Construction and validation of a novel coagulation-related 7-gene prognostic signature for gastric cancer - Wang, Bofang; Zou, Dan; Wang, Na; Wang, Haotian; Zhang, Tao; Gao, Lei; Ma, Chenhui; Zheng, Peng; Gu, Baohong; Li, Xuemei; Wang, Yunpeng; He, Puyi; et al. (Frontiers in Genetics, 2022)
Background: Gastric cancer (GC) is the most common malignant tumor. Due to the lack of practical molecular markers, the prognosis of patients with advanced gastric cancer is still poor. Many studies have confirmed that the coagulation system is closely related to tumor progression. Therefore, the purpose of this study was to construct a coagulation-related gene signature and prognostic model for GC by bioinformatics methods. Methods: We download the gene expression and clinical data of GC patients from the TCGA and GEO databases. 216 coagulation-related genes (CRGs) were obtained from AmiGO 2. Weighted gene co-expression network analysis (WGCNA) was used to identify coagulation-related genes associated with clinical features of GC. Last absolute shrinkage and selection operator (LASSO) Cox regression was utilized to shrink the relevant predictors of the coagulation system, and a Coag-Score prognostic model was constructed based on the coefficients. According to this risk model, GC patients were divided into high-risk and low-risk groups, and overall survival (OS) curves and receiver operating characteristic (ROC) curves were drawn in the training and validation sets, respectively. We also constructed nomograms for predicting 1-, 2-, and 3-year survival in GC patients. Single-sample gene set enrichment analysis (ssGSEA) was exploited to explore immune cells’ underlying mechanisms and correlations. The expression levels of coagulation-related genes were verified by real-time quantitative polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). Results: We identified 7 CRGs employed to construct a Coag-Score risk model using WGCNA combined with LASSO regression. In both training and validation sets, GC patients in the high-risk group had worse OS than the low-risk group, and Coag-Score was identified as an independent predictor of OS, and the nomogram provided a quantitative method to predict the 1, 2, and 3-year survival rates of GC patients. Functional analysis showed that Coag-Score was mainly related to MAPK signaling pathway, complement and coagulation cascades, angiogenesis, KRAS signaling pathway. In addition, the high-risk group had a significantly higher infiltration enrichment score and was positively associated with immune checkpoint gene expression. Conclusion: Coagulation-related gene models provide new insights and targets for the diagnosis, prognosis prediction, and treatment management of GC patients.