Zuo Z-G, Zhang X-F, Ye X-Z, Zhou Z-H, Wu X-B, Ni S-C, Song H-Y
Department of Colorectal Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, P.R. China.
Eur Rev Med Pharmacol Sci. 2016 Jul;20(14):3017-25.
RNA-seq data of rectal adenocarcinoma (READ) were analyzed with bioinformatics tools to unveil potential biomarkers in the disease.
RNA-seq data of READ were downloaded from The Cancer Genome Atlas (TCGA) database. Differential analysis was performed with package edgeR. False discovery rate (FDR) < 0.05 and |log2 (fold change)|>1 were set as cut-off values to screen out differentially expressed genes (DEGs). Gene coexpression network was constructed with package Ebcoexpress. Gene Ontology enrichment analysis was performed for the DEGs in the gene coexpression network with DAVID online tool. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was also performed for the genes with KOBASS 2.0.
A total of 620 DEGs, 389 up-regulated genes, and 231 down-regulated genes, were identified from 163 READ samples and 9 normal controls. A gene coexpression network consisting of 71 DEGs and 253 edges were constructed. Genes were associated with ribosome and focal adhesion functions. Three modules were identified, in which genes were involved in muscle contraction, negative regulation of glial cell proliferation and extracellular matrix organization functions, respectively. Several critical hub genes were disclosed, such as RPS2, MMP1, MMP11 and FAM83H. Thirteen relevant small molecule drugs were identified, such as scriptaid and spaglumic acid. A total of 8 TFs and 5 miRNAs were acquired, such as MYC, NFY, STAT5A, miR-29, miR-200 and miR-19.
Several critical genes and relevant drugs, TFs and miRNAs were revealed in READ. These findings could advance the understanding about the disease and benefit therapy development.
运用生物信息学工具分析直肠腺癌（READ）的RNA测序数据，以揭示该疾病潜在的生物标志物。
从癌症基因组图谱（TCGA）数据库下载READ的RNA测序数据。使用edgeR软件包进行差异分析。将错误发现率（FDR）<0.05和|log2（倍数变化）|>1设定为筛选差异表达基因（DEG）的临界值。使用Ebcoexpress软件包构建基因共表达网络。通过DAVID在线工具对基因共表达网络中的DEG进行基因本体富集分析。还使用KOBASS 2.0对基因进行京都基因与基因组百科全书通路富集分析。
从163例READ样本和9例正常对照中，共鉴定出620个DEG，其中389个上调基因和231个下调基因。构建了一个由71个DEG和253条边组成的基因共表达网络。这些基因与核糖体和粘着斑功能相关。鉴定出三个模块，其中的基因分别参与肌肉收缩、神经胶质细胞增殖的负调控和细胞外基质组织功能。揭示了几个关键的枢纽基因，如RPS2、MMP1、MMP11和FAM83H。鉴定出13种相关小分子药物，如曲古抑菌素A和谷氨酸。共获得8个转录因子（TF）和5个微小RNA（miRNA），如MYC、NFY、STAT5A、miR-29、miR-200和miR-19。
在READ中揭示了几个关键基因以及相关药物、TF和miRNA。这些发现有助于增进对该疾病的了解，并有利于治疗方案的开发。
