A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

Abstract

BackgroundGastric 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.MethodsWeighted 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.ResultsWGCNA 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.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

Identification and Validation of Invasion-Related Molecular Subtypes and Prognostic Features for Cervical Cancer

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The Pan-Cancer Crosstalk Between the EFNA Family and Tumor Microenvironment for Prognosis and Immunotherapy of Gastric Cancer

“…Liu et al constructed a gastric cancer prognostic scoring system based on several genes closely related to gastric cancer progression. There were differences in the TME immune score, stromal score and inhibitory immune checkpoint expression between high- and low-risk groups ( Liu et al, 2021 ). In another study on the TME, the prognostic power of tumor-stromal ratio in gastric cancer was no less than that of the TNM stage ( Peng Q et al, 2018 ).…”

The Construction of a Prognostic Model Based on a Peptidyl Prolyl Cis–Trans Isomerase Gene Signature in Hepatocellular Carcinoma

“…In recent years, with the rapid development of next-generation sequencing technology and precision medicine, more and more evidence indicates that gene signatures of mRNA level have good potential in predicting the prognosis of many cancers, including HCC. For example, the application of bioinformatics methods to construct a prognostic model based on the gene signature of autophagy, M6A methylation, and immunity have been reported for a variety of cancers, which is even better than TNM staging to a certain extent ( Brebi et al, 2014 ; Frost and Amos, 2017 ; Liu et al, 2021 ). However, most of the existing signatures have not been widely used in clinical practice of HCC because the reliability of models is affected by many factors such as over-fitting.…”