A Weighted Gene Co-Expression Network Analysis-Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer.
A Weighted Gene Co-Expression Network Analysis-Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer.
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基于加权基因共表达网络分析的胃癌预后预测模型
DOI：
10.3389/fonc.2021.554779
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发表时间：
2021
影响因子：
4.7
通讯作者：
Xu H
中科院分区：
文献类型：
--
作者：
Chen Q;Tan Y;Zhang C;Zhang Z;Pan S;An W;Xu H
胃癌（GC）是一个全球性的重大公共卫生问题。近几十年来，胃癌的治疗水平有了很大提高，但由于胃癌的高度异质性，其基础研究和临床应用仍面临挑战。在这里，我们为确定GC的预后模型提供了新的见解。我们获得了包含300个样本的GSE 62254的基因表达谱用于训练。用于验证的GSE 15459和TCGA-STAD，分别包含200和375个样品。加权基因共表达网络分析（WGCNA）用于识别基因模块。我们进行了Lasso回归和考克斯回归分析，以确定最重要的五个基因，以开发一种新的预后模型。并选取模型中两个具有代表性的基因对我院105例胃癌标本进行免疫组化染色，验证模型的预测效率。此外，我们估计我们的模型和免疫渗透使用CIBERSORT算法之间的相关系数。来自GSE 15459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。在鉴定的12个基因模块中，选择1，198个绿黄色模块基因用于进一步分析。使用考克斯比例风险回归模型对来自单变量考克斯回归和Lasso回归分析的基因进行多变量考克斯分析。最后，我们构建了一个五基因预后模型：风险评分= [（-0.7547）* 表达（ARHGAP 32）] + [（-0.8272）* 表达（KLF 5）] + [1.09 * 表达（MAMLD 1）] + [0.5174 * 表达（MATN 3）] + [1.66 * 表达（内斯）]。高风险组样本的预后显著差于低风险组样本（p = 6.503e-11）。风险模型也被认为是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，这种风险模型可能预测免疫浸润。本研究采用WGCNA和考克斯回归分析确定了胃癌预后和免疫浸润预测的潜在风险模型。
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 (ARHGAP32)] + [(-0.8272) * Expression (KLF5)] + [1.09 * Expression (MAMLD1)] + [0.5174 * Expression (MATN3)] + [1.66 * Expression (NES)]. 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.
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影响因子：
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DOI：
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发表时间：
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