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Sanchez JI, Fontillas AC, Kwan SY, Sanchez CI, Calderone TL, Lee JL, Elsaiey A, Cleere DW, Wei P, Vierling JM, Victor DW, Beretta L. Metabolomics biomarkers of hepatocellular carcinoma in a prospective cohort of patients with cirrhosis. JHEP Rep 2024; 6:101119. [PMID: 39139459 PMCID: PMC11321296 DOI: 10.1016/j.jhepr.2024.101119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 05/01/2024] [Accepted: 05/08/2024] [Indexed: 08/15/2024] Open
Abstract
Background & Aims The effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited, due to inadequate risk stratification and suboptimal performance of current screening modalities. Methods We developed a multicenter prospective cohort of patients with cirrhosis undergoing surveillance with MRI and applied global untargeted metabolomics to 612 longitudinal serum samples from 203 patients. Among them, 37 developed HCC during follow-up. Results We identified 150 metabolites with significant abundance changes in samples collected prior to HCC (Cases) compared to samples from patients who did not develop HCC (Controls). Tauro-conjugated bile acids and gamma-glutamyl amino acids were increased, while acyl-cholines and deoxycholate derivatives were decreased. Seven amino acids including serine and alanine had strong associations with HCC risk, while strong protective effects were observed for N-acetylglycine and glycerophosphorylcholine. Machine learning using the 150 metabolites, age, gender, and PNPLA3 and TMS6SF2 single nucleotide polymorphisms, identified 15 variables giving optimal performance. Among them, N-acetylglycine had the highest AUC in discriminating Cases and Controls. When restricting Cases to samples collected within 1 year prior to HCC (Cases-12M), additional metabolites including microbiota-derived metabolites were identified. The combination of the top six variables identified by machine learning (alpha-fetoprotein, 6-bromotryptophan, N-acetylglycine, salicyluric glucuronide, testosterone sulfate and age) had good performance in discriminating Cases-12M from Controls (AUC 0.88, 95% CI 0.83-0.93). Finally, 23 metabolites distinguished Cases with LI-RADS-3 lesions from Controls with LI-RADS-3 lesions, with reduced abundance of acyl-cholines and glycerophosphorylcholine-related lysophospholipids in Cases. Conclusions This study identified N-acetylglycine, amino acids, bile acids and choline-derived metabolites as biomarkers of HCC risk, and microbiota-derived metabolites as contributors to HCC development. Impact and implications The effectiveness of surveillance for hepatocellular carcinoma (HCC) in patients with cirrhosis is limited. There is an urgent need for improvement in risk stratification and new screening modalities, particularly blood biomarkers. Longitudinal collection of paired blood samples and MRI images from patients with cirrhosis is particularly valuable in assessing how early blood and imaging markers become positive during the period when lesions are observed to obtain a diagnosis of HCC. We generated a multicenter prospective cohort of patients with cirrhosis under surveillance with contrast MRI, applied untargeted metabolomics on 612 serum samples from 203 patients and identified metabolites associated with risk of HCC development. Such biomarkers may significantly improve early-stage HCC detection for patients with cirrhosis undergoing HCC surveillance, a critical step to increasing curative treatment opportunities and reducing mortality.
