Digestive Medicine Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen 518107, China.
Department of Thoracic Surgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China.
Curr Oncol. 2022 Dec 23;30(1):171-183. doi: 10.3390/curroncol30010014.
It is widely acknowledged that the molecular biological characteristics of diffuse-type gastric cancer are different from intestinal-type gastric cancer. Notwithstanding that significant progress in high-throughput sequencing technology has been made, there is a paucity of effective prognostic biomarkers for diffuse gastric cancer for clinical practice.
We downloaded four GEO datasets (GSE22377, GSE38749, GSE47007 and GSE62254) to establish and validate a prognostic two-gene signature for diffuse gastric cancer. The TGCA-STAD dataset was used for external validation. The optimal gene signature was established by using Cox regression analysis. Receiver operating characteristic (ROC) methodology was used to find the best prognostic model. Gene set enrichment analysis was used to analyze the possible signaling pathways of the two genes (MEF2C and TRIM15).
A total of four differently expressed genes (DEGs) (two upregulated and two downregulated) were identified. After a comprehensive analysis, two DEGs (MEF2C and TRIM15) were utilized to construct a prognostic model. A prognostic prediction model was constructed according to T stage, N stage, M stage and the expression of MEF2C and TRIM15. The area under the time-dependent receiver operator characteristic was used to evaluate the performance of the prognosis model in the GSE62254 dataset.
We demonstrated that MEF2C and TRIM15 might be key genes. We also established a prognostic nomogram based on the two-gene signature that yielded a good performance for predicting overall survival in diffuse-type gastric cancer.
弥漫型胃癌的分子生物学特征不同于肠型胃癌,这一点已得到广泛认可。尽管高通量测序技术取得了重大进展,但对于弥漫型胃癌,临床上仍缺乏有效的预后生物标志物。
我们下载了四个 GEO 数据集(GSE22377、GSE38749、GSE47007 和 GSE62254),用于建立和验证弥漫型胃癌的预后双基因signature。使用 TCGA-STAD 数据集进行外部验证。使用 Cox 回归分析建立最优基因 signature。使用接收器操作特征(ROC)方法寻找最佳预后模型。基因集富集分析用于分析这两个基因(MEF2C 和 TRIM15)的可能信号通路。
共鉴定出四个差异表达基因(DEGs)(两个上调和两个下调)。经过综合分析,选取两个 DEGs(MEF2C 和 TRIM15)构建预后模型。根据 T 分期、N 分期、M 分期以及 MEF2C 和 TRIM15 的表达情况,构建了预后预测模型。使用时间依赖性接收器操作特征下的面积来评估预后模型在 GSE62254 数据集的性能。
我们证明 MEF2C 和 TRIM15 可能是关键基因。我们还建立了一个基于双基因 signature 的预后列线图,该列线图在预测弥漫型胃癌的总生存期方面表现良好。