Wu Kun-Zhe, Xu Xiao-Hua, Zhan Cui-Ping, Li Jing, Jiang Jin-Lan
Scientific Research Center, China-Japan Union Hospital of Jilin University, Changchun 130000, Jilin Province, China.
Department of Nephrology, China-Japan Union Hospital of Jilin University, Changchun 130000, Jilin Province, China.
World J Gastrointest Oncol. 2020 Sep 15;12(9):975-991. doi: 10.4251/wjgo.v12.i9.975.
Gastric carcinoma (GC) is one of the most aggressive primary digestive cancers. It has unsatisfactory therapeutic outcomes and is difficult to diagnose early.
To identify prognostic biomarkers for GC patients using comprehensive bioinformatics analyses.
Differentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas and Gene Expression Omnibus databases for GC. Overlapping DEGs were analyzed using univariate and multivariate Cox regression analyses. A risk score model was then constructed and its prognostic value was validated utilizing an independent Gene Expression Omnibus dataset (GSE15459). Multiple databases were used to analyze each gene in the risk score model. High-risk score-associated pathways and therapeutic small molecule drugs were analyzed and predicted, respectively.
A total of 95 overlapping DEGs were found and a nine-gene signature (, and ) was constructed for the GC prognosis prediction. Receiver operating characteristic curve performance in the training dataset (The Cancer Genome Atlas-stomach adenocarcinoma) and validation dataset (GSE15459) demonstrated a robust prognostic value of the risk score model. Multiple database analyses for each gene provided evidence to further understand the nine-gene signature. Gene set enrichment analysis showed that the high-risk group was enriched in multiple cancer-related pathways. Moreover, several new small molecule drugs for potential treatment of GC were identified.
The nine-gene signature-derived risk score allows to predict GC prognosis and might prove useful for guiding therapeutic strategies for GC patients.
胃癌（GC）是侵袭性最强的原发性消化系统癌症之一。其治疗效果不理想，且难以早期诊断。
通过全面的生物信息学分析确定胃癌患者的预后生物标志物。
利用来自癌症基因组图谱（The Cancer Genome Atlas）和基因表达综合数据库（Gene Expression Omnibus）的胃癌基因表达数据筛选差异表达基因（DEGs）。使用单变量和多变量Cox回归分析对重叠的DEGs进行分析。然后构建风险评分模型，并利用独立的基因表达综合数据集（GSE15459）验证其预后价值。使用多个数据库分析风险评分模型中的每个基因。分别分析和预测与高风险评分相关的通路和治疗性小分子药物。
共发现95个重叠的DEGs，并构建了一个包含9个基因的特征标签（，和）用于胃癌预后预测。训练数据集（癌症基因组图谱 - 胃腺癌）和验证数据集（GSE15459）中的受试者工作特征曲线性能表明风险评分模型具有强大的预后价值。对每个基因的多个数据库分析为进一步理解9基因特征标签提供了证据。基因集富集分析表明高风险组富集于多个癌症相关通路。此外，还鉴定了几种潜在治疗胃癌的新型小分子药物。
基于9基因特征标签的风险评分能够预测胃癌预后，可能对指导胃癌患者的治疗策略有用。
