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Wu J, Bai X, Yu G, Zhang Q, Tian X, Wang Y. Efficacy and safety of apatinib plus immune checkpoint inhibitors and transarterial chemoembolization for the treatment of advanced hepatocellular carcinoma. J Cancer Res Clin Oncol 2024; 150:340. [PMID: 38976071 PMCID: PMC11230948 DOI: 10.1007/s00432-024-05854-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 06/17/2024] [Indexed: 07/09/2024]
Abstract
PURPOSE The evidence of apatinib plus immune checkpoint inhibitors (ICIs) and transarterial chemoembolization (TACE) for treating advanced hepatocellular carcinoma (HCC) is limited. This study aimed to compare the treatment efficacy and safety of apatinib plus ICIs and TACE with apatinib plus TACE in these patients. METHODS This study retrospectively enrolled 90 patients with advanced HCC treated with apatinib plus TACE (A-TACE group, n = 52) or apatinib plus ICIs and TACE (IA-TACE group, n = 38). RESULTS The objective response rate was numerically higher in IA-TACE group compared with A-TACE group without statistical significance (57.9% vs. 36.5%, P = 0.055). Disease control rate was not different between groups (86.8% vs. 76.9%, P = 0.248). Progression-free survival (PFS) was improved in IA-TACE group compared with A-TACE group (P = 0.018). The median PFS (95% confidence interval) was 12.5 (8.7-16.3) months in IA-TACE group and 8.5 (5.6-11.4) months in A-TACE group. Overall survival (OS) was also prolonged in IA-TACE group compared with A-TACE group (P = 0.007). The median OS (95% confidence interval) was 21.1 (15.8-26.4) months in IA-TACE group and 14.3 (11.5-17.1) months in A-TACE group. By multivariate Cox regression model, IA-TACE was independently associated with prolonged PFS (hazard ratio = 0.539, P = 0.038) and OS (hazard ratio = 0.447, P = 0.025). Most adverse events were not different between groups. Only the incidence of reactive cutaneous capillary endothelial proliferation was higher in IA-TACE group compared with A-TACE group (10.5% vs. 0.0%, P = 0.029). CONCLUSION Apatinib plus ICIs and TACE may be an effective and safe treatment for patients with advanced HCC, but further large-scale studies are needed for verification.
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Affiliation(s)
- Jianfei Wu
- Department of Hepatobiliary Surgery, Affiliated Hospital of Hebei University, Baoding, 07100, China
| | - Xuefeng Bai
- Department of Hepatobiliary Surgery, Affiliated Hospital of Hebei University, Baoding, 07100, China
| | - Guodong Yu
- Department of Hepatobiliary Surgery, Affiliated Hospital of Hebei University, Baoding, 07100, China
| | - Quan Zhang
- Department of Hepatobiliary Surgery, Affiliated Hospital of Hebei University, Baoding, 07100, China
| | - Xixi Tian
- Department of Hepatobiliary Surgery, Affiliated Hospital of Hebei University, Baoding, 07100, China.
| | - Yuan Wang
- Department of Hepatobiliary Surgery, Affiliated Hospital of Hebei University, Baoding, 07100, China.
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Hong J, Eun JW, Baek GO, Cheong JY, Park S, Kim SS, Cho HJ, Lim SB. Multiomics profiling of buffy coat and plasma unveils etiology-specific signatures in hepatocellular carcinoma. Clin Mol Hepatol 2024; 30:360-374. [PMID: 38486508 PMCID: PMC11261225 DOI: 10.3350/cmh.2024.0042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 03/12/2024] [Accepted: 03/14/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND/AIMS Hepatocellular carcinoma (HCC) is a leading cause of cancer mortality worldwide. Despite identification of several biomarkers for HCC diagnosis, challenges such as low sensitivity and intratumoral heterogeneity have impeded early detection, highlighting the need for etiology-specific blood biomarkers. METHODS We generated whole-transcriptome sequencing (WTS) and targeted proteome data from buffy coat and plasma samples from HCC patients. By integrating etiological information on viral infection, we investigated the etiology-specific gene expression landscape at the blood level. Validation of differentially expressed genes (DEGs) was performed using publicly available RNA-seq datasets and qRT‒PCR with AUC analyses. RESULTS Differential expression analyses with multiomics data revealed distinct gene expression profiles between HBV-associated HCC and nonviral HCC, indicating the presence of etiology-specific blood biomarkers. The identified DEGs were validated across multiple independent datasets, underscoring their utility as biomarkers. Additionally, single-cell RNA-seq analysis of HCC confirmed differences in DEG expression across distinct immune cell types. CONCLUSION Our buffy coat WTS data and plasma proteome data may serve as reliable sources for identifying etiology-specific blood biomarkers of HCC and might contribute to discovery of therapeutic targets for HCC across different etiologies.
