Current biomarkers for hepatocellular carcinoma: Surveillance, diagnosis and prediction of prognosis

Schütte, Kerstin; Schulz, Christian; Link, Alexander; Malfertheiner, Peter

doi:10.4254/wjh.v7.i2.139

Cited by 78 publications

(65 citation statements)

References 101 publications

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“…Besides the simple patient related demographic data such as age and gender, many other specific biochemical and clinical variables of liver function, tumor burden and biology as well as agerelated clinical consequences and comorbidities have been included in regression analysis of the different studied populations worldwide [1] [ Table 1]. Several biomarkers have also been studied for their prognostic significance in patients with HCC [13] .…”

Section: Introductionmentioning

confidence: 99%

Staging of hepatocellular carcinoma

Karademir¹

2018

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Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths in the world. In contrast to other cancers, survival of patients with HCC is determined by the extent of the tumor in addition to underlying liver disease and its functional reserve. From risk factors to management, HCC reveals a considerable geographic and institutional variation throughout the world. Although many staging and/or scoring systems have been proposed, each prognostic system has several benefits and limitations on its own. Therefore, there is currently no globally accepted system for HCC due to the extreme heterogeneity of the disease. In this review, currently available staging systems for assessing the prognosis of HCC, their uses, limitations, and future prospects are revisited.

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Section: Introductionmentioning

confidence: 99%

Staging of hepatocellular carcinoma

Karademir¹

2018

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“…However, these markers have not been applied in clinic due to poor sensitivity and specificity (26). With the advance of precision medicine, it is urgent to develop an alternative strategy for diagnosis and provide prognostic information of HCC (27).…”

Section: Introductionmentioning

confidence: 99%

Development of a prediction classifier for the early diagnosis of liver cancer

Wu¹,

Cao²,

Li³

et al. 2018

EasyChair Preprints

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Hepatocellular carcinoma (HCC) is the second cause of cancer-related death worldwide, and the incidence rate of liver cancer has continuously increased, with approximately 750,000 new diagnosed cases each year. Especially in China, both the incidence and mortality rate of HCC have been ranked second among all cancers. Importantly, HCC mortality rate is similar to its incidence rate, indicating that most patients with liver cancer die from HCC. In clinical practice, liver cancers are usually diagnosed by detecting alpha-fetoprotein (AFP) and abdominal ultrasound. However, abnormal AFP is usually detected at late stages of liver cancer, in which most patients are refractory to surgery, radiotherapy and chemotherapy. Moreover, AFP is not detectable in some liver cancer patients. In this study, we aimed to establish an alternative diagnostic method for liver cancer patients by analyzing hidden patterns and relationships among multiple specific markers of liver cancers. By building a predictive classification of liver cancer and the relationship between different markers, a support vector machine (SVM) classifier was developed. Our SVM classifier integrated 22 specific markers. Our results revealed that the input of these 22 markers into the classifier could accurately determine the exitence of HCC in a patient. Our established SVM classifier may achieve the early prediction of liver cancer, thereby improving the accuracy of diagnosis and treatment of live cancer patients.

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“…Currently three FDA approved serum biomarkers are recommended to indicate the risk of liver cancer in high risk populations: α-fetoprotein (AFP), AFP-L3, which is a fucosylated isoform of AFP, and des-gamma-carboxy-prothrombin (DCP) 4 . However, these markers are not included in the surveillance guidelines published by the American Association for the Study of Liver Diseases 5 and the European Association for the Study of the Liver 6 because of low sensitivity and specificity.…”

Section: Introductionmentioning

confidence: 99%

Identification of protein abundance changes in biopsy-level hepatocellular carcinoma tissues using PCT-SWATH

Zhu

et al. 2018

Preprint

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and Tiannan Guo (guotiannan@wias.org.cn) Zhu, et al. Proteomics in Pathology: PCT-SWATH analysis of Hepatocellular carcinoma 2 / 26 2 AbstractIn this study, we optimized the pressure-cycling technology (PCT) and SWATH mass spectrometry workflow to analyze biopsy-level tissue samples (2 mg wet weight) from 19 hepatocellular carcinoma (HCC) patients. Using OpenSWATH and pan-human spectral library, we quantified 11,787 proteotypic peptides from 2,579 SwissProt proteins in 76 HCC tissue samples within about 9 working days (from receiving tissue to SWATH data). The coefficient of variation (CV) of peptide yield using PCT was 32.9%, and the R 2 of peptide quantification was 0.9729. We identified protein changes in malignant tissues compared to matched control samples in HCC patients, and further stratified patient samples into groups with high α-fetoprotein (AFP) expression or HBV infection. In aggregate, the data identified 23 upregulated pathways and 13 ones. We observed enhanced biomolecule synthesis and suppressed small molecular metabolism in liver tumor tissues. 16 proteins of high documented relevance to HCC are highlighted in our data. We also identified changes of virus-infection-related proteins including PKM, CTPS1 and ALDOB in the HBV + HCC subcohort. In conclusion, we demonstrate the practicality of performing proteomic analysis of biopsy-level tissue samples with PCT-SWATH methodology with moderate effort and within a relatively short timeframe. Zhu, et al. Proteomics in Pathology: PCT-SWATH analysis of Hepatocellular carcinoma 3 / 26

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Current biomarkers for hepatocellular carcinoma: Surveillance, diagnosis and prediction of prognosis

Cited by 78 publications

References 101 publications

Staging of hepatocellular carcinoma

Staging of hepatocellular carcinoma

Development of a prediction classifier for the early diagnosis of liver cancer

Identification of protein abundance changes in biopsy-level hepatocellular carcinoma tissues using PCT-SWATH

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