A Weighted Gene Co-Expression Network Analysis-Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer.

Qingchuan Chen,Huimian Xu,Wen An,Yuen Tan,Siwei Pan,Zhe Zhang,Chao Zhang

doi:10.3389/fonc.2021.554779

Abstract

BackgroundGastric 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.MethodsWe 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.ResultsOf 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.ConclusionThis study identified a potential risk model for prognosis and immune infiltration prediction in GC using WGCNA and Cox regression analysis.

Highlights

Worldwide, gastric cancer (GC) is a common malignant tumor with relatively poor prognosis
Genes with expression in the first quarter of variance was selected for further weighted gene co-expression network analysis (WGCNA) analysis
Many genetic prognostic models of GC have been published, most of which are based on genetic difference analysis followed by Cox regression analysis

Summary

Introduction

Gastric cancer (GC) is a common malignant tumor with relatively poor prognosis. There are a large number of patients with GC in China, the majority of whom have advanced stage disease. Over the past two decades, the 5-year overall survival of patients with GC has improved [2]. This change is due to increased knowledge about the pathogenesis of GC, and treatment advances [3, 4]. Such advances include the identification of GC biomarkers and therapeutic targets [5, 6]. Gastric cancer (GC) is a major public health problem worldwide. We provide new insights for identifying prognostic models of GC

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Results

Conclusion