Identification of Anticancer Target Combinations to Treat Pancreatic Cancer and Its Associated Cachexia Using Constraint-Based Modeling
Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancr...
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MDPI AG
2025-07-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/30/15/3200 |
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| author | Feng-Sheng Wang Ching-Kai Wu Kuang-Tse Huang |
| author_facet | Feng-Sheng Wang Ching-Kai Wu Kuang-Tse Huang |
| author_sort | Feng-Sheng Wang |
| collection | DOAJ |
| description | Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. |
| format | Article |
| id | doaj-art-36d46371b2004303a9c82972277458b7 |
| institution | DOAJ |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Molecules |
| spelling | doaj-art-36d46371b2004303a9c82972277458b72025-08-20T03:02:49ZengMDPI AGMolecules1420-30492025-07-013015320010.3390/molecules30153200Identification of Anticancer Target Combinations to Treat Pancreatic Cancer and Its Associated Cachexia Using Constraint-Based ModelingFeng-Sheng Wang0Ching-Kai Wu1Kuang-Tse Huang2Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, TaiwanDepartment of Chemical Engineering, National Chung Cheng University, Chiayi 621301, TaiwanDepartment of Chemical Engineering, National Chung Cheng University, Chiayi 621301, TaiwanPancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology.https://www.mdpi.com/1420-3049/30/15/3200constraint-based modelingcancer metabolismdrug target discoverybiomarker identificationfuzzy optimizationnested hybrid differential evolution |
| spellingShingle | Feng-Sheng Wang Ching-Kai Wu Kuang-Tse Huang Identification of Anticancer Target Combinations to Treat Pancreatic Cancer and Its Associated Cachexia Using Constraint-Based Modeling Molecules constraint-based modeling cancer metabolism drug target discovery biomarker identification fuzzy optimization nested hybrid differential evolution |
| title | Identification of Anticancer Target Combinations to Treat Pancreatic Cancer and Its Associated Cachexia Using Constraint-Based Modeling |
| title_full | Identification of Anticancer Target Combinations to Treat Pancreatic Cancer and Its Associated Cachexia Using Constraint-Based Modeling |
| title_fullStr | Identification of Anticancer Target Combinations to Treat Pancreatic Cancer and Its Associated Cachexia Using Constraint-Based Modeling |
| title_full_unstemmed | Identification of Anticancer Target Combinations to Treat Pancreatic Cancer and Its Associated Cachexia Using Constraint-Based Modeling |
| title_short | Identification of Anticancer Target Combinations to Treat Pancreatic Cancer and Its Associated Cachexia Using Constraint-Based Modeling |
| title_sort | identification of anticancer target combinations to treat pancreatic cancer and its associated cachexia using constraint based modeling |
| topic | constraint-based modeling cancer metabolism drug target discovery biomarker identification fuzzy optimization nested hybrid differential evolution |
| url | https://www.mdpi.com/1420-3049/30/15/3200 |
| work_keys_str_mv | AT fengshengwang identificationofanticancertargetcombinationstotreatpancreaticcanceranditsassociatedcachexiausingconstraintbasedmodeling AT chingkaiwu identificationofanticancertargetcombinationstotreatpancreaticcanceranditsassociatedcachexiausingconstraintbasedmodeling AT kuangtsehuang identificationofanticancertargetcombinationstotreatpancreaticcanceranditsassociatedcachexiausingconstraintbasedmodeling |