Jiang Fei, Chen Xiaowei, Shen Yan, Shen Xiaobing
Key Laboratory of Environmental Medical Engineering and Education Ministry, Nanjing Public Health College, Southeast University, Nanjing, China.
Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.
Front Genet. 2022 Jul 19;13:891744. doi: 10.3389/fgene.2022.891744. eCollection 2022.
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. 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. 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 ( = 0.006). Multivariate Cox regression analyses showed that the model could be an independent predictor ( < 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. This four-gene prognostic model could be applied to predict the prognosis of GC patients and might be a promising therapeutic target in GC.
胃癌（GC）是全球范围内的恶性肿瘤之一。Janus（JAK）-信号转导和转录激活因子（STAT）信号通路参与细胞生物学过程和免疫功能。然而，它们之间的关联仍未得到系统描述。因此，在本研究中，我们旨在确定参与JAK-STAT信号通路和GC的关键基因以及潜在机制。癌症基因组图谱（TCGA）数据库是GC患者RNA测序数据的来源。基因表达综合数据库（GEO）用作验证集。使用最小绝对收缩和选择算子（LASSO）、生存分析、单因素和多因素Cox回归分析以及受试者工作特征曲线（ROC）分析来检验JAK-STAT信号通路相关预后预测模型的预测价值，以检验该模型的预测价值。采用定量实时聚合酶链反应（qRT-PCR）和卡方检验分别验证模型中基因的表达并评估这些基因与GC患者临床病理参数之间的关联。然后，利用基因本体论（GO）、京都基因与基因组百科全书（KEGG）、基因集富集分析3.0版（GSEA）、基于序列的RNA腺苷甲基化位点预测器（SRAMP）在线网站以及RNA免疫沉淀（RIP）实验来预测模型相关的潜在通路、m6A修饰以及模型基因与m6A之间的关联。构建了一个四基因预后模型（GHR、PIM1、IFNA8和IFNB1），即风险评分（riskScore）。Kaplan-Meier曲线表明，高风险评分（riskScore）表达的患者预后比低风险评分表达的患者差（P = 0.006）。多因素Cox回归分析表明，该模型可能是一个独立的预测因子（P < 0.001；HR = 3.342，95% CI = 1.834 - 6.088）。5年时间依赖性ROC曲线下面积（AUC）达到0.655。训练测试集验证了这些结果。进一步分析揭示了癌症相关通路、m6A修饰的富集以及m6A与这四个基因之间的直接相互作用。这个四基因预后模型可用于预测GC患者的预后，可能是GC中有前景的治疗靶点。
