School of Medical Laboratory, Shao Yang University, Shaoyang, Hunan, China (mainland).
Med Sci Monit. 2018 Jul 5;24:4625-4633. doi: 10.12659/MSM.907224.
BACKGROUND Colorectal cancer (CRC) is a common malignant tumor with high incidence and mortality worldwide. The aim of this study was to evaluate the association between differentially expressed genes (DEGs), which may function as biomarkers for CRC prognosis and therapies, and the clinical outcome in patients with CRC. MATERIAL AND METHODS A total of 116 normal mucous tissue and 930 CRC tissue datasets were downloaded from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). After screening DEGs based on limma package in R. Gene Ontology (GO) and KEGG enrichment analysis as well as the protein-protein interaction (PPI) networks were performed to predict the function of these DEGs. Meanwhile, Cox proportional hazards regression was used to build a prognostic model of these DEGs. Then, Kaplan-Meier risk analysis was used to test the model in TCGA datasets and validation datasets. RESULTS In the present study, 300 DEGs with 100 upregulated genes and 200 downregulated genes were identified. The PPI networks including 162 DEGs and 256 nodes were constructed and 2 modules with high degree were selected. Moreover, 5 genes (MMP1, ACSL6, SMPD1, PPARGC1A, and HEPACAM2) were identified using the Cox proportional hazards stepwise regression. Kaplan-Meier risk curve in the TCGA and validation cohorts showed that high-risk group had significantly poor overall survival than the low-risk group. CONCLUSIONS Our study provided insights into the mechanisms of CRC formation and found 5 prognostic genes, which could potentially inform further studies and clinical therapies.
结直肠癌（CRC）是一种常见的恶性肿瘤，具有全球范围内高发病率和死亡率的特点。本研究旨在评估差异表达基因（DEGs）与 CRC 患者临床结局之间的关系，这些基因可能作为 CRC 预后和治疗的生物标志物。
从基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）中下载了 116 份正常黏膜组织和 930 份 CRC 组织数据集。然后基于 R 语言中的 limma 包筛选 DEGs，进行基因本体论（GO）和京都基因与基因组百科全书（KEGG）富集分析以及蛋白质-蛋白质相互作用（PPI）网络分析，以预测这些 DEGs 的功能。同时，使用 Cox 比例风险回归构建这些 DEGs 的预后模型。然后，在 TCGA 数据集和验证数据集中进行 Kaplan-Meier 风险分析来测试该模型。
本研究共鉴定出 300 个 DEGs，其中包括 100 个上调基因和 200 个下调基因。构建了包含 162 个 DEGs 和 256 个节点的 PPI 网络，并选择了两个具有高节点度的模块。此外，通过 Cox 比例风险逐步回归鉴定出 5 个基因（MMP1、ACSL6、SMPD1、PPARGC1A 和 HEPACAM2）。在 TCGA 和验证队列中，Kaplan-Meier 风险曲线表明，高风险组的总生存率明显低于低风险组。
本研究深入了解了 CRC 形成的机制，并发现了 5 个预后基因，这可能为进一步的研究和临床治疗提供信息。
