背景:这项研究旨在鉴定结直肠癌(CRC)中的关键糖基转移酶(GTs),并建立源自GTs的可靠预后特征。
方法:利用AUCell,UCell,singscore,ssgsea,和AddModuleScore算法,随着相关性分析,我们在单细胞RNA水平上重新定义了CRC中与GTs相关的基因.为了提高风险模型的准确性,采用单变量Cox和Lasso回归发现CRC中GTs的更多临床子集。随后,评估了七种机器学习算法对CRC预后的有效性,通过嵌套交叉验证关注生存结果。然后在四个独立的外部队列中验证了该模型,探索肿瘤微环境(TME)的变化,对免疫疗法的反应,突变谱,以及每个风险组的路径。重要的是,我们确定了针对高GARS组患者的潜在治疗药物.
结果:在我们的研究中,我们将CRC患者分为不同的亚组,每个都表现出预后的变化,临床特征,途径富集,免疫浸润,和免疫检查点基因表达。此外,我们建立了基于机器学习的糖基转移酶相关风险标签(GARS).GARS在预后能力和生存预测准确性方面都超越了传统的临床病理特征,它与较高的恶性程度相关,为CRC患者提供有价值的见解。此外,我们探讨了风险评分与免疫治疗疗效之间的关系.
结论:开发了基于GTs的预后模型来预测对免疫疗法的反应,提供了一种新的CRC管理方法。
BACKGROUND: This study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs.
METHODS: Utilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group.
RESULTS: In our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a
Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy.
CONCLUSIONS: A prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.