He Youfu, You Ganhua, Zhou Yu, Ai Liqiong, Liu Wei, Meng Xuantong, Wu Qiang
Medical College, Guizhou University, Guiyang, Guizhou Province, China.
Department of Cardiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou Province, China.
J Cell Mol Med. 2025 Jan;29(2):e70377. doi: 10.1111/jcmm.70377.
It is critical to appreciate the role of the tumour-associated microenvironment (TME) in developing strategies for the effective therapy of cancer, as it is an important factor that determines the evolution and treatment response of tumours. This work combines machine learning and single-cell RNA sequencing (scRNA-seq) to explore the glioma tumour microenvironment's TME. With the help of genome-wide association studies (GWAS) and Mendelian randomization (MR), we found genetic variants associated with TME elements that affect cancer and cardiovascular disease outcomes. Using machine learning techniques high dimensional data was analysed to obtain new molecular sub-types and biomarkers that are important for prognosis and treatment response. F3 was identified as a top regulator and revealed potential angiogenic and immunogenic characteristics within the TME that could be harnessed in immunotherapy. These results demonstrate the potential of machine-learning approaches in identifying and dissecting TME heterogeneity and informing treatment in precision oncology. This work proposes improving the immunotherapeutic response through targeted modulation of relevant cellular and molecular interactions.
认识到肿瘤相关微环境（TME）在制定有效的癌症治疗策略中的作用至关重要，因为它是决定肿瘤演变和治疗反应的一个重要因素。这项工作结合了机器学习和单细胞RNA测序（scRNA-seq）来探索神经胶质瘤肿瘤微环境的TME。借助全基因组关联研究（GWAS）和孟德尔随机化（MR），我们发现了与影响癌症和心血管疾病结局的TME元素相关的遗传变异。使用机器学习技术对高维数据进行分析，以获得对预后和治疗反应很重要的新分子亚型和生物标志物。F3被确定为顶级调节因子，并揭示了TME内潜在的血管生成和免疫原性特征，可用于免疫治疗。这些结果证明了机器学习方法在识别和剖析TME异质性以及为精准肿瘤学中的治疗提供信息方面的潜力。这项工作提出通过对相关细胞和分子相互作用的靶向调节来改善免疫治疗反应。