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Affiliation(s)
- Jiwon Hong
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, Korea
| | - Jung Woo Eun
- Department of Gastroenterology, Ajou University School of Medicine, Suwon, Korea
| | - Geum Ok Baek
- Department of Gastroenterology, Ajou University School of Medicine, Suwon, Korea
| | - Jae Youn Cheong
- Department of Gastroenterology, Ajou University School of Medicine, Suwon, Korea
| | - Seryoung Park
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, Korea
| | - Soon Sun Kim
- Department of Gastroenterology, Ajou University School of Medicine, Suwon, Korea
| | - Hyo Jung Cho
- Department of Gastroenterology, Ajou University School of Medicine, Suwon, Korea
| | - Su Bin Lim
- Department of Biochemistry & Molecular Biology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, Korea
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Gil-Rojas S, Suárez M, Martínez-Blanco P, Torres AM, Martínez-García N, Blasco P, Torralba M, Mateo J. Prognostic Impact of Metabolic Syndrome and Steatotic Liver Disease in Hepatocellular Carcinoma Using Machine Learning Techniques. Metabolites 2024; 14:305. [PMID: 38921441 PMCID: PMC11205954 DOI: 10.3390/metabo14060305] [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: 04/30/2024] [Revised: 05/22/2024] [Accepted: 05/24/2024] [Indexed: 06/27/2024] Open
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) currently represents the predominant cause of chronic liver disease and is closely linked to a significant increase in the risk of hepatocellular carcinoma (HCC), even in the absence of liver cirrhosis. In this retrospective multicenter study, machine learning (ML) methods were employed to investigate the relationship between metabolic profile and prognosis at diagnosis in a total of 219 HCC patients. The eXtreme Gradient Boosting (XGB) method demonstrated superiority in identifying mortality predictors in our patients. Etiology was the most determining prognostic factor followed by Barcelona Clinic Liver Cancer (BCLC) and Eastern Cooperative Oncology Group (ECOG) classifications. Variables related to the development of hepatic steatosis and metabolic syndrome, such as elevated levels of alkaline phosphatase (ALP), uric acid, obesity, alcohol consumption, and high blood pressure (HBP), had a significant impact on mortality prediction. This study underscores the importance of metabolic syndrome as a determining factor in the progression of HCC secondary to MASLD. The use of ML techniques provides an effective tool to improve risk stratification and individualized therapeutic management in these patients.
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Affiliation(s)
- Sergio Gil-Rojas
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
| | - Miguel Suárez
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
| | - Pablo Martínez-Blanco
- Gastroenterology Department, Virgen de la Luz Hospital, 16002 Cuenca, Spain
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
| | - Ana M. Torres
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
| | | | - Pilar Blasco
- Department of Pharmacy, General University Hospital, 46014 Valencia, Spain
| | - Miguel Torralba
- Internal Medicine Unit, University Hospital of Guadalajara, 19002 Guadalajara, Spain
- Faculty of Medicine, Universidad de Alcalá de Henares, 28801 Alcalá de Henares, Spain
- Translational Research Group in Cellular Immunology (GITIC), Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
| | - Jorge Mateo
- Medical Analysis Expert Group, Instituto de Investigación Sanitaria de Castilla-La Mancha (IDISCAM), 45071 Toledo, Spain
- Medical Analysis Expert Group, Institute of Technology, Universidad de Castilla-La Mancha, 16071 Cuenca, Spain
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Ramachandran L, Abul Rub F, Hajja A, Alodhaibi I, Arai M, Alfuwais M, Makhzoum T, Yaqinuddin A, Al-Kattan K, Assiri AM, Broering DC, Chinnappan R, Mir TA, Mani NK. Biosensing of Alpha-Fetoprotein: A Key Direction toward the Early Detection and Management of Hepatocellular Carcinoma. BIOSENSORS 2024; 14:235. [PMID: 38785709 PMCID: PMC11117836 DOI: 10.3390/bios14050235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 04/16/2024] [Accepted: 04/25/2024] [Indexed: 05/25/2024]
Abstract
Hepatocellular carcinoma (HCC) is currently one of the most prevalent cancers worldwide. Associated risk factors include, but are not limited to, cirrhosis and underlying liver diseases, including chronic hepatitis B or C infections, excessive alcohol consumption, nonalcoholic fatty liver disease (NAFLD), and exposure to chemical carcinogens. It is crucial to detect this disease early on before it metastasizes to adjoining parts of the body, worsening the prognosis. Serum biomarkers have proven to be a more accurate diagnostic tool compared to imaging. Among various markers such as nucleic acids, circulating genetic material, proteins, enzymes, and other metabolites, alpha-fetoprotein (AFP) is a protein marker primarily used to diagnose HCC. However, current methods need a large sample and carry a high cost, among other challenges, which can be improved using biosensing technology. Early and accurate detection of AFP can prevent severe progression of the disease and ensure better management of HCC patients. This review sheds light on HCC development in the human body. Afterward, we outline various types of biosensors (optical, electrochemical, and mass-based), as well as the most relevant studies of biosensing modalities for non-invasive monitoring of AFP. The review also explains these sensing platforms, detection substrates, surface modification agents, and fluorescent probes used to develop such biosensors. Finally, the challenges and future trends in routine clinical analysis are discussed to motivate further developments.
