Suppr超能文献

用于预测结肠腺癌预后的 65 基因标志物。

A 65‑gene signature for prognostic prediction in colon adenocarcinoma.

机构信息

Department of Gastrointestinal Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214062, P.R. China.

Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu 214062, P.R. China.

出版信息

Int J Mol Med. 2018 Apr;41(4):2021-2027. doi: 10.3892/ijmm.2018.3401. Epub 2018 Jan 18.

Abstract

The aim of the present study was to examine the molecular factors associated with the prognosis of colon cancer. Gene expression datasets were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases to screen differentially expressed genes (DEGs) between colon cancer samples and normal samples. Survival‑related genes were selected from the DEGs using the Cox regression method. A co‑expression network of survival‑related genes was then constructed, and functional clusters were extracted from this network. The significantly enriched functions and pathways of the genes in the network were identified. Using Bayesian discriminant analysis, a prognostic prediction system was established to distinguish the positive from negative prognostic samples. The discrimination efficacy of the system was validated in the GSE17538 dataset using Kaplan‑Meier survival analysis. A total of 636 and 1,892 DEGs between the colon cancer samples and normal samples were screened from the TCGA and GSE44861 dataset, respectively. There were 155 survival‑related genes selected. The co‑expression network of survival‑related genes included 138 genes, 534 lines (connections) and five functional clusters, including the signaling pathway, cellular response to cAMP, and immune system process functional clusters. The molecular function, cellular components and biological processes were the significantly enriched functions. The peroxisome proliferator‑activated receptor signaling pathway, Wnt signaling pathway, B cell receptor signaling pathway, and cytokine‑cytokine receptor interactions were the significant pathways. A prognostic prediction system based on a 65‑gene signature was established using this co‑expression network. Its discriminatory effect was validated in the TCGA dataset (P=3.56e‑12) and the GSE17538 dataset (P=1.67e‑6). The 65‑gene signature included kallikrein‑related peptidase 6 (KLK6), collagen type XI α1 (COL11A1), cartilage oligomeric matrix protein, wingless‑type MMTV integration site family member 2 (WNT2) and keratin 6B. In conclusion, a 65‑gene signature was screened in the present study, which showed a prognostic prediction effect in colon adenocarcinoma. KLK6, COL11A1, and WNT2 may be suitable prognostic predictors for colon adenocarcinoma.

摘要

本研究旨在探讨与结肠癌预后相关的分子因素。从癌症基因组图谱(TCGA)和基因表达综合数据库中下载基因表达数据集,筛选结肠癌样本与正常样本之间差异表达基因(DEGs)。使用 Cox 回归方法从 DEGs 中筛选生存相关基因。然后构建生存相关基因的共表达网络,并从该网络中提取功能簇。鉴定网络中基因的显著富集功能和途径。使用贝叶斯判别分析建立预测系统,以区分阳性和阴性预后样本。使用 Kaplan-Meier 生存分析在 GSE17538 数据集验证该系统的区分效能。从 TCGA 和 GSE44861 数据集分别筛选出结肠癌样本与正常样本之间的 636 个和 1892 个 DEGs,筛选出 155 个生存相关基因。生存相关基因的共表达网络包括 138 个基因、534 条线(连接)和 5 个功能簇,包括信号通路、细胞对 cAMP 的反应和免疫系统过程功能簇。分子功能、细胞成分和生物学过程是显著富集的功能。过氧化物酶体增殖物激活受体信号通路、Wnt 信号通路、B 细胞受体信号通路和细胞因子-细胞因子受体相互作用是显著通路。使用该共表达网络建立基于 65 个基因特征的预后预测系统,并在 TCGA 数据集(P=3.56e-12)和 GSE17538 数据集(P=1.67e-6)中验证其判别效果。该 65 个基因特征包括激肽释放酶相关肽 6(KLK6)、胶原 XI 型α1(COL11A1)、软骨寡聚基质蛋白、无翅型 MMV 整合位点家族成员 2(WNT2)和角蛋白 6B。综上所述,本研究筛选出一个 65 个基因特征,在结肠腺癌中具有预后预测作用。KLK6、COL11A1 和 WNT2 可能是结肠腺癌的合适预后预测因子。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3efc/5810222/897f58d46b3f/IJMM-41-04-2021-g01.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验