ArticleFrontiers in oncology2021
Identification of Ten Core Hub Genes as Potential Biomarkers and Treatment Target for Hepatoblastoma.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Hepatoblastoma: an investigation of diagnostic, prognostic, and therapeutic gene targets and biomarkers.Pediatric research · 2026Pooled it
- Identification of MAD2L1 as a novel biomarker for hepatoblastoma through bioinformatics and machine learning approaches.Frontiers in oncology · 2025Article
- HAND2-AS1 plays a tumor-suppressive role in hepatoblastoma through the negative regulation of CDK1.Heliyon · 2024Article
- Article
- Decreased Expression of GLYATL1 Predicts Poor Prognosis in Patients with Clear Cell Renal Cell Carcinoma.International journal of general medicine · 2023Article
- Screening of Potential Biomarkers in the Peripheral Serum for Steroid-Induced Osteonecrosis of the Femoral Head Based on WGCNA and Machine Learning Algorithms.Disease markers · 2022Article
- Exploration of Potential Biomarkers and Immune Landscape for Hepatoblastoma: Evidence from Machine Learning Algorithm.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- Article
- miR-126 in Extracellular Vesicles Derived from Hepatoblastoma Cells Promotes the Tumorigenesis of Hepatoblastoma through Inducing the Differentiation of BMSCs into Cancer Stem Cells.Journal of immunology research · 2021Article
- Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma.International journal of general medicine · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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,
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.
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