Identification and Validation of an m6A Modification of JAK-STAT Signaling Pathway–Related Prognostic Prediction Model in Gastric Cancer - Jiang, Fei; Chen, Xiaowei; Shen, Yan; Shen, Xiaobing (Frontiers in Genetics, 2022)
Background: Gastric cancer(GC) is one of the malignant tumors in the world. JAK-STAT signaling pathway participates cellular biological process and immune function. While, the association between them are still not systematically described. Therefore, we aimed to identify key genes involved in JAK-STAT signaling pathway and GC, as well as the potential mechanism. Methods: The Cancer Genome Atlas (TCGA) database was the source of RNA-sequencing data of GC patients. Gene Expression Omnibus(GEO) database was used as the validation set. Following the construction of the JAK-STAT signaling pathway related prognostic prediction model employing Least absolute shrinkage and selection operator (LASSO), survival, univariate, multivariate Cox regression analyses and receiver operating characteristic curve (ROC) analyses to examine the predictive value of the model. Quantitative real-time polymerase chain reaction (qRT-PCR) and chi-square test were used to verify the expression of genes in model and assess the association between the genes and clinicopathological parameters of GC patients, respectively. Then, GO, KEGG, GSEA, SRAMP online websites and RNA immunoprecipitation (RIP) experiment were used to predict the model related potential pathways, m6A modifications and the association between model genes and m6A. Results: A four-gene prognostic model (GHR, PIM1, IFNA8 and IFNB1) was constructed, namely riskScore. The Kaplan–Meier curves suggested that patients with high riskScore expression had a poorer prognosis than low riskScore expression group (p=0.006). Multivariate Cox regression analyses showed that the model could be an independent predictor (p<0.001; HR=3.342, 95%CI=1.834-6.088). The five-years area under time dependent ROC curve (AUC) reaching 0.655. Training test set verified these results. Further analyses unveiled an enrichment of cancer-related pathways, m6A modification and the direct interaction between m6A and the four genes. Conclusion: This four-gene prognostic model could be employed to predict the prognosis of GC patients and might be a promising therapeutic targets in GC.