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Affiliation(s)
- Jessica I. Sanchez
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Antoine C. Fontillas
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Suet-Ying Kwan
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Caren I. Sanchez
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tiffany L. Calderone
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jana L. Lee
- Margaret M. and Albert B. Alkek Department of Medicine, Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX, USA
| | - Ahmed Elsaiey
- Department of Gastroenterology, Houston Methodist Hospital, Houston, TX, USA
| | - Darrel W. Cleere
- Department of Gastroenterology, Houston Methodist Hospital, Houston, TX, USA
| | - Peng Wei
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John M. Vierling
- Margaret M. and Albert B. Alkek Department of Medicine, Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, TX, USA
| | - David W. Victor
- Department of Gastroenterology, Houston Methodist Hospital, Houston, TX, USA
| | - Laura Beretta
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Shin D, Kim Y, Park J, Kim Y. High-throughput Proteomics-Guided Biomarker Discovery of Hepatocellular Carcinoma. Biomed J 2024:100752. [PMID: 38901798 DOI: 10.1016/j.bj.2024.100752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 06/07/2024] [Accepted: 06/12/2024] [Indexed: 06/22/2024] Open
Abstract
Liver cancer stands as the fifth leading cause of cancer-related deaths globally. Hepatocellular carcinoma (HCC) comprises approximately 85%-90% of all primary liver malignancies. However, only 20-30% of HCC patients qualify for curative therapy, primarily due to the absence of reliable tools for early detection and prognosis of HCC. This underscores the critical need for molecular biomarkers for HCC management. Since proteins reflect disease status directly, proteomics has been utilized in biomarker developments for HCC. In particular, proteomics coupled with liquid chromatography-mass spectrometer (LC-MS) methods facilitate the process of discovering biomarker candidates for diagnosis, prognosis, and therapeutic strategies. In this work, we investigated LC-MS-based proteomics methods through recent reference reviews, with a particular focus on sample preparation and LC-MS methods appropriate for the discovery of HCC biomarkers and their clinical applications. We classified proteomics studies of HCC according to sample types, and we examined the coverage of protein biomarker candidates based on LC-MS methods in relation to study scales and goals. Comprehensively, we proposed protein biomarker candidates categorized by sample types and biomarker types for appropriate clinical use. In this review, we summarized recent LC-MS-based proteomics studies on HCC and proposed potential protein biomarkers. Our findings are expected to expand the understanding of HCC pathogenesis and enhance the efficiency of HCC diagnosis and prognosis, thereby contributing to improved patient outcomes.
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Affiliation(s)
- Dongyoon Shin
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea
| | - Yeongshin Kim
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Medical Science, School of Medicine, CHA University, Seongnam, Republic of Korea
| | - Junho Park
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Pharmacology, School of Medicine, CHA University, Seongnam, Republic of Korea.
| | - Youngsoo Kim
- Proteomics Research Team, CHA Institute of Future Medicine, Seongnam, Republic of Korea; Department of Medical Science, School of Medicine, CHA University, Seongnam, Republic of Korea.
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Xu M, Xu K, Yin S, Sun W, Wang G, Zhang K, Mu J, Wu M, Xing B, Zhang X, Han J, Zhao X, Chang C, Wang Y, Xu D, Yu X. In-depth serum proteomics reveals the trajectory of hallmarks of cancer in hepatitis B virus-related liver diseases. Mol Cell Proteomics 2023:100574. [PMID: 37209815 PMCID: PMC10316086 DOI: 10.1016/j.mcpro.2023.100574] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 04/25/2023] [Accepted: 05/16/2023] [Indexed: 05/22/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is a prevalent cancer in China, with chronic hepatitis B (CHB) and liver cirrhosis (LC) being high-risk factors for developing HCC. Here, we determined the serum proteomes (762 proteins) of 125 healthy controls and Hepatitis B virus-infected CHB, LC, and HCC patients and constructed the first cancerous trajectory of liver diseases. The results not only reveal that the majority of altered biological processes were involved in the hallmarks of cancer (inflammation, metastasis, metabolism, vasculature, coagulation), but also identify potential therapeutic targets in cancerous pathways (i.e., IL17 signaling pathway). Notably, the biomarker panels for detecting HCC in CHB and LC high-risk populations were further developed using machine learning in two cohorts comprised of 200 samples (discovery cohort=125, validation cohort=75). The protein signatures significantly improved the area under the receiver operating characteristic curve (AUC) of HCC (CHB discovery and validation cohort = 0.953 and 0.891, respectively; LC discovery and validation cohort = 0.966 and 0.818, respectively) compared to using the traditional biomarker, alpha-fetoprotein (AFP), alone. Finally, selected biomarkers were validated with parallel reaction monitoring (PRM) mass spectrometry in an additional cohort (n=120). Altogether, our results provide fundamental insights into the continuous changes of cancer biology processes in liver diseases and identify candidate protein targets for early detection and intervention.
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Affiliation(s)
- Meng Xu
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Kaikun Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing, 102206, China; Research Unit of Proteomics Driven Cancer Precision Medicine, Chinese Academy of Medical Sciences, Beijing 102206, China
| | - Shangqi Yin
- Department of Clinical Laboratory, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Wei Sun
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Guibin Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Kai Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Jinsong Mu
- Department of Critical Care Medicine, The Fifth Medical Center, Chinese PLA General Hospital, Beijing, 100039, China
| | - Miantao Wu
- Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Baocai Xing
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery I, Peking University Cancer Hospital and Institute, Beijing, 100036, China
| | - Xiaomei Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Jinyu Han
- Department of Clinical Laboratory, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Xiaohang Zhao
- State Key Laboratory of Molecular Oncology, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
| | - Cheng Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing, 102206, China; Research Unit of Proteomics Driven Cancer Precision Medicine, Chinese Academy of Medical Sciences, Beijing 102206, China.
| | - Yajie Wang
- Department of Clinical Laboratory, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China.
| | - Danke Xu
- State Key Laboratory of Analytical Chemistry for Life Science, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing, China.
| | - Xiaobo Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing, 102206, China.
