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      Signature of immune-related metabolic genes predicts the prognosis of hepatocellular carcinoma

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          Abstract

          Introduction

          The majority of liver cancer cases (90%) are attributed to hepatocellular carcinoma (HCC), which exhibits significant heterogeneity and an unfavorable prognosis. Modulating the immune response and metabolic processes play a crucial role in both the prevention and treatment of HCC. However, there is still a lack of comprehensive understanding regarding the immune-related metabolic genes that can accurately reflect the prognosis of HCC.

          Methods

          In order to address this issue, we developed a prognostic prediction model based on immune and metabolic genes. To evaluate the accuracy of our model, we performed survival analyses including Kaplan-Meier (K-M) curve and time-dependent receiver operating characteristic (ROC) curve. Furthermore, we compared the predictive performance of our risk model with existing models. Finally, we validated the accuracy of our risk model using mouse models with in situ transplanted liver cancer.

          Results

          By conducting lasso regression analysis, we identified four independent prognostic genes: fatty acid binding protein 6 (FABP6), phosphoribosyl pyrophosphate amidotransferase (PPAT), spermine synthase (SMS), and dihydrodiol dehydrogenase (DHDH). Based on these findings, we constructed a prognostic model. Survival analysis revealed that the high-risk group had significantly lower overall survival (OS) rates. Besides that, the ROC curve demonstrated the effective prognostic capability of our risk model for hepatocellular carcinoma (HCC) patients. Furthermore, through animal experiments, we validated the accuracy of our model by showing a correlation between high-risk scores and poor prognosis in tumor development.

          Discussion

          In conclusion, our prognostic model surpasses those solely based on immune genes or metabolic genes in terms of accuracy. We observed variations in prognosis among different risk groups, accompanied by distinct immune and metabolic characteristics. Therefore, our model provides an original evaluation index for personalized clinical treatment strategies targeting HCC patients.

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          Most cited references37

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          Atezolizumab plus Bevacizumab in Unresectable Hepatocellular Carcinoma

          The combination of atezolizumab and bevacizumab showed encouraging antitumor activity and safety in a phase 1b trial involving patients with unresectable hepatocellular carcinoma.
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            Hepatocellular carcinoma

            Liver cancer remains a global health challenge, with an estimated incidence of >1 million cases by 2025. Hepatocellular carcinoma (HCC) is the most common form of liver cancer and accounts for ~90% of cases. Infection by hepatitis B virus and hepatitis C virus are the main risk factors for HCC development, although non-alcoholic steatohepatitis associated with metabolic syndrome or diabetes mellitus is becoming a more frequent risk factor in the West. Moreover, non-alcoholic steatohepatitis-associated HCC has a unique molecular pathogenesis. Approximately 25% of all HCCs present with potentially actionable mutations, which are yet to be translated into the clinical practice. Diagnosis based upon non-invasive criteria is currently challenged by the need for molecular information that requires tissue or liquid biopsies. The current major advancements have impacted the management of patients with advanced HCC. Six systemic therapies have been approved based on phase III trials (atezolizumab plus bevacizumab, sorafenib, lenvatinib, regorafenib, cabozantinib and ramucirumab) and three additional therapies have obtained accelerated FDA approval owing to evidence of efficacy. New trials are exploring combination therapies, including checkpoint inhibitors and tyrosine kinase inhibitors or anti-VEGF therapies, or even combinations of two immunotherapy regimens. The outcomes of these trials are expected to change the landscape of HCC management at all evolutionary stages.
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              Hepatocellular carcinoma

              Hepatocellular carcinoma appears frequently in patients with cirrhosis. Surveillance by biannual ultrasound is recommended for such patients because it allows diagnosis at an early stage, when effective therapies are feasible. The best candidates for resection are patients with a solitary tumour and preserved liver function. Liver transplantation benefits patients who are not good candidates for surgical resection, and the best candidates are those within Milan criteria (solitary tumour ≤5 cm or up to three nodules ≤3 cm). Image-guided ablation is the most frequently used therapeutic strategy, but its efficacy is limited by the size of the tumour and its localisation. Chemoembolisation has survival benefit in asymptomatic patients with multifocal disease without vascular invasion or extrahepatic spread. Finally, sorafenib, lenvatinib, which is non-inferior to sorafenib, and regorafenib increase survival and are the standard treatments in advanced hepatocellular carcinoma. This Seminar summarises the scientific evidence that supports the current recommendations for clinical practice, and discusses the areas in which more research is needed.
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                Author and article information

                Contributors
                Role: Role:
                Role: Role:
                Role: Role:
                URI : https://loop.frontiersin.org/people/678651Role: Role: Role:
                URI : https://loop.frontiersin.org/people/1850719Role: Role: Role:
                URI : https://loop.frontiersin.org/people/1044376Role: Role:
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                25 November 2024
                2024
                : 15
                : 1481331
                Affiliations
                [1] 1 Innovation Center for Cancer Research, Laboratory of Radiation Oncology and Radiobiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital , Fuzhou, Fujian, China
                [2] 2 Huzhou Central Hospital, Fifth School of Clinical Medicine, Zhejiang Chinese Medical University , Huzhou, Zhejiang, China
                [3] 3 Interdisciplinary Institute for Medical Engineering, Fuzhou University , Fuzhou, Fujian, China
                [4] 4 XMU-Fujian Cancer Hospital Research Center of Metabolism and Cancer, Xiamen University , Xiamen, Fujian, China
                Author notes

                Edited by: Xiaogang Wu, University of Texas MD Anderson Cancer Center, United States

                Reviewed by: Ayse Banu Demir, İzmir University of Economics, Türkiye

                Jyothi Padiadpu, National Institutes of Health (NIH), United States

                †These authors have contributed equally to this work

                Article
                10.3389/fimmu.2024.1481331
                11625796
                39654885
                b00861b2-a4b1-4238-9fcb-64cc559e18e9
                Copyright © 2024 Zhuo, Xia, Lan, Chen, Wang and Liu

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 August 2024
                : 08 November 2024
                Page count
                Figures: 6, Tables: 0, Equations: 0, References: 37, Pages: 13, Words: 4215
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by grants from the Joint Funds for the Innovation of Science and Technology, Fujian province (2021Y9232, 2021Y9227, 2023Y9448), the Fujian provincial health technology project (2022ZD01005, 2022ZQNZD009), Natural Science Foundation of Fujian Province (2023J06038, 2023J05239), Talent research project funded by Fujian Provincial Cancer Hospital (2022YNY12, 2022YNY13, 2022YNG01), the Special Research Funds for Local Science and Technology Development Guided by Central Government (2023L3020) and the XMU-Fujian Cancer Hospital cooperation grant for the Research Center of Metabolism and Tumor.
                Categories
                Immunology
                Original Research
                Custom metadata
                Cancer Immunity and Immunotherapy

                Immunology
                liver cancer,immunity,metabolism,risk score,prognostic model
                Immunology
                liver cancer, immunity, metabolism, risk score, prognostic model

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