Guo Sicheng, Ma Yuting, Li Xiaokang, Li Wei, He Xiaogang, Yuan Zheming, Hu Yuan
Hunan Engineering & Technology Research Centre for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University, Changsha, Hunan, China.
College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
Front Genet. 2023 Jul 27;14:1165648. doi: 10.3389/fgene.2023.1165648. eCollection 2023.
The tumor microenvironment (TME) of breast cancer (BRCA) is a complex and dynamic micro-ecosystem that influences BRCA occurrence, progression, and prognosis through its cellular and molecular components. However, as the tumor progresses, the dynamic changes of stromal and immune cells in TME become unclear. The aim of this study was to identify differentially co-expressed genes (DCGs) associated with the proportion of stromal cells in TME of BRCA, to explore the patterns of cell proportion changes, and ultimately, their impact on prognosis. A new heuristic feature selection strategy (CorDelSFS) was combined with differential co-expression analysis to identify TME-key DCGs. The expression pattern and co-expression network of TME-key DCGs were analyzed across different TMEs. A prognostic model was constructed using six TME-key DCGs, and the correlation between the risk score and the proportion of stromal cells and immune cells in TME was evaluated. TME-key DCGs mimicked the dynamic trend of BRCA TME and formed cell type-specific subnetworks. The IG gene-related subnetwork, plasmablast-specific expression, played a vital role in the BRCA TME through its adaptive immune function and tumor progression inhibition. The prognostic model showed that the risk score was significantly correlated with the proportion of stromal cells and immune cells in TME, and low-risk patients had stronger adaptive immune function. IGKV1D-39 was identified as a novel BRCA prognostic marker specifically expressed in plasmablasts and involved in adaptive immune responses. This study explores the role of proportionate-related genes in the tumor microenvironment using a machine learning approach and provides new insights for discovering the key biological processes in tumor progression and clinical prognosis.
乳腺癌（BRCA）的肿瘤微环境（TME）是一个复杂且动态的微生态系统，通过其细胞和分子成分影响BRCA的发生、进展和预后。然而，随着肿瘤进展，TME中基质细胞和免疫细胞的动态变化尚不清楚。本研究旨在识别与BRCA的TME中基质细胞比例相关的差异共表达基因（DCG），探索细胞比例变化模式，并最终探究其对预后的影响。一种新的启发式特征选择策略（CorDelSFS）与差异共表达分析相结合，以识别TME关键DCG。分析了TME关键DCG在不同TME中的表达模式和共表达网络。使用六个TME关键DCG构建了一个预后模型，并评估了风险评分与TME中基质细胞和免疫细胞比例之间的相关性。TME关键DCG模拟了BRCA TME的动态趋势，并形成了细胞类型特异性子网。IG基因相关子网，即浆母细胞特异性表达，通过其适应性免疫功能和肿瘤进展抑制在BRCA TME中发挥重要作用。预后模型显示，风险评分与TME中基质细胞和免疫细胞比例显著相关，低风险患者具有更强的适应性免疫功能。IGKV1D - 39被鉴定为一种新型的BRCA预后标志物，在浆母细胞中特异性表达并参与适应性免疫反应。本研究使用机器学习方法探索了肿瘤微环境中比例相关基因的作用，并为发现肿瘤进展和临床预后的关键生物学过程提供了新见解。
