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基于加权基因共表达网络分析的 hub 基因及其对弥漫性大 B 细胞淋巴瘤的关键作用。

Hub Gene and Its Key Effects on Diffuse Large B-Cell Lymphoma by Weighted Gene Coexpression Network Analysis.

机构信息

Department of Ophthalmology, The First Affiliated Hospital of Zhengzhou University, China.

The Eye Hospital, School of Ophthalmology and Optometry, Wenzhou Medical University, China.

出版信息

Biomed Res Int. 2021 Nov 27;2021:8127145. doi: 10.1155/2021/8127145. eCollection 2021.

Abstract

Diffuse large B-cell lymphoma (DLBC) is a kind of tumor with rapid progress and poor prognosis. Therefore, it is necessary to explore new biomarkers or therapeutic targets to assist in diagnosis or treatment. This study is aimed at screening hub genes by weighted gene coexpression network analysis (WGCNA) and exploring the significance of overall survival (OS) in DLBC patients. Statistical data using WGCNA to analyze mRNA expression in DLBC patients came from The Cancer Genome Atlas (TCGA) dataset. After analyzing with clinical information, the biological functions of hub genes were detected. Survival analysis, Cox regression detection, and correlation analysis of the hub genes were carried out. The potential function of the hub gene related to prognosis was predicted by gene set enrichment analysis (GSEA). The results showed that , , , and expression in normal tissues was significantly higher than that in cancer tissues ( < 0.01). Survival analysis showed that patients with high and were associated with better OS ( < 0.01). and genes were mainly enriched in the regulation of ROS and oxidative stress. The two hub genes related to the prognosis of DLBC were identified and verified based on WGCNA. Survival analysis showed that the overexpression of and in DLBC might be beneficial to the prognosis. These findings identified vital pathways and genes that may become new therapeutic targets and contribute to prognostic indicators.

摘要

弥漫性大 B 细胞淋巴瘤(DLBC)是一种进展迅速、预后不良的肿瘤。因此,有必要探索新的生物标志物或治疗靶点,以协助诊断或治疗。本研究旨在通过加权基因共表达网络分析(WGCNA)筛选枢纽基因,并探讨其在 DLBC 患者总生存(OS)中的意义。使用 WGCNA 分析 DLBC 患者 mRNA 表达的统计数据来自癌症基因组图谱(TCGA)数据集。经过与临床信息分析后,检测了枢纽基因的生物学功能。进行了生存分析、Cox 回归检测以及枢纽基因的相关性分析。通过基因集富集分析(GSEA)预测了与预后相关的枢纽基因的潜在功能。结果表明,在正常组织中的 、 、 和 表达显著高于癌组织(<0.01)。生存分析表明,高表达 和 的患者 OS 较好(<0.01)。 和 基因主要富集在 ROS 和氧化应激的调节。基于 WGCNA 鉴定和验证了与 DLBC 预后相关的两个枢纽基因。生存分析表明,DLBC 中 和 的过表达可能对预后有益。这些发现确定了重要的途径和基因,它们可能成为新的治疗靶点,并有助于预后指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da74/8643236/484ece9e31fe/BMRI2021-8127145.001.jpg

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