Mining hub genes related to liver cancer stage in TCGA database with weighted correlation network analysis method

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. Key words: Liver neoplasms; Database; Weighted correlation network analysis; Data mining