Mining hub genes related to liver cancer stage in TCGA database with weighted correlation network analysis method
10.3760/cma.j.issn.1673-4904.2019.11.004
- VernacularTitle: 加权基因共表达网络分析方法挖掘TCGA数据库中与肝癌分期相关的关键基因
- Author:
Shuqiang LI
1
;
Wenyan ZHAO
1
;
Baolin LIU
1
Author Information
1. Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang 110004, China
- Publication Type:Journal Article
- Keywords:
Liver neoplasms;
Database;
Weighted correlation network analysis;
Data mining
- From:
Chinese Journal of Postgraduates of Medicine
2019;42(11):974-977
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Primary liver cancer is a kind of gastrointestinal malignant tumor with high morbidity and mortality. Early diagnosis is difficult, with postoperative high recurrence rate and high metastasis rate, poor prognosis, so it is particularly important to better understand the occurrence and development of liver cancer from the gene level, so as to provide theoretical basis for gene therapy or targeted drug therapy in the future.
Methods:TCGA database for liver cancer gene transcriptome data information and clinical phenotype information was downloaded, and R language edgeR package was used to standardize the transcriptome data. The log FC > 1, FDR < 0.01 gene was set to be expressing differences gene, and then the weighted correlation network analysis (WGCNA) methods was used to analyze the relationship between liver cancer stage (stage) and the differentially expressed genes to look for hub genes.
Results:Four hub genes related to tumor staging were identified (TPX2, HJURP, KIAA1524, SGO2). Kaplan-meier survival curve was drawn and it was found that their expression level was significantly correlated with patient survival time, and high expression level was an independent prognostic factor.
Conclusions:WGCNA method is used to mine the TCGA database information and find the hub genes related to tumor staging. The relationship between TPX2, HJURP, KIAA1524 and liver cancer has been reported in the literature, except SGO2. This study will provide important theoretical basis for the follow-up study of liver cancer.
