目的:我们的目的是开发一种具有干细胞相关基因的预后模型,以评估胃癌(GC)的预后和免疫治疗反应性。
背景:肿瘤干性与肿瘤内异质性有关,免疫抑制,和抗肿瘤抗性。我们开发了一个具有干性相关基因的预后模型来评估GC的预后和免疫治疗反应性。
目的:我们的目的是建立一个具有干细胞相关基因的预后模型,以评估GC的预后和免疫治疗反应性。
方法:我们从基因表达综合(GEO)数据库下载了GC患者的单细胞RNA测序(scRNA-seq)数据,并使用CytoTRACE筛选了GC干细胞相关基因。我们表征了肿瘤干性与免疫检查点阻断(ICB)和免疫力的关联。此后,使用加权基因共表达网络分析(WGCNA)开发了基于9-stemness特征的预后模型,单变量Cox回归分析,和最小绝对收缩和选择算子(LASSO)回归分析。用列线图评估模型预测值。
结果:早期GC患者的干性水平明显较高。干性评分与肿瘤免疫功能紊乱(TIDE)评分和免疫浸润呈负相关,特别是T细胞和B细胞。基于9个基因(ERCC6L,IQCC,NKAPD1,BLMH,SLC25A15,MRPL4,VPS35,SUMO3和CINP)在预后预测中具有良好的性能,其稳健性在GSE26942队列中得到验证。此外,列线图和风险评分显示出最强大的预后预测能力。高风险患者表现出发展免疫逃逸和对PD-L1免疫疗法的低反应的趋势。
结论:我们开发了一种用于预后预测的基于干性的基因标签,具有准确性和可靠性。该特征还有助于GC患者的免疫治疗的临床决策。
OBJECTIVE: We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in gastric cancer (GC).
BACKGROUND: Tumor stemness is related to intratumoral heterogeneity, immunosuppression, and anti-tumor resistance. We developed a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC.
OBJECTIVE: We aimed to develop a prognostic model with stemness-correlated genes to evaluate prognosis and immunotherapy responsiveness in GC.
METHODS: We downloaded single-cell RNA sequencing (scRNA-seq) data of GC patients from the Gene-Expression Omnibus (GEO) database and screened GC stemness- related genes using CytoTRACE. We characterized the association of tumor stemness with immune checkpoint blockade (ICB) and immunity. Thereafter, a 9-stemness signature-based prognostic model was developed using weighted gene co-expression network analysis (WGCNA), univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO) regression analysis. The model predictive value was evaluated with a nomogram.
RESULTS: Early GC patients had significantly higher levels of stemness. The stemness score showed a negative relationship to tumor immune dysfunction and exclusion (TIDE) score and immune infiltration, especially T cells and B cells. A stemness-based signature based on 9 genes (ERCC6L, IQCC, NKAPD1, BLMH, SLC25A15, MRPL4, VPS35, SUMO3, and CINP) was constructed with good performance in prognosis prediction, and its robustness was validated in GSE26942 cohort. Additionally, nomogram and risk score exhibited the most powerful ability for prognosis prediction. High-risk patients exhibited a tendency to develop immune escape and low response to PD-L1 immunotherapy.
CONCLUSIONS: We developed a stemness-based gene signature for prognosis prediction with accuracy and reliability. This signature also helps clinical decision-making of immunotherapy for GC patients.