Biomarkers in Hepatocellular Carcinoma: Diagnosis, Prognosis and Treatment Response Assessment

Piñero, Federico; Dirchwolf, Melisa; Pessôa, Mário

doi:10.3390/cells9061370

Cited by 354 publications

(291 citation statements)

References 190 publications

(322 reference statements)

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“…139,140 The scarcity of predictive biomarkers limits personalized treatments and reduces their efficacy. 141 In addition, many of the current treatments result in not only high toxicity but also resistance and recurrence, thus ultimately leading to death. In the present section, the mechanisms and limitations of the different therapeutic strategies approved for HCC treatment will be discussed.…”

Section: Molecular Mechanisms and Limitations Of Currently Approved Systemic Therapiesmentioning

confidence: 99%

The therapeutic landscape of hepatocellular carcinoma

Gallage

García‐Beccaria

Szydlowska

et al. 2021

Med

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The liver is endowed with an amazing regenerative capacity that allows it to withstand an enormous amount of damage. Nevertheless, it is precisely this highly regenerative capacity that renders it susceptible to dysplasia and liver cancer. Liver cancer is not only one of the most common cancers but also one of the deadliest. Hepatocellular carcinoma (HCC) is the most common form of liver cancer, accounting for up to 70%-90% of all cases, but treatment options for advanced stages remain scarce. Therefore, a great deal of effort has gone into identifying early diagnostic markers as well as novel therapies, both local and systemic, for the treatment of this deadly disease. In this review, we aim to shed light into the current therapeutic landscape of HCC with an emphasis on the available treatments, ranging from surgical and local-ablative therapy for early and intermediate stages of the disease to systemic therapies for advanced cancer treatments. We will also address the molecular mechanisms and limitations of currently available systemic therapies and the causes of treatment resistance and finally summarize the emerging future avenues and novel concepts that are promising.

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Section: Molecular Mechanisms and Limitations Of Currently Approved Systemic Therapiesmentioning

confidence: 99%

The therapeutic landscape of hepatocellular carcinoma

Gallage

García‐Beccaria

Szydlowska

et al. 2021

Med

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“…Since the important role of the liver, HCC evaluation includes not only tumor features but also liver function parameters as key prognostic factors for survival and progression. Different staging algorithms are currently in use [ 46 ], they usually include prognostic clinical variables, tumor burden variables, liver function variables and biomarkers.…”

Section: Resultsmentioning

confidence: 99%

A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

Bedon

Mossenta

et al. 2021

IJMS

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Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

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“…Several different biomarkers have been studied in the last decades in order to improve and even personalize the surveillance of patients at high risk for HCC development [ 32 , 33 , 34 ]. Promising results have derived from comprehensive approaches that have allowed the detection of a wide spectrum of circulating molecules, including tumor proteins and nucleic acids (i.e., circulating tumor DNA/RNA) derived from the primary tumor [ 35 ], epigenetic biomarkers such as DNA methylation profiles and noncoding RNAs [ 36 , 37 ] and genetic variants recapitulated in polygenic risk scores [ 38 ].…”

Section: Discussionmentioning

confidence: 99%

Biomarkers of Oncogenesis, Adipose Tissue Dysfunction and Systemic Inflammation for the Detection of Hepatocellular Carcinoma in Patients with Nonalcoholic Fatty Liver Disease

Caviglia

Armandi

Rosso

et al. 2021

Cancers

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Current surveillance strategy for patients with nonalcoholic fatty liver disease (NAFLD) at risk of hepatocellular carcinoma (HCC) development is unsatisfactory. We aimed to investigate the diagnostic accuracy of alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist-II (PIVKA-II), glypican-3 (GPC-3), adiponectin, leptin and interleukin-6 (IL-6), alone or in combination, for the discrimination between NAFLD patients with or without HCC. The biomarkers were investigated in a cohort of 191 NAFLD patients (median age 62, 54–68 years; 121 males and 70 females) with advanced fibrosis/cirrhosis, 72 of whom had a diagnosis of HCC. PIVKA-II showed the best performance for the detection of HCC with an area under the curve (AUC) of 0.853, followed by adiponectin (AUC = 0.770), AFP (AUC = 0.763), GPC-3 (AUC = 0.759) and by IL-6 (AUC = 0.731), while the leptin values were not different between patients with and without HCC. The accuracy of the biomarkers’ combination was assessed by a stratified cross-validation approach. The combination of age, gender, PIVKA-II, GPC-3 and adiponectin further improved the diagnostic accuracy (AUC = 0.948); the model correctly identified the 87% of the patients. In conclusion, we developed a model with excellent accuracy for the detection of HCC that may be useful to improve the surveillance of NAFLD patients at risk of tumor development.

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Biomarkers in Hepatocellular Carcinoma: Diagnosis, Prognosis and Treatment Response Assessment

Cited by 354 publications

References 190 publications

The therapeutic landscape of hepatocellular carcinoma

The therapeutic landscape of hepatocellular carcinoma

A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

Biomarkers of Oncogenesis, Adipose Tissue Dysfunction and Systemic Inflammation for the Detection of Hepatocellular Carcinoma in Patients with Nonalcoholic Fatty Liver Disease

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