alexa Diagnostic and prognostic molecular markers in hepatocellular carcinoma.
Oncology

Oncology

Journal of Integrative Oncology

Author(s): Mnguez B, Lachenmayer A

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Abstract Hepatocellular carcinoma (HCC) is one of the most lethal cancers worldwide, representing also the main cause of death among cirrhotic patients. In contrast to most other solid tumors, the underlying cirrhotic liver disease in HCC patients greatly impairs tumor related prognosis, conferring this neoplasm a unique situation, in which accurate prognostic prediction is a relevant and unmet need. Although clinical staging systems have improved significantly and now comprise tumor characteristics, liver function and patient performance status, the integration of molecular data into these algorithms is still hypothetical. Molecular profiling of HCC has led to a better understanding of the physiopathology of this neoplasm and has allowed developing novel therapeutic approaches (e.g. molecular targeted therapies) for a tumor previously considered as therapy-refractory. Integrative analysis of different reported genomic datasets has revealed common subclasses between different studies, highlighting their biological relevance in HCC. Gene signatures derived from tumors and from the adjacent tissue have been able to differentiate subclasses with different outcomes and have been proposed as potential predictive markers in the clinical setting. Genomic characterization of surrounding non-tumor tissue might be of particular interest to identify patients at high risk of developing HCC and therefore to select those patients that would benefit of potential chemopreventive strategies. Epigenetic analyses (methylation and miRNA profiling) are adding up to the knowlegde derived from gene expression data and should not be forgotten in the molecular diagnosis of HCC. Integrative analyses of genetic and epigenetic information of the tumor and the surrounding tissue should be used to identify novel biomarkers and therapeutic targets in HCC, to improve existing treatment algorithms and to eventually design a more personalized medicine in this devastating disease.
This article was published in Dis Markers and referenced in Journal of Integrative Oncology

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