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Two genome-scale metabolic models were reconstructed using RNA sequencing expression patterns from HCC and matched healthy tissues, then integrated with an anticancer target discovery framework using fuzzy objectives to suppress tumor growth while limiting side effects. Ten nutrient media were evaluated to assess uptake. Results highlighted cholesterol-pathway target identification under cholesterol-free conditions, inconsistent identification when cholesterol was included, and robust detection of PGS1 and CRL1 across media. 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through Constraint-Based Modeling Feng-Sheng Wang * and Hao-Xiang Zhang  \nCitation: Wang, F.-S.; Zhang, H.-X. Identification of Anticancer Enzymes and Biomarkers for Hepatocellular Carcinoma through Constraint-Based Modeling. Molecules 2024, 29, 2594 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)molecules29112594  \nAcademic Editor: Y. George Zheng  \nReceived: 12 April 2024  \nRevised: 26 May 2024  \nAccepted: 28 May 2024  \nPublished: 31 May 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nDepartment of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan; [aa0970797987@gmail.com](aa0970797987@gmail.com)  \n* Correspondence: [chmfsw@ccu.edu.tw](chmfsw@ccu.edu.tw)  \nAbstract: Hepatocellular carcinoma (HCC) results in the abnormal regulation of cellular metabolic pathways. Constraint-based modeling approaches can be utilized to dissect metabolic reprogramming, enabling the identification of biomarkers and anticancer targets for diagnosis and treatment. In this study, two genome-scale metabolic models (GSMMs) were reconstructed by employing RNA sequencing expression patterns of hepatocellular carcinoma (HCC) and their healthy counterparts. An anticancer target discovery (ACTD) framework was integrated with the two models to identify HCC targets for anticancer treatment. The ACTD framework encompassed four fuzzy objectives to assess both the suppression of cancer cell growth and the minimization of side effects during treatment. The composition of a nutrient may significantly affect target identification. Within the ACTD framework, ten distinct nutrient media were utilized to assess nutrient uptake for identifying potential anticancer enzymes. The findings revealed the successful identification of target enzymes within the cholesterol biosynthetic pathway using a cholesterol-free cell culture medium. Conversely, target enzymes in the cholesterol biosynthetic pathway were not identified when the nutrient uptake included a cholesterol component. Moreover, the enzymes PGS1 and CRL1 were detected in all ten nutrient media. Additionally, the ACTD framework comprises dual-group representations of target combinations, pairing a single-target enzyme with an additional nutrient uptake reaction. Additionally, the enzymes PGS1 and CRL1 were identified across the ten-nutrient media. Furthermore, the ACTD framework encompasses two-group representations of target combinations involving the pairing of a single-target enzyme with an additional nutrient uptake reaction. Computational analysis unveiled that cell viability for all dual-target combinations exceeded that of their respective single-target enzymes. Consequently, integrating a target enzyme while adjusting an additional exchange reaction could efficiently mitigate cell proliferation rates and ATP production in the treated cancer cells. Nevertheless, most dual-target combinations led to lower side effects in contrast to their single-target counterparts. Additionally, differential expression of metabolites between cancer cells and their healthy counterparts were assessed via parsimonious flux variability analysis employing the GSMMs to pinpoint potential biomarkers. The variabilities of the fluxes and metabolite flow rates in cancer and healthy cells were classified into seven categories. Accordingly, two secretions and thirteen uptakes (including eight essential amino acids and two conditionally essential amino acids) were identified as potential biomarkers. The findings of this study indicated that cancer cells exhi","cbCaicpn2YgbaMSP","https://ap.wps.com/l/cbCaicpn2YgbaMSP","pdf",7409849,"English","# Abstract\n# Introduction\n## Metabolic roles of the liver and HCC background\n## Cancer metabolism and metabolic reprogramming","[{\"question\":\"What is the purpose of the constraint-based modeling in this study?\",\"answer\":\"It is used to dissect metabolic reprogramming in hepatocellular carcinoma, enabling identification of biomarkers and anticancer targets for diagnosis and treatment.\"},{\"question\":\"How were the genome-scale metabolic models constructed?\",\"answer\":\"Two genome-scale metabolic models were reconstructed using RNA sequencing expression patterns from hepatocellular carcinoma and their healthy counterparts.\"},{\"question\":\"How did nutrient composition influence the identified anticancer enzymes?\",\"answer\":\"Target enzymes in the cholesterol biosynthetic pathway were identified with a cholesterol-free medium, but not when cholesterol uptake included a cholesterol component.\"}]","Identification of Anticancer Enzymes and Biomarkers for Hepatocellular Carcinoma through Constraint-Based Modeling | PDF",1790030842]