Chen Qingchuan, Tan Yuen, Zhang Chao, Zhang Zhe, Pan Siwei, An Wen, Xu Huimian
Department of Surgical Oncology, The First Affiliated Hospital of China Medical University, Shenyang, China.
Front Oncol. 2021 Feb 25;11:554779. doi: 10.3389/fonc.2021.554779. eCollection 2021.
Gastric cancer (GC) is a major public health problem worldwide. In recent decades, the treatment of gastric cancer has improved greatly, but basic research and clinical application of gastric cancer remain challenges due to the high heterogeneity. Here, we provide new insights for identifying prognostic models of GC.
We obtained the gene expression profiles of GSE62254 containing 300 samples for training. GSE15459 and TCGA-STAD for validation, which contain 200 and 375 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules. We performed Lasso regression and Cox regression analyses to identify the most significant five genes to develop a novel prognostic model. And we selected two representative genes within the model for immunohistochemistry staining with 105 GC specimens from our hospital to verify the prediction efficiency. Moreover, we estimated the correlation coefficient between our model and immune infiltration using the CIBERSORT algorithm. The data from GSE15459 and TCGA cohort validated the robustness and predictive accuracy of this prognostic model.
Of the 12 gene modules identified, 1,198 green-yellow module genes were selected for further analysis. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using the Cox proportional hazards regression model. Finally, we constructed a five gene prognostic model: Risk Score = [(-0.7547) * Expression ()] + [(-0.8272) * Expression ()] + [1.09 * Expression ()] + [0.5174 * Expression ()] + [1.66 * Expression ()]. The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group (p = 6.503e-11). The risk model was also regarded as an independent predictor of prognosis (HR, 1.678, p < 0.001). The observed correlation with immune cells suggested that this risk model could potentially predict immune infiltration.
This study identified a potential risk model for prognosis and immune infiltration prediction in GC using WGCNA and Cox regression analysis.
胃癌（GC）是全球主要的公共卫生问题。近几十年来，胃癌的治疗有了很大改善，但由于高度异质性，胃癌的基础研究和临床应用仍然面临挑战。在此，我们为识别胃癌的预后模型提供了新的见解。
我们获取了包含300个样本的GSE62254基因表达谱用于训练。分别获取包含200个样本的GSE15459和包含375个样本的TCGA-STAD用于验证。使用加权基因共表达网络分析（WGCNA）来识别基因模块。我们进行了Lasso回归和Cox回归分析，以识别最显著的五个基因来构建一个新的预后模型。并且我们从模型中选择两个代表性基因，对我院105例胃癌标本进行免疫组织化学染色，以验证预测效率。此外，我们使用CIBERSORT算法估计了我们的模型与免疫浸润之间的相关系数。来自GSE15459和TCGA队列的数据验证了该预后模型的稳健性和预测准确性。
在识别出的12个基因模块中，选择了1198个绿黄色模块基因进行进一步分析。使用Cox比例风险回归模型对单变量Cox回归和Lasso回归分析中的基因进行多变量Cox分析。最后，我们构建了一个五基因预后模型：风险评分 = [（-0.7547）*表达（）] + [（-0.8272）*表达（）] + [1.09 *表达（）] + [0.5174 *表达（）] + [1.66 *表达（）]。高风险组样本的预后明显差于低风险组样本（p = 6.503e-11）。风险模型也被视为预后的独立预测因子（HR，1.678，p < 0.001）。观察到的与免疫细胞的相关性表明该风险模型可能潜在地预测免疫浸润。
本研究使用WGCNA和Cox回归分析识别了一种用于胃癌预后和免疫浸润预测的潜在风险模型。
