Frontiers in Genetics (Jul 2022)
Identification and Validation of an m6A Modification of JAK-STAT Signaling Pathway–Related Prognostic Prediction Model in Gastric Cancer
Abstract
Background: Gastric cancer (GC) is one of the malignant tumors worldwide. Janus (JAK)–signal transduction and activator of transcription (STAT) signaling pathway is involved in cellular biological process and immune function. However, the association between them is still not systematically described. Therefore, in this study, 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. The predictive value of the JAK-STAT signaling pathway-related prognostic prediction model was examined using least absolute shrinkage and selection operator (LASSO); survival, univariate, and 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 the model and assess the association between the genes and clinicopathological parameters of GC patients, respectively. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis, version 3.0 (GSEA), sequence-based RNA adenosine methylation site predictor (SRAMP) online websites, and RNA immunoprecipitation (RIP) experiments 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 those with low riskScore expression (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 5-year area under time-dependent ROC curve (AUC) reached 0.655. The training test set verified these results. Further analyses unveiled an enrichment of cancer-related pathways, m6A modifications, and the direct interaction between m6A and the four genes.Conclusion: This four-gene prognostic model could be applied to predict the prognosis of GC patients and might be a promising therapeutic target in GC.
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