Cargando…

MAFLD: an optimal framework for understanding liver cancer phenotypes

Hepatocellular carcinoma has a substantial global mortality burden which is rising despite advancements in tackling the traditional viral risk factors. Metabolic (dysfunction) associated fatty liver disease (MAFLD) is the most prevalent liver disease, increasing in parallel with the epidemics of obe...

Descripción completa

Detalles Bibliográficos
Autores principales: Crane, Harry, Gofton, Cameron, Sharma, Ankur, George, Jacob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522746/
https://www.ncbi.nlm.nih.gov/pubmed/37470858
http://dx.doi.org/10.1007/s00535-023-02021-7
_version_ 1785110419789053952
author Crane, Harry
Gofton, Cameron
Sharma, Ankur
George, Jacob
author_facet Crane, Harry
Gofton, Cameron
Sharma, Ankur
George, Jacob
author_sort Crane, Harry
collection PubMed
description Hepatocellular carcinoma has a substantial global mortality burden which is rising despite advancements in tackling the traditional viral risk factors. Metabolic (dysfunction) associated fatty liver disease (MAFLD) is the most prevalent liver disease, increasing in parallel with the epidemics of obesity, diabetes and systemic metabolic dysregulation. MAFLD is a major factor behind this sustained rise in HCC incidence, both as a single disease entity and often via synergistic interactions with other liver diseases. Mechanisms behind MAFLD-related HCC are complex but is crucially underpinned by systemic metabolic dysregulation with variable contributions from interacting disease modifiers related to environment, genetics, dysbiosis and immune dysregulation. MAFLD-related HCC has a distinct clinical presentation, most notably its common occurrence in non-cirrhotic liver disease. This is just one of several major challenges to effective surveillance programmes. The response of MAFLD-related HCC to immune-checkpoint therapy is currently controversial, and is further complicated by the high prevalence of MAFLD in individuals with HCC from viral aetiologies. In this review, we highlight the current data on epidemiology, clinical characteristics, outcomes and screening controversies. In addition, concepts that have arisen because of the MAFLD paradigm such as HCC in MAFLD/NAFLD non-overlapping groups, dual aetiology tumours and MAFLD sub-phenotypes is reviewed.
format Online
Article
Text
id pubmed-10522746
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer Nature Singapore
record_format MEDLINE/PubMed
spelling pubmed-105227462023-09-28 MAFLD: an optimal framework for understanding liver cancer phenotypes Crane, Harry Gofton, Cameron Sharma, Ankur George, Jacob J Gastroenterol Review Hepatocellular carcinoma has a substantial global mortality burden which is rising despite advancements in tackling the traditional viral risk factors. Metabolic (dysfunction) associated fatty liver disease (MAFLD) is the most prevalent liver disease, increasing in parallel with the epidemics of obesity, diabetes and systemic metabolic dysregulation. MAFLD is a major factor behind this sustained rise in HCC incidence, both as a single disease entity and often via synergistic interactions with other liver diseases. Mechanisms behind MAFLD-related HCC are complex but is crucially underpinned by systemic metabolic dysregulation with variable contributions from interacting disease modifiers related to environment, genetics, dysbiosis and immune dysregulation. MAFLD-related HCC has a distinct clinical presentation, most notably its common occurrence in non-cirrhotic liver disease. This is just one of several major challenges to effective surveillance programmes. The response of MAFLD-related HCC to immune-checkpoint therapy is currently controversial, and is further complicated by the high prevalence of MAFLD in individuals with HCC from viral aetiologies. In this review, we highlight the current data on epidemiology, clinical characteristics, outcomes and screening controversies. In addition, concepts that have arisen because of the MAFLD paradigm such as HCC in MAFLD/NAFLD non-overlapping groups, dual aetiology tumours and MAFLD sub-phenotypes is reviewed. Springer Nature Singapore 2023-07-20 2023 /pmc/articles/PMC10522746/ /pubmed/37470858 http://dx.doi.org/10.1007/s00535-023-02021-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Review
Crane, Harry
Gofton, Cameron
Sharma, Ankur
George, Jacob
MAFLD: an optimal framework for understanding liver cancer phenotypes
title MAFLD: an optimal framework for understanding liver cancer phenotypes
title_full MAFLD: an optimal framework for understanding liver cancer phenotypes
title_fullStr MAFLD: an optimal framework for understanding liver cancer phenotypes
title_full_unstemmed MAFLD: an optimal framework for understanding liver cancer phenotypes
title_short MAFLD: an optimal framework for understanding liver cancer phenotypes
title_sort mafld: an optimal framework for understanding liver cancer phenotypes
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522746/
https://www.ncbi.nlm.nih.gov/pubmed/37470858
http://dx.doi.org/10.1007/s00535-023-02021-7
work_keys_str_mv AT craneharry mafldanoptimalframeworkforunderstandinglivercancerphenotypes
AT goftoncameron mafldanoptimalframeworkforunderstandinglivercancerphenotypes
AT sharmaankur mafldanoptimalframeworkforunderstandinglivercancerphenotypes
AT georgejacob mafldanoptimalframeworkforunderstandinglivercancerphenotypes