集成机器学习算法和多个免疫组织化学验证，以揭示基于共刺激分子的新型诊断标记，用于预测三阴性乳腺癌的免疫微环境状态
Integrating machine learning algorithms and multiple immunohistochemistry validation to unveil novel diagnostic markers based on costimulatory molecules for predicting immune microenvironment status in triple-negative breast cancer
影响因子:5.90000
分区:医学2区 / 免疫学2区
发表日期:2024
作者:
Chao Zhang, Wenyu Zhai, Yuyu Ma, Minqing Wu, Qiaoting Cai, Jiajia Huang, Zhihuan Zhou, Fangfang Duan
摘要
共刺激分子是针对当前可用免疫疗法的假定新目标或潜在的添加，但是要澄清其表达模式和三阴性乳腺癌（TNBC）的临床价值。使用最不绝对的收缩和选择操作员（LASSO）（LASSO）和支持向量机器回顾性特征消除（SVM-RFE）算法鉴定出用于分层个性化肿瘤免疫微环境（时间）的诊断生物标志物。此外，我们通过多重免疫组织化学（MIHC）探讨了他们与免疫疗法的反应。总共获得了60个共刺激分子基因（CMG），并确定了通过K-Means Clustering方法确定了两个不同的时间子类（“热”和“冷”）。 “热”肿瘤表现出更高的激活免疫细胞的浸润，即CD4记忆激活的T细胞，静息NK细胞，M1巨噬细胞和CD8 T细胞，从而富集在B细胞和T细胞受体信号通路中。 LASSO和SVM-RFE算法将三个CMG（CD86，TNFRSF17和TNFRSF1B）确定为诊断生物标志物。随后，构建了一个新颖的诊断列表，以预测个性化的时间状态，并在TCGA，GSE76250和GSE58812数据库中以良好的预测精度进行了验证。进一步的MIHC符合较高CD86，TNFRSF17和TNFRSF1B水平的TNBC患者倾向于对免疫疗法做出反应。这项研究补充了有关CMGS在TNBC中价值的证据。此外，发现CD86，TNFRSF17和TNFRSF1B是潜在的生物标志物，可显着促进TNBC患者选择免疫治疗指导。
Abstract
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.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).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.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.