Yin Long-Kuan, Yuan Hua-Yan, Liu Jian-Jun, Xu Xiu-Lian, Wang Wei, Bai Xiang-Yu, Wang Pan
Department of Gastrointestinal Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China.
Sichuan Key Laboratory of Medical Imaging, North Sichuan Medical College, Nanchong 637000, Sichuan Province, China.
World J Clin Cases. 2023 Jul 16;11(20):4763-4787. doi: 10.12998/wjcc.v11.i20.4763.
Gastric cancer (GC) is one of the most common malignant tumors with poor prognosis in terms of advanced stage. However, the survival-associated biomarkers for GC remains unclear.
To investigate the potential biomarkers of the prognosis of patients with GC, so as to provide new methods and strategies for the treatment of GC.
RNA sequencing data from The Cancer Genome Atlas (TCGA) database of STAD tumors, and microarray data from Gene Expression Omnibus (GEO) database (GSE19826, GSE79973 and GSE29998) were obtained. The differentially expressed genes (DEGs) between GC patients and health people were picked out using R software (x64 4.1.3). The intersections were underwent between the above obtained co-expression of differential genes (co-DEGs) and the DEGs of GC from Gene Expression Profiling Interactive Analysis database, and Gene Ontology (GO) analysis, Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA), Protein-protein Interaction (PPI) analysis and Kaplan-Meier Plotter survival analysis were performed on these DEGs. Using Immunohistochemistry (IHC) database of Human Protein Atlas (HPA), we verified the candidate Hub genes.
With DEGs analysis, there were 334 co-DEGs, including 133 up-regulated genes and 201 down-regulated genes. GO enrichment analysis showed that the co-DEGs were involved in biological process, cell composition and molecular function pathways. KEGG enrichment analysis suggested the co-DEGs pathways were mainly enriched in ECM-receptor interaction, protein digestion and absorption pathways, . GSEA pathway analysis showed that co-DEGs mainly concentrated in cell cycle progression, mitotic cell cycle and cell cycle pathways, . PPI analysis showed 84 nodes and 654 edges for the co-DEGs. The survival analysis illustrated 11 Hub genes with notable significance for prognosis of patients were screened. Furtherly, using IHC database of HPA, we confirmed the above candidate Hub genes, and 10 Hub genes that associated with prognosis of GC were identified, namely BGN, CEP55, COL1A2, COL4A1, FZD2, MAOA, PDGFRB, SPARC, TIMP1 and VCAN.
The 10 Hub genes may be the potential biomarkers for predicting the prognosis of GC, which can provide new strategies and methods for the diagnosis and treatment of GC.
胃癌（GC）是最常见的恶性肿瘤之一，晚期预后较差。然而，GC的生存相关生物标志物仍不清楚。
探讨GC患者预后的潜在生物标志物，为GC的治疗提供新的方法和策略。
获取来自癌症基因组图谱（TCGA）数据库中STAD肿瘤的RNA测序数据，以及来自基因表达综合数据库（GEO）（GSE19826、GSE79973和GSE29998）的微阵列数据。使用R软件（x64 4.1.3）挑选出GC患者与健康人之间的差异表达基因（DEG）。将上述获得的差异基因共表达（co-DEG）与来自基因表达谱交互分析数据库中GC的DEG进行交集分析，并对这些DEG进行基因本体（GO）分析、京都基因与基因组百科全书（KEGG）通路分析、基因集富集分析（GSEA）、蛋白质-蛋白质相互作用（PPI）分析和Kaplan-Meier Plotter生存分析。利用人类蛋白质图谱（HPA）的免疫组织化学（IHC）数据库验证候选枢纽基因。
通过DEG分析，有334个co-DEG，包括133个上调基因和201个下调基因。GO富集分析表明，co-DEG参与生物过程、细胞组成和分子功能途径。KEGG富集分析表明，co-DEG途径主要富集在细胞外基质-受体相互作用、蛋白质消化和吸收途径。GSEA途径分析表明，co-DEG主要集中在细胞周期进程、有丝分裂细胞周期和细胞周期途径。PPI分析显示co-DEG有84个节点和654条边。生存分析表明筛选出11个对患者预后有显著意义的枢纽基因。进一步地，利用HPA的IHC数据库，我们证实了上述候选枢纽基因，并鉴定出10个与GC预后相关的枢纽基因，即BGN、CEP55、COL1A2、COL4A1、FZD2、MAOA、PDGFRB、SPARC、TIMP1和VCAN。
这10个枢纽基因可能是预测GC预后的潜在生物标志物，可为GC的诊断和治疗提供新的策略和方法。