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Affiliation(s)
- Lohit Ramachandran
- Microfluidics, Sensors and Diagnostics (μSenD) Laboratory, Centre for Microfluidics, Biomarkers, Photoceutics and Sensors (μBioPS), Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Farah Abul Rub
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
| | - Amro Hajja
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
| | - Ibrahim Alodhaibi
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
| | - Momo Arai
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
| | - Mohammed Alfuwais
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
| | - Tariq Makhzoum
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
| | - Ahmed Yaqinuddin
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
| | - Khaled Al-Kattan
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
- Lung Health Center Department, Organ Transplant Centre of Excellence, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Abdullah M. Assiri
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
- Tissue/Organ Bioengineering & BioMEMS Laboratory, Organ Transplant Centre of Excellence (TR&I-Dpt), King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Dieter C. Broering
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
- Tissue/Organ Bioengineering & BioMEMS Laboratory, Organ Transplant Centre of Excellence (TR&I-Dpt), King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Raja Chinnappan
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
- Tissue/Organ Bioengineering & BioMEMS Laboratory, Organ Transplant Centre of Excellence (TR&I-Dpt), King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Tanveer Ahmad Mir
- College of Medicine, Alfaisal University, Riyadh 11533, Saudi Arabia; (F.A.R.); (A.H.); (I.A.); (M.A.); (M.A.); (T.M.); (A.Y.); (K.A.-K.); (A.M.A.); (D.C.B.)
- Tissue/Organ Bioengineering & BioMEMS Laboratory, Organ Transplant Centre of Excellence (TR&I-Dpt), King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia
| | - Naresh Kumar Mani
- Microfluidics, Sensors and Diagnostics (μSenD) Laboratory, Centre for Microfluidics, Biomarkers, Photoceutics and Sensors (μBioPS), Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
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Höpfl S, Albadry M, Dahmen U, Herrmann KH, Kindler EM, König M, Reichenbach JR, Tautenhahn HM, Wei W, Zhao WT, Radde NE. Bayesian modelling of time series data (BayModTS)-a FAIR workflow to process sparse and highly variable data. Bioinformatics 2024; 40:btae312. [PMID: 38741151 PMCID: PMC11128094 DOI: 10.1093/bioinformatics/btae312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 04/11/2024] [Accepted: 05/13/2024] [Indexed: 05/16/2024] Open
Abstract
MOTIVATION Systems biology aims to better understand living systems through mathematical modelling of experimental and clinical data. A pervasive challenge in quantitative dynamical modelling is the integration of time series measurements, which often have high variability and low sampling resolution. Approaches are required to utilize such information while consistently handling uncertainties. RESULTS We present BayModTS (Bayesian modelling of time series data), a new FAIR (findable, accessible, interoperable, and reusable) workflow for processing and analysing sparse and highly variable time series data. BayModTS consistently transfers uncertainties from data to model predictions, including process knowledge via parameterized models. Further, credible differences in the dynamics of different conditions can be identified by filtering noise. To demonstrate the power and versatility of BayModTS, we applied it to three hepatic datasets gathered from three different species and with different measurement techniques: (i) blood perfusion measurements by magnetic resonance imaging in rat livers after portal vein ligation, (ii) pharmacokinetic time series of different drugs in normal and steatotic mice, and (iii) CT-based volumetric assessment of human liver remnants after clinical liver resection. AVAILABILITY AND IMPLEMENTATION The BayModTS codebase is available on GitHub at https://github.com/Systems-Theory-in-Systems-Biology/BayModTS. The repository contains a Python script for the executable BayModTS workflow and a widely applicable SBML (systems biology markup language) model for retarded transient functions. In addition, all examples from the paper are included in the repository. Data and code of the application examples are stored on DaRUS: https://doi.org/10.18419/darus-3876. The raw MRI ROI voxel data were uploaded to DaRUS: https://doi.org/10.18419/darus-3878. The steatosis metabolite data are published on FairdomHub: 10.15490/fairdomhub.1.study.1070.1.
