Liu Fen, Yang Zongcheng, Zheng Lixin, Shao Wei, Cui Xiujie, Wang Yue, Jia Jihui, Fu Yue
Department of Microbiology/Key Laboratory for Experimental Teratology of the Chinese Ministry of Education, School of Basic Medical Science, Cheeloo College of Medicine, Shandong University, Jinan, China.
Key Laboratory of Infection and Immunity of Shandong Province, School of Basic Medical Science, Cheeloo College of Medicine, Shandong University, Jinan, China.
Front Oncol. 2021 Jun 14;11:690129. doi: 10.3389/fonc.2021.690129. eCollection 2021.
Gastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.
Weighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.
WGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.
Our results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.
胃癌是一种常见的胃肠道恶性肿瘤。由于其往往在晚期才被诊断出来，所以死亡率很高。传统疗法（如持续化疗）对晚期胃癌并不令人满意，但免疫疗法已显示出巨大的治疗潜力。胃癌具有高度的分子和表型异质性。迫切需要新的策略来进行准确的预后评估以及为免疫疗法选择合适的患者。
使用加权基因共表达网络分析（WGCNA）来识别与胃癌进展相关的核心基因。基于这些核心基因，通过一致性聚类分析将样本分为两个亚型。在获得亚型之间的差异表达基因后，通过单变量Cox回归、最小绝对收缩和选择算子（LASSO）回归以及多变量Cox回归分析构建胃癌风险模型。比较亚型和风险组之间在预后、临床特征、肿瘤微环境（TME）组成和免疫特征方面的差异，并应用连通性图谱（CMap）数据库来识别高危患者的潜在治疗方法。
WGCNA和筛选揭示了九个与胃癌进展密切相关的核心基因。根据核心基因表达进行的无监督聚类将胃癌患者分为与疾病进展相关的两个亚型，这些患者在预后、TME免疫和基质评分以及抑制性免疫检查点表达方面存在显著差异。基于亚型之间的不同表达模式，我们构建了一个胃癌风险模型，并根据风险评分将患者分为高危组和低危组。高危患者预后较差，TME免疫/基质评分较高，抑制性免疫检查点表达较高，且具有更多适合免疫疗法的免疫特征。包括年龄、分期和风险评分的多变量Cox回归分析表明，风险评分可作为胃癌的独立预后因素。基于风险评分，我们构建了一个能相对准确预测胃癌患者预后的列线图，并为高危患者筛选了潜在药物。
我们的结果表明，与肿瘤进展相关的7基因特征可以预测胃癌的临床预后和肿瘤免疫特征。
