Wu Xiandan, Wu Yongning, Ye Binhua, Wu Fubin, Wang Peien
Department of General Medicine, The First People's Hospital of Wenling.
Department of Surgical Oncology, Taizhou Cancer Hospital, Wenling, PR China.
Medicine (Baltimore). 2020 Jun 26;99(26):e20635. doi: 10.1097/MD.0000000000020635.
Gastric cancer (GC) is the most prevailing digestive tract malignant tumor worldwide with high mortality and recurrence rates. However, its potential molecular mechanism and prognostic biomarkers are still not fully understood. We aim to screen novel prognostic biomarkers related to GC prognosis using comprehensive bioinformatic tools.
Four gene expression microarray data were downloaded from the Gene Expression Omnibus (GEO) database (GSE26942, GSE33335, GSE63089, and GSE79973). Differentially expressed genes (DEGs) between gastric carcinoma and normal gastric tissue samples were identified by an integrated bioinformatic analysis. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using statistical software R. STRING and Cytoscape software were employed to construct protein-protein interaction (PPI) networks. Hub genes with a high score of connectivity identified from the PPI network were identified. Prognostic values of hub genes were evaluated in GSE15459 dataset. Hub genes related to GC overall survival were further validated in GEPIA (Gene Expression Profiling Interactive Analysis) online tool.
A total of 12 upregulated DEGs and 59 downregulated DEGs were identified when the 4 microarray data overlapped. Among them, 10 hub genes with a high score of connectivity were identified. High expression of ghrelin and obestatin prepropeptide (GHRL), BGN, TIMP metallopeptidase inhibitor 1, thrombospondin 2, secreted phosphoprotein 1, and low expression of CHGA were associated with a poor overall survival of gastric cancer (all log rank P < .05). After validation in GEPIA database, only GHRL was confirmed associated with a poor overall survival of gastric cancer (log rank P = .04).
GHRL could be used as a novel biomarker for the prediction of a poor overall survival of gastric cancer, and could be a novel therapeutic target for gastric cancer treatment. However, future experimental studies are still required to validate these findings.
胃癌（GC）是全球最常见的消化道恶性肿瘤，死亡率和复发率都很高。然而，其潜在的分子机制和预后生物标志物仍未完全明确。我们旨在使用综合生物信息学工具筛选与胃癌预后相关的新型预后生物标志物。
从基因表达综合数据库（GEO）（GSE26942、GSE33335、GSE63089和GSE79973）下载四个基因表达微阵列数据。通过综合生物信息学分析鉴定胃癌组织和正常胃组织样本之间的差异表达基因（DEGs）。使用统计软件R进行基因本体（GO）术语富集和京都基因与基因组百科全书（KEGG）通路分析。使用STRING和Cytoscape软件构建蛋白质-蛋白质相互作用（PPI）网络。从PPI网络中鉴定出具有高连接分数的枢纽基因。在GSE15459数据集中评估枢纽基因的预后价值。在GEPIA（基因表达谱交互式分析）在线工具中进一步验证与胃癌总生存相关的枢纽基因。
当四个微阵列数据重叠时，共鉴定出12个上调的DEGs和59个下调的DEGs。其中，鉴定出10个具有高连接分数的枢纽基因。胃泌素释放肽和肥胖抑制素前体（GHRL）、BGN、基质金属蛋白酶抑制剂1、血小板反应蛋白2、分泌性磷蛋白1的高表达以及嗜铬粒蛋白A（CHGA）的低表达与胃癌患者较差的总生存相关（所有对数秩检验P<0.05）。在GEPIA数据库中验证后，只有GHRL被证实与胃癌患者较差的总生存相关（对数秩检验P = 0.04）。
GHRL可作为预测胃癌患者较差总生存的新型生物标志物，并且可能成为胃癌治疗的新型治疗靶点。然而，未来仍需要进行实验研究来验证这些发现。
