Abstract
Hepatocellular carcinoma (HCC) is the most common primary cancer of the liver with high morbidity and mortality rates worldwide. Since 1963, when alpha-fetoprotein (AFP) was discovered as a first HCC serum biomarker, several other protein biomarkers have been identified and introduced into clinical practice. However, insufficient specificity and sensitivity of these biomarkers dictate the necessity of novel biomarker discovery. Remarkable advancements in integrated multiomics technologies for the identification of gene expression and protein or metabolite distribution patterns can facilitate rising to this challenge. Current multiomics technologies lead to the accumulation of a huge amount of data, which requires clustering and finding correlations between various datasets and developing predictive models for data filtering, pre-processing, and reducing dimensionality. Artificial intelligence (AI) technologies have an enormous potential to overcome accelerated data growth, complexity, and heterogeneity within and across data sources. Our review focuses on the recent progress in integrative proteomic profiling strategies and their usage in combination with machine learning and deep learning technologies for the discovery of novel biomarker candidates for HCC early diagnosis and prognosis. We discuss conventional and promising proteomic biomarkers of HCC such as AFP, lens culinaris agglutinin (LCA)-reactive L3 glycoform of AFP (AFP-L3), des-gamma-carboxyprothrombin (DCP), osteopontin (OPN), glypican-3 (GPC3), dickkopf-1 (DKK1), midkine (MDK), and squamous cell carcinoma antigen (SCCA) and highlight their functional significance including the involvement in cell signaling such as Wnt/β-catenin, PI3K/Akt, integrin αvβ3/NF-κB/HIF-1α, JAK/STAT3 and MAPK/ERK-mediated pathways dysregulated in HCC. We show that currently available computational platforms for big data analysis and AI technologies can both enhance proteomic profiling and improve imaging techniques to enhance the translational application of proteomics data into precision medicine.
Highlights
Liver cirrhosis is a main cause of Hepatocellular carcinoma (HCC) and together with inflammation associated with hepatitis B virus (HBV) or hepatitis C virus (HCV) accompanies early stages of HCC [3,4]
A proteomics approach exploited to analyze the secretory releasing proteome of HBVassociated HCC has showed that among 1365 proteins identified in serum-free conditioned media, levels of AFP, OPN, pregnancy-specific beta-1-glycoprotein-9 (PSG-9) and matrix metalloproteinase-1 (MMP-1), members of transforming growth factor-β (TGF-β)-signaling pathway were the most significantly increased in HCC patients [128]
The revolution in Artificial intelligence (AI) technologies has enormously enhanced HCC diagnosis and prognosis based on the usage of various imaging techniques, especially in combination with HCC molecular biomarkers
Summary
Comprehensive multiomics profiling enables differentiating early and advanced HCCs as well as HCC from chronic liver diseases, even without knowledge of the clinical symptoms [10] This allows assessing intra-tumoral phenotypic heterogeneity and uncovering individual variability and alterations in unique gene expression patterns, which underlie tumor initiation and progression [11]. Significant progress has been achieved due to the application of artificial intelligence (AI) technologies, which enhance healthcare data collection and interpretation This is provided by computer-based algorithms for data analysis and by the construction of predictive models for improving image recognition and representation in HCC diagnosis and prognosis [14]. Translating knowledge on a biomarker structure, functions, and expression into clinical practice should enable HCC early diagnosis, prognosis, and assessment of treatment efficacy.
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