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Kim JY, Kim J, Lim YS, Gwak GY, Yeo I, Kim Y, Lee J, Shin D, Lee JH, Kim Y. Proteome Multimarker Panel for the Early Detection of Hepatocellular Carcinoma: Multicenter Derivation, Validation, and Comparison. ACS OMEGA 2022; 7:29934-29943. [PMID: 36061641 PMCID: PMC9434733 DOI: 10.1021/acsomega.2c02926] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
Conventional methods for the surveillance of hepatocellular carcinoma (HCC) by imaging, with and without serum tumor markers, are suboptimal with regard to accuracy. We aimed to develop and validate a reliable serum biomarker panel for the early detection of HCC using a proteomic technique. This multicenter case-control study comprised 727 patients with HCC and patients with risk factors but no HCC. We developed a multiple reaction monitoring-mass spectrometry (MRM-MS) multimarker panel using 17 proteins from the sera of 398 patients. Area under the receiver operating characteristics curve (AUROC) values of this MRM-MS panel with and without α-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II) were compared. The combination and standalone MRM-MS panels had higher AUROC values than AFP in the training (0.940 and 0.929 vs 0.775, both P < 0.05), test (0.894 and 0.893 vs 0.593, both P < 0.05), and confirmation sets (0.961 and 0.937 vs 0.806, both P < 0.05) in detecting small single HCC. The combination and standalone MRM-MS panels had significantly higher AUROC values than the GALAD score (0.945 and 0.931 vs 0.829, both P < 0.05). Our proteome 17-protein multimarker panel distinguished HCC patients from high-risk controls and had high accuracy in the early detection of HCC.
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Affiliation(s)
- Ju Yeon Kim
- Department
of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Jaenyeon Kim
- Interdisciplinary
Program of Bioengineering, Graduate School,
Seoul National University, Seoul 08826, Republic of Korea
| | - Young-Suk Lim
- Department
of Gastroenterology, Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul 44610, Republic of Korea
| | - Geum-Youn Gwak
- Department
of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Republic
of Korea
| | - Injoon Yeo
- Interdisciplinary
Program of Bioengineering, Graduate School,
Seoul National University, Seoul 08826, Republic of Korea
| | - Yoseop Kim
- Interdisciplinary
Program of Bioengineering, Graduate School,
Seoul National University, Seoul 08826, Republic of Korea
| | - Jihyeon Lee
- Department
of Biomedical Sciences, Seoul National University
College of Medicine, Seoul 03080, Republic of Korea
| | - Dongyoon Shin
- Department
of Biomedical Sciences, Seoul National University
College of Medicine, Seoul 03080, Republic of Korea
| | - Jeong-Hoon Lee
- Department
of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Youngsoo Kim
- Interdisciplinary
Program of Bioengineering, Graduate School,
Seoul National University, Seoul 08826, Republic of Korea
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Moldogazieva NT, Mokhosoev IM, Zavadskiy SP, Terentiev AA. Proteomic Profiling and Artificial Intelligence for Hepatocellular Carcinoma Translational Medicine. Biomedicines 2021; 9:biomedicines9020159. [PMID: 33562077 PMCID: PMC7914649 DOI: 10.3390/biomedicines9020159] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Revised: 01/27/2021] [Accepted: 02/02/2021] [Indexed: 02/06/2023] Open
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.
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Affiliation(s)
- Nurbubu T. Moldogazieva
- Laboratory of Bioinformatics, Institute of Translational Medicine and Biotechnology, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
- Correspondence: or
| | - Innokenty M. Mokhosoev
- Department of Biochemistry and Molecular Biology, N.I. Pirogov Russian National Research Medical University, 117997 Moscow, Russia; (I.M.M.); (A.A.T.)
| | - Sergey P. Zavadskiy
- Department of Pharmacology, A.P. Nelyubin Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia;
| | - Alexander A. Terentiev
- Department of Biochemistry and Molecular Biology, N.I. Pirogov Russian National Research Medical University, 117997 Moscow, Russia; (I.M.M.); (A.A.T.)
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