ArticleDiscover oncology2026
Integrative bioinformatics analysis identifies ubiquitin-related genes and their regulatory networks in hepatoblastoma.
Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundHepatoblastoma (HB), the most common pediatric liver malignancy, poses significant challenges in early diagnosis and treatment. To identify ubiquitin-related genes (UbRGs) and signaling pathways involved in HB progression, we analyzed HB-associated gene expression profiles.
methodsDifferential expression analysis was performed on a combined Gene Expression Omnibus dataset integrating two HB datasets (GSE81928 and GSE133039). UbRGs were retrieved from the GeneCards database and the literature. Differentially expressed genes (DEGs) were intersected with UbRGs to identify ubiquitin-related DEGs (UbRDEGs). Subsequent analyses included gene set enrichment analysis (GSEA), protein–protein interaction (PPI) network construction, regulatory network assessment, receiver operating characteristic (ROC) curve evaluation, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Immune infiltration analysis was performed to evaluate the biological and clinical relevance of UbRDEGs.
resultsWe identified 3,591 DEGs in HB samples compared with those in control samples, comprising 1,855 upregulated and 1,736 downregulated genes. Intersecting DEGs with UbRGs yielded 30 UbRDEGs. GSEA indicated that these genes exhibited notable enrichment within the pathways associated with fatty acid metabolism, biological oxidation, and cellular senescence. PPI analysis revealed 18 hub genes with complex interactions, supported by mRNA–TF and mRNA–miRNA regulatory networks. ROC curve analysis revealed that PINK1 demonstrated high diagnostic accuracy in differentiating between HB and control samples. GO and KEGG analyses showed that 18 hub genes were enriched in protein deubiquitination, protein polyubiquitination, and positive cell cycle regulation. Significant variations in 15 distinct immune cell types were observed across HB samples through immune infiltration analysis, highlighting the essential influence of the immune microenvironment in HB progression.
conclusionsThis study highlights UbRGs as key contributors to HB, identifying potentially relevant molecules and thereby providing clues for further research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.