Department of General Surgery, The Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200025, P.R. China.
Mol Med Rep. 2018 Jan;17(1):109-116. doi: 10.3892/mmr.2017.7913. Epub 2017 Oct 27.
The aim of the current study was to identify potential biomarkers of childhood obesity, and investigate molecular mechanisms and candidate agents in order to improve therapeutic strategies for childhood obesity. The GSE9624 gene expression profile was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) in omental adipose tissues were analyzed with limma package by comparing samples from obese and normal control children. Two‑way hierarchical clustering was applied using the pheatmap package. The co‑expression (CE) analysis was performed using online CoExpress software. Subsequent to functional classification via the GOSim package, the gene network enriched by DEGs was visualized using the Cytoscape package. The codon usage bias of the DEGs was then examined using the CAI program from the European Molecular Biology Open Software Suite. In total, 583 DEGs (273 upregulated genes and 310 downregulated genes) were observed in the omental adipose tissues between samples from obese and normal control children. Hierarchical clustering identified a significant difference between samples from obese and normal control children. Subsequent to CE analysis, 130 DEGs, which were classified into 4 clusters, were selected. The following 3 upregulated and 2 downregulated genes were identified to be significant: Upregulated genes, microtubule‑associated protein tau (MAPT), destrin (actin depolymerizing factor) (DSTN) and spectrin, β, non‑erythrocytic 1 (SPTBN1); downregulated genes, Rho/Rac guanine nucleotide exchange factor 2 (ARHGEF2) and spindle and kinetochore associated complex subunit 1 (SKA1). The top 3 amino acids were identified to be glycine, leucine and serine with a high bias. The DEGs MAPT, DSTN, SPTBN1, ARHGEF2 and SKA1 are suggested to be candidate biomarkers for childhood obesity.
本研究旨在寻找儿童肥胖的潜在生物标志物，并研究分子机制和候选药物，以改善儿童肥胖的治疗策略。从基因表达综合数据库（GEO）下载 GSE9624 基因表达谱。通过比较肥胖和正常对照儿童的样本，使用 limma 软件包分析大网膜脂肪组织中的差异表达基因（DEGs）。使用 pheatmap 软件包进行双向层次聚类。使用在线 CoExpress 软件进行共表达（CE）分析。通过 GOSim 软件包进行功能分类后，使用 Cytoscape 软件包可视化 DEGs 富集的基因网络。使用欧洲分子生物学开放软件套件（EMBOSS）中的 CAI 程序检查 DEGs 的密码子使用偏性。结果显示，肥胖和正常对照儿童大网膜脂肪组织样本之间共观察到 583 个 DEGs（273 个上调基因和 310 个下调基因）。层次聚类确定了肥胖和正常对照儿童样本之间的显著差异。CE 分析后，选择了 130 个 DEGs，分为 4 个簇。以下 3 个上调基因和 2 个下调基因具有显著差异：上调基因，微管相关蛋白 tau（MAPT），动力蛋白解聚因子（DSTN）和 spectrin，β，非红细胞 1（SPTBN1）；下调基因，Rho/Rac 鸟嘌呤核苷酸交换因子 2（ARHGEF2）和纺锤体和着丝粒相关复合物亚基 1（SKA1）。确定前 3 位氨基酸为甘氨酸、亮氨酸和丝氨酸，偏性较高。MAPT、DSTN、SPTBN1、ARHGEF2 和 SKA1 等 DEGs 可能是儿童肥胖的候选生物标志物。