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Affiliation(s)
- Sebastian Höpfl
- Institute for Stochastics and Applications, University of Stuttgart, 70569 Stuttgart, Germany
| | - Mohamed Albadry
- Experimental Transplantation Surgery, Department of General, Vascular and Visceral Surgery, University Hospital Jena, 07745 Jena, Germany
- Department of Pathology, Faculty of Veterinary Medicine, Menoufia University, Shebin Elkom, Menoufia, Egypt
| | - Uta Dahmen
- Experimental Transplantation Surgery, Department of General, Vascular and Visceral Surgery, University Hospital Jena, 07745 Jena, Germany
| | - Karl-Heinz Herrmann
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, 07743 Jena, Germany
| | - Eva Marie Kindler
- Clinic for General, Visceral and Vascular Surgery, Jena University Hospital, 07747 Jena, Germany
| | - Matthias König
- Institute for Biology, Faculty of Life Sciences, Humboldt-University Berlin, 10115 Berlin, Germany
| | - Jürgen Rainer Reichenbach
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, 07743 Jena, Germany
| | - Hans-Michael Tautenhahn
- Clinic for Visceral, Transplantation, Thoracic and Vascular Surgery, Leipzig University Hospital, 04103 Leipzig, Germany
| | - Weiwei Wei
- Experimental Transplantation Surgery, Department of General, Vascular and Visceral Surgery, University Hospital Jena, 07745 Jena, Germany
| | - Wan-Ting Zhao
- Medical Physics Group, Institute for Diagnostic and Interventional Radiology, University Hospital Jena, 07743 Jena, Germany
| | - Nicole Erika Radde
- Institute for Stochastics and Applications, University of Stuttgart, 70569 Stuttgart, Germany
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Nair DG, Weiskirchen R. Recent Advances in Liver Tissue Engineering as an Alternative and Complementary Approach for Liver Transplantation. Curr Issues Mol Biol 2023; 46:262-278. [PMID: 38248320 PMCID: PMC10814863 DOI: 10.3390/cimb46010018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 12/20/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024] Open
Abstract
Acute and chronic liver diseases cause significant morbidity and mortality worldwide, affecting millions of people. Liver transplantation is the primary intervention method, replacing a non-functional liver with a functional one. However, the field of liver transplantation faces challenges such as donor shortage, postoperative complications, immune rejection, and ethical problems. Consequently, there is an urgent need for alternative therapies that can complement traditional transplantation or serve as an alternative method. In this review, we explore the potential of liver tissue engineering as a supplementary approach to liver transplantation, offering benefits to patients with severe liver dysfunctions.
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Affiliation(s)
- Dileep G. Nair
- Institute of Molecular Pathobiochemistry, Experimental Gene Therapy and Clinical Chemistry (IFMPEGKC), Rheinisch-Westfälische Technische Hochschule (RWTH) University Hospital Aachen, D-52074 Aachen, Germany
| | - Ralf Weiskirchen
- Institute of Molecular Pathobiochemistry, Experimental Gene Therapy and Clinical Chemistry (IFMPEGKC), Rheinisch-Westfälische Technische Hochschule (RWTH) University Hospital Aachen, D-52074 Aachen, Germany
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