Evidence map›Paper›PMID 41385729›Full record

ArticleHepatology communications2026

The alternative splicing landscape of hepatocellular carcinoma and its potential for HCC detection.

Courtney E Hershberger, Noah J Daniels, William M Patterson, Daniel M Rotroff

Abstract read
In one paragraph

Article in Hepatology communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Courtney E HershbergerDepartment of Quantitative Health Sciences, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio, USA.ORCID 0000-0002-2195-989
Noah J DanielsDepartment of Quantitative Health Sciences, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio, USA.
William M PattersonCleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.
Daniel M RotroffDepartment of Quantitative Health Sciences, Cleveland Clinic Research, Cleveland Clinic, Cleveland, Ohio, USA.ORCID 0000-0003-0553-3220

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPre-mRNA alternative splicing contributes to oncogenic gene expression in hepatocellular carcinoma (HCC), and some oncogenic isoforms escape the tumor into circulation. This study aimed to characterize the alternative splicing landscape of HCC and investigate its potential for early detection by identifying HCC-specific alternative splicing events (ASEs) in peripheral fluids.

methodsWe analyzed RNA-Seq data from 787 HCC tumor samples across 16 studies to identify commonly mis-spliced ASEs. ASEs were examined in relation to HCC etiology, progression, and functional domain changes. We then identified HCC-tumor-associated ASEs in samples isolated from various peripheral fluids, including circulating epithelial cells (CECs), tumor-educated platelets, serum, and plasma. Machine-learning models were trained to classify HCC versus non-HCC based on ASEs in CECs and plasma.

resultsWe identified 3416 ASEs commonly mis-spliced in HCC across studies (FDR p<0.05, |ΔPSI|>0.1). We made these ASEs widely available through a new web application, HCC-INSIGHT (INvestigation of Spliced Isoforms and Genes in Human Tissues). Ten ASEs in CECs distinguished HCC from non-HCC in an independent test cohort with a balanced accuracy of 74%, 66% sensitivity, and 81% specificity. When combined with alpha-fetoprotein (AFP), classification performance improved to a balanced accuracy of 78%, 66% sensitivity, and 90% specificity.

conclusionsThis study provides a comprehensive characterization of ASEs in HCC and highlights their potential as circulating biomarkers. Our findings support the utility of ASEs in CECs for HCC detection, particularly in combination with AFP, although additional studies are needed to validate these ASEs in an independent prospective cohort. Finally, we introduce HCC-INSIGHT as a resource for further research into HCC splicing alterations.

Indexed as

Alternative SplicingCarcinoma, HepatocellularLiver NeoplasmsBiomarkers, TumorEarly Detection of CancerHumansMachine LearningBiomarkers, Tumoralternative splicingbiomarkerexonshumanintronsisoformliquid biopsymachine learning

Identifiers

PMID41385729
PMCPMC12705057

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LicenceCC BY-NC-ND
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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.