基于机器学习的糖酵解相关分子分类揭示了结直肠癌患者预后、TME 和免疫治疗的差异。

BACKGROUND: Aerobic glycolysis is a process that metabolizes glucose under aerobic conditions, finally producing pyruvate, lactic acid, and ATP for tumor cells. Nevertheless, the overall significance of glycolysis-related genes in colorectal cancer and how they affect the immune microenvironment have not been investigated. METHODS: By combining the transcriptome and single-cell analysis, we summarize the various expression patterns of glycolysis-related genes in colorectal cancer. Three glycolysis-associated clusters (GAC) were identified with distinct clinical, genomic, and tumor microenvironment (TME). By mapping GAC to single-cell RNA sequencing analysis (scRNA-seq), we next discovered that the immune infiltration profile of GACs was similar to that of bulk RNA sequencing analysis (bulk RNA-seq). In order to determine the kind of GAC for each sample, we developed the GAC predictor using markers of single cells and GACs that were most pertinent to clinical prognostic indications. Additionally, potential drugs for each GAC were discovered using different algorithms. RESULTS: GAC1 was comparable to the immune-desert type, with a low mutation probability and a relatively general prognosis; GAC2 was more likely to be immune-inflamed/excluded, with more immunosuppressive cells and stromal components, which also carried the risk of the poorest prognosis; Similar to the immune-activated type, GAC3 had a high mutation rate, more active immune cells, and excellent therapeutic potential. CONCLUSION: In conclusion, we combined transcriptome and single-cell data to identify new molecular subtypes using glycolysis-related genes in colorectal cancer based on machine-learning methods, which provided therapeutic direction for colorectal patients.

背景：有氧糖酵解是一种在有氧条件下代谢葡萄糖的过程，最终为肿瘤细胞产生丙酮酸、乳酸和 ATP。然而，糖酵解相关基因在结直肠癌中的整体意义以及它们如何影响免疫微环境尚未得到研究。

方法：通过整合转录组和单细胞分析，我们总结了结直肠癌中糖酵解相关基因的各种表达模式。鉴定出三个与糖酵解相关的簇（GAC），它们具有不同的临床、基因组和肿瘤微环境（TME）特征。通过将 GAC 映射到单细胞 RNA 测序分析（scRNA-seq），我们发现 GAC 的免疫浸润图谱与 bulk RNA 测序分析（bulk RNA-seq）相似。为了确定每个样本的 GAC 类型，我们使用与单细胞和 GAC 最相关的标记开发了 GAC 预测器，这些标记与临床预后指征有关。此外，使用不同的算法发现了每种 GAC 的潜在药物。

结果：GAC1 与免疫荒漠型相似，突变概率低，预后一般；GAC2 更有可能是免疫炎症/排斥型，具有更多的免疫抑制细胞和基质成分，同时也具有预后最差的风险；与免疫激活型相似，GAC3 具有高突变率、更活跃的免疫细胞和出色的治疗潜力。

结论：总之，我们通过机器学习方法，结合转录组和单细胞数据，利用结直肠癌中与糖酵解相关的基因，鉴定出新的分子亚型，为结直肠癌患者提供了治疗方向。