Table_1_Identification of Ten Core Hub Genes as Potential Biomarkers and Treatment Target for Hepatoblastoma.xlsx

BackgroundThis study aimed to systematically investigate gene signatures for hepatoblastoma (HB) and identify potential biomarkers for its diagnosis and treatment.
Materials and MethodsGSE131329 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.
ResultsA 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 using the 114 hub genes as feature genes, resulting in a 95.5% true positive rate when classifying HB and normal samples. A total of 35 core hub genes were obtained through the mean decrease in accuracy and mean decrease Gini of the random forest model. The classification efficiency of the random forest model was 81.4%. Finally, CDK1, TOP2A, ADRA1A, FANCI, XRCC1, TPX2, CCNB2, CDK4, GLYATL1, and CFHR3 were identified by cross-comparison with the InnateDB database.
ConclusionOur study established a random forest classifier that identified 10 core genes in HB. These findings may be beneficial for the diagnosis, prediction, and targeted therapy of HB.

背景
本研究旨在系统探究肝母细胞瘤（hepatoblastoma, HB）的基因特征，并筛选其诊断与治疗的潜在生物标志物。
材料与方法
从基因表达综合数据库（Gene Expression Omnibus, GEO）中获取GSE131329与GSE81928数据集。采用R语言Limma包鉴定肝母细胞瘤样本与正常样本间的差异表达基因（differentially expressed genes, DEGs）。通过加权基因共表达网络分析（weighted gene correlation network analysis, WGCNA）分析两组基因的网络特征相似性。使用Cytoscape软件完成可视化操作并筛选核心基因，同时依托Cytoscape构建核心基因的蛋白质相互作用（Protein-Protein Interaction, PPI）网络。利用ClueGO工具开展核心基因的基因本体（Gene Ontology, GO）富集分析与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）通路富集分析。以GSE131329数据集作为训练集，基于核心基因构建随机森林分类器，并通过GSE81928数据集验证其可靠性。将所得核心枢纽基因与InnateDB数据库进行交叉比对，以鉴定先天核心基因。
结果
本研究共鉴定出4244个肝母细胞瘤相关差异表达基因。通过WGCNA筛选出4个与疾病状态显著相关的基因模块。从各节点排名前20的基因中，共获得114个核心基因。114个核心基因的PPI网络共包含6982个相互作用对与3700个节点。核心基因的GO富集与KEGG通路富集分析显示，其显著富集于MAPK信号通路、细胞周期、p53通路等与肝母细胞瘤发生发展密切相关的关键通路。以114个核心基因作为特征基因构建随机森林分类器，在区分肝母细胞瘤与正常样本时的真阳性率达95.5%。通过随机森林模型的平均准确率下降值与平均基尼系数下降值，共筛选出35个核心枢纽基因，该模型的分类效能为81.4%。最终通过与InnateDB数据库交叉比对，鉴定出CDK1、TOP2A、ADRA1A、FANCI、XRCC1、TPX2、CCNB2、CDK4、GLYATL1及CFHR3共10个核心基因。
结论
本研究构建了可识别肝母细胞瘤10个核心基因的随机森林分类器。本研究结果可为肝母细胞瘤的诊断、预后预测及靶向治疗提供潜在参考价值。