Evidence map›Paper›PMID 40807373›Full record

ArticleMolecules (Basel, Switzerland)2025

Identification of Anticancer Target Combinations to Treat Pancreatic Cancer and Its Associated Cachexia Using Constraint-Based Modeling.

Feng-Sheng Wang, Ching-Kai Wu, Kuang-Tse Huang

Abstract read
In one paragraph

Article in Molecules (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Feng-Sheng WangDepartment of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan.ORCID 0000-0001-5266-2346
Ching-Kai WuDepartment of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan.
Kuang-Tse HuangDepartment of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan.ORCID 0000-0001-7676-0813

Funding

National Science and Technology Council, Taiwan NSTC113-2221-E-194-001
6 · The paper itself

Abstract

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.

Indexed as

Antineoplastic AgentsCachexiaCarcinoma, Pancreatic DuctalPancreatic NeoplasmsCell Line, TumorHumansMetabolic Networks and PathwaysModels, BiologicalMolecular Targeted TherapySystems BiologyAntineoplastic Agentsbiomarker identificationcancer metabolismconstraint-based modelingdrug target discoveryfuzzy optimizationnested hybrid differential evolution

Identifiers

PMID40807373
PMCPMC12348900

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.