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.
Highlights
Gastric cancer is the fifth most frequently diagnosed cancer and the third leading cause of cancer-related death worldwide [1]
To identify genes related to the progression of gastric cancer, a coexpression network was constructed by weighted gene coexpression network analysis (WGCNA) in GSE26901
The results showed that C1QA, CIQB, C1QC, CD14, FCER1G, and TYROBP were highly expressed in gastric cancer tissues compared with normal tissues (Supplementary Figure 2), and there were no significant differences in CD163, CSF1R, and MS4A6A
Summary
Gastric cancer is the fifth most frequently diagnosed cancer and the third leading cause of cancer-related death worldwide [1]. Despite continuous improvements in treatment, the 5-year survival rate of metastatic gastric cancer is only 5–20% [3,4,5]. The use of immunotherapy alone or in combination with other therapies can have a positive impact on the treatment of gastric cancer, but due to the high heterogeneity of gastric cancer, the response rate of patients during immunotherapy is not satisfactory [7, 8]. 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. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed
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