Cell cycle pathway alterations predict outcomes post-liver transplantation for hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) incidence and mortality are rising. Liver transplantation (LT) offers the best outcomes, but current tumor size- and number-based selection criteria restrict access. Molecular profiling may better reflect tumor biology and guide precision-based selection strategies. Methods: This prospective single-center study included patients with HCC who had undergone LT between 11 November 2016 and 4 April 2023 with sufficient tumor cellularity in explanted livers. Tumor DNA was subjected to targeted sequencing for 38 genes, with the results returned to clinicians. Altered genes were grouped into HCC-relevant signaling pathways. Outcomes included post-LT overall survival (OS), recurrence-free survival (RFS), and recurrence sites. The Cancer Genome Atlas (TCGA) HCC cohort was used for validation. Results: < 0.05). Conclusion: Post-LT molecular profiling of HCC reveals tumor-specific alterations associated with outcomes, supporting the incorporation of tumor biology into future LT selection criteria.

Authors

Institutions

Publication Details

Journal
Frontiers in Transplantation
Published
2026-04-10
DOI
https://doi.org/10.3389/frtra.2026.1758576
Primary Topic
Hepatocellular Carcinoma Treatment and Prognosis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cell cycle pathway alterations predict outcomes post-liver transplantation for hepatocellular carcinoma

Ahmed Elaileh, Linda W. Moore, R. K. Olsen, Sudha Kodali et al.
Frontiers in Transplantation
Hepatocellular Carcinoma Treatment and Prognosis
article

Cell cycle pathway alterations predict outcomes post-liver transplantation for hepatocellular carcinoma

Ahmed Elaileh, Linda W. Moore, R. K. Olsen, Sudha Kodali, Jessica S. Thomas, M. Schwartz, Constance M. Mobley, Timothy Eric Newhook, Ashish Saharia, Yee Lee Cheah, Kirk Heyne, Mahinur Mattohti, S. Semaan, Y. Dib, Khush Patel, Jason Todd, Sadhna Dhingra, Jean-Nicolas Vauthey, Ashton A. Connor, A. Osama Gaber, Caroline J. Simon, R. Mark Ghobrial, Urmi Sengupta, David W. Victor, Xian C. Li, Ahmed O. Kaseb, Maen Abdelrahim
article en

Abstract

Background: Hepatocellular carcinoma (HCC) incidence and mortality are rising. Liver transplantation (LT) offers the best outcomes, but current tumor size- and number-based selection criteria restrict access. Molecular profiling may better reflect tumor biology and guide precision-based selection strategies. Methods: This prospective single-center study included patients with HCC who had undergone LT between 11 November 2016 and 4 April 2023 with sufficient tumor cellularity in explanted livers. Tumor DNA was subjected to targeted sequencing for 38 genes, with the results returned to clinicians. Altered genes were grouped into HCC-relevant signaling pathways. Outcomes included post-LT overall survival (OS), recurrence-free survival (RFS), and recurrence sites. The Cancer Genome Atlas (TCGA) HCC cohort was used for validation. Results: < 0.05). Conclusion: Post-LT molecular profiling of HCC reveals tumor-specific alterations associated with outcomes, supporting the incorporation of tumor biology into future LT selection criteria.

Frontiers in TransplantationVol. 5
Houston Methodist (US), The University of Texas MD Anderson Cancer Center (US), Cornell University (US), Methodist Hospital (US), Methodist Hospital (US), Iowa Methodist Medical Center (US)
Good health and well-being
Openalex Percentile: Top 34%
Hepatocellular Carcinoma Treatment and Prognosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.