乳腺癌（TNBC）中的表达模式和临床价值尚待阐明。
■TNBC患者的基因表达谱数据集从癌症基因组图谱和基因表达综合数据库获得。使用最小绝对收缩和选择算子（LASSO）和支持向量机递归特征消除（SVM-RFE）算法鉴定用于分层个体化肿瘤免疫微环境（TIME）的诊断生物标志物。此外,我们通过多重免疫组织化学(mIHC)探讨了它们与免疫治疗反应的关系.
■共获得60个共刺激分子基因（CMGs），我们通过K-means聚类方法确定了两个不同的TIME子类（“热”和“冷”）。“热”肿瘤表现出更高的活化免疫细胞浸润，即，CD4记忆激活T细胞,静息NK细胞，M1巨噬细胞，和CD8T细胞，从而富集B细胞和T细胞受体信号通路。LASSO和SVM-RFE算法鉴定了三种CMG(CD86、TNFRSF17和TNFRSF1B)作为诊断生物标志物。Follows,构建了用于预测个性化时间状态的新诊断列线图，并在TCGA中以良好的预测准确性进行了验证，GSE76250和GSE58812数据库。进一步的mIHC证实，具有高CD86，TNFRSF17和TNFRSF1B水平的TNBC患者倾向于对免疫疗法有反应。
■这项研究补充了CMG在TNBC中的价值。此外,CD86，TNFRSF17和TNFRSF1B被发现是潜在的生物标志物，显着促进TNBC患者选择免疫治疗指导。
■TNBC患者的基因表达谱数据集从癌症基因组图谱和基因表达综合数据库获得。使用最小绝对收缩和选择算子（LASSO）和支持向量机递归特征消除（SVM-RFE）算法鉴定用于分层个体化肿瘤免疫微环境（TIME）的诊断生物标志物。此外,我们通过多重免疫组织化学(mIHC)探讨了它们与免疫治疗反应的关系.
■共获得60个共刺激分子基因（CMGs），我们通过K-means聚类方法确定了两个不同的TIME子类（“热”和“冷”）。“热”肿瘤表现出更高的活化免疫细胞浸润，即，CD4记忆激活T细胞,静息NK细胞，M1巨噬细胞，和CD8T细胞，从而富集B细胞和T细胞受体信号通路。LASSO和SVM-RFE算法鉴定了三种CMG(CD86、TNFRSF17和TNFRSF1B)作为诊断生物标志物。Follows,构建了用于预测个性化时间状态的新诊断列线图，并在TCGA中以良好的预测准确性进行了验证，GSE76250和GSE58812数据库。进一步的mIHC证实，具有高CD86，TNFRSF17和TNFRSF1B水平的TNBC患者倾向于对免疫疗法有反应。
■这项研究补充了CMG在TNBC中的价值。此外,CD86，TNFRSF17和TNFRSF1B被发现是潜在的生物标志物，显着促进TNBC患者选择免疫治疗指导。
