Identification of Ten Core Hub Genes as Potential Biomarkers and Treatment Target for Hepatoblastoma

作者：Rui Sun, Simin Li, Ke Zhao, Mei Diao, Long Li · 发表于：Frontiers in Oncology · 年份：2021 · DOI：10.3389/fonc.2021.591507 · 被引用次数：42 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Ferroptosis and cancer prognosis、Kruppel-like factors research

Background This study aimed to systematically investigate gene signatures for hepatoblastoma (HB) and identify potential biomarkers for its diagnosis and treatment. Materials and Methods GSE131329 and GSE81928 were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between hepatoblastoma and normal samples were identified using the Limma package in R. Then, the similarity of network traits between two sets of genes was analyzed by weighted gene correlation network analysis (WGCNA). Cytoscape was used to visualize and select hub genes. PPI network of hub genes was construed by Cytoscape. GO enrichment and KEGG pathway analyses of hub genes were carried out using ClueGO. The random forest classifier was constructed based on the hub genes using the GSE131329 dataset as the training set, and its reliability was validated using the GSE81928 dataset. The resulting core hub genes were combined with the InnateDB database to identify the innate core genes. Results A total of 4244 DEGs in HB were identified. WGCNA identified four modules that were significantly correlated with the disease status. A total of 114 hub genes were obtained within the top 20 genes of each node rank. 6982 relation pairs and 3700 nodes were contained in the PPI network of 114 hub genes. GO enrichment and KEGG pathway analyses of hub genes were focused on MAPK, cell cycle, p53, and other crucial pathways involved in HB. A random forest classifier was constructed usin...