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 - Zhang, Chao; Zhai, Wenyu; Ma, Yuyu; Wu, Minqing; Cai, Qiaoting; Huang, Jiajia; Zhou, Zhihuan; Duan, Fangfang (Frontiers in Immunology, 2024)
This study aimed to dissect the expression patterns and clinical value of costimulatory molecules in triple-negative breast cancer (TNBC). The expression profiles of costimulatory molecule genes from a total of 440 patients were obtained from public databases. Two different tumor immune microenvironment subclasses ("hot" and "cold") were determined using 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. Three costimulatory molecule genes (CD86, TNFRSF17 and TNFRSF1B) were identified as diagnostic markers by the Least Absolute Shrinkage and Selection Operator and the Support Vector Machine-Recursive Feature Elimination machine learning algorithms. Following, a diagnostic nomogram was constructed for predicting the tumor immune microenvironment subclasses and was validated with good predictive accuracy in GSE76250 and GSE58812 datasets.Further multiple immunohistochemistry conformed that TNBC patients responding to immunotherapy had high CD86, TNFRSF17 and TNFRSF1B levels. Collectively, these data provided evidence about the value of costimulatory molecule genes in predicting individualized tumor immune microenvironment status and promoting TNBC patient selection for immunotherapeutic guidance.