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Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine

1
Laboratory of Bioinformatics, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
2
Department of Biochemistry and Molecular Biology, N.I. Pirogov Russian National Research Medical University, 117997 Moscow, Russia
3
Department of Pharmacology, A.P. Nelyubin Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
*
Author to whom correspondence should be addressed.
Academic Editor: Giovanni Squadrito
Biomedicines 2021, 9(2), 159; https://doi.org/10.3390/biomedicines9020159
Received: 29 December 2020 / Revised: 27 January 2021 / Accepted: 2 February 2021 / Published: 6 February 2021
(This article belongs to the Special Issue Liver Cancers: From Bench to Bedside)
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. View Full-Text
Keywords: proteomics; artificial intelligence; biomarkers; hepatocellular carcinoma; translational medicine proteomics; artificial intelligence; biomarkers; hepatocellular carcinoma; translational medicine
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MDPI and ACS Style

Moldogazieva, N.T.; Mokhosoev, I.M.; Zavadskiy, S.P.; Terentiev, A.A. Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine. Biomedicines 2021, 9, 159. https://doi.org/10.3390/biomedicines9020159

AMA Style

Moldogazieva NT, Mokhosoev IM, Zavadskiy SP, Terentiev AA. Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine. Biomedicines. 2021; 9(2):159. https://doi.org/10.3390/biomedicines9020159

Chicago/Turabian Style

Moldogazieva, Nurbubu T.; Mokhosoev, Innokenty M.; Zavadskiy, Sergey P.; Terentiev, Alexander A. 2021. "Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine" Biomedicines 9, no. 2: 159. https://doi.org/10.3390/biomedicines9020159

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