Evidence map›Paper›PMID 42065042›Full record

ArticleOncology research2026

Integrated Global Phosphoproteomic, Bioinformatic, and Machine Learning Analysis Reveals Regulatory Networks of TOP1, TOP2A, TOP2B, and C1orf35 in Hepatocellular Carcinoma (HCC).

Aktham Mestareehi

Abstract read
In one paragraph

Article in Oncology research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Aktham MestareehiDepartment of Applied Pharmaceutical Sciences and Clinical Pharmacy, Faculty of Pharmacy, Isra University, Amman, Jordan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, largely due to late diagnosis, molecular heterogeneity, and limited prognostic biomarkers. Aberrant protein phosphorylation plays a critical role in cancer progression by regulating DNA damage response, cell cycle control, and signaling pathways; however, the prognostic relevance of phosphorylation events in key DNA topology-related proteins remains incompletely understood. This study aimed to investigate the prognostic significance of phosphorylation of TOP1, TOP2A, TOP2B, and C1orf35 in HCC and to characterize their associated molecular features to identify potential diagnostic and therapeutic biomarkers. Methods: Publicly available HCC phosphoproteomic and proteomic datasets were analyzed to identify significantly upregulated phosphorylation sites of TOP1, TOP2A, TOP2B, and C1orf35. Integrated bioinformatics and machine learning approaches were applied, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) network analysis, drug-gene interaction analysis, and survival analyses (overall and disease-free survival). Results: A total of 11,547 phosphorylation sites corresponding to 4043 phosphoproteins were quantified from 159 HCC patients. Phosphorylation of TOP1, TOP2A, TOP2B, and C1orf35 was significantly upregulated. Enriched pathways included DNA damage response, homologous recombination repair, cell cycle regulation, SUMOylation, and TP53 signaling. PPI analysis identified these proteins as highly interconnected hub nodes. Elevated expression was significantly associated with poor clinical outcomes. Conclusions: Phosphorylated TOP1, TOP2A, TOP2B, and C1orf35 are strongly associated with HCC progression and poor prognosis, highlighting their potential as prognostic biomarkers and therapeutic targets. These insights not only enhance our understanding of the complex molecular mechanisms underlying HCC but also offer promising avenues for the identification of novel therapeutic targets.

Indexed as

Carcinoma, HepatocellularDNA Topoisomerases, Type IILiver NeoplasmsPoly-ADP-Ribose Binding ProteinsBiomarkers, TumorComputational BiologyGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansMachine LearningPhosphoproteinsPhosphorylationPrognosisProtein Interaction MapsProteomicsBiomarkers, TumorDNA Topoisomerases, Type IIPhosphoproteinsPoly-ADP-Ribose Binding ProteinsTOP2A protein, humanbioinformaticsc-BioPortalHepatocellular carcinoma (HCC)nano–liquid chromatography coupled with electrospray ionization tandem mass spectrometry (nLC-ESI-MS/MS)phosphoproteomicsproteomicsthe Kaplan-Meier method

Identifiers

PMID42065042
PMCPMC13126569

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Registered trials

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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.