ArticleFrontiers in oncology2019
Elucidating the Reprograming of Colorectal Cancer Metabolism Using Genome-Scale Metabolic Modeling.
Article in Frontiers in oncology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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.
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.
Who cites it
24 citing papers in PubMed, 41 citations in OpenAlex.
- A Systematic Machine Learning Framework for Evaluating and Ranking Omics Layers in Cancer Drug Response Prediction.Current issues in molecular biology · 2026Article
- ZNF33A activates Wnt/β-catenin signaling and promotes BCAA catabolism in colorectal cancer.Cell communication and signaling : CCS · 2026Article
- Transcriptional Activity of Genes Related to the Biotransformation Process in the Development of Colorectal Cancer.International journal of molecular sciences · 2025Article
- Abnormal β-Hydroxybutyrylation Modification of ARG1 Drives Reprogramming of Arginine Metabolism to Promote the Progression of Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Genome-scale metabolic modeling and machine learning unravel metabolic reprogramming and mast cell role in lung cancer: a multi-level analysis.Translational lung cancer research · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Linking Metastatic Behavior and Metabolic Heterogeneity of Circulating Tumor Cells at Single-Cell Level Using an Integrative Microfluidic System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- The functional correlation between mir-16-5p and BIRC5 gene in colorectal cancer: integrated analysis of transcriptomics and in vitro validation.Molecular biology reports · 2025Article
- Article
- Whole-cell energy modeling reveals quantitative changes of predicted energy flows in RAS mutant cancer cell lines.iScience · 2023Article
- Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems.Computational and structural biotechnology journal · 2023Review
- Exploring the metabolic landscape of pancreatic ductal adenocarcinoma cells using genome-scale metabolic modeling.iScience · 2022Article
- Comprehensive functional genomic analyses link APC somatic mutation and mRNA-miRNA networks to the clinical outcome of stage-III colorectal cancer patients.Biomedical journal · 2022Observational
- Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer.Frontiers in oncology · 2022Review
- Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment.Cancers · 2021Review
- Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer.Cancers · 2021Article
- Article
- Revealing the Metabolic Alterations during Biofilm Development ofMetabolites · 2021Article
- Acupuncture Synergized With Bortezomib Improves Survival of Multiple Myeloma MiceFrontiers in oncology · 2021Article
- Identifying Personalized Metabolic Signatures in Breast Cancer.Metabolites · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Colorectal cancer is the third most incidental cancer worldwide, and the response rate of current treatment for colorectal cancer is very low. Genome-scale metabolic models (GEMs) are systems biology platforms, and they had been used to assist researchers in understanding the metabolic alterations in different types of cancer. Here, we reconstructed a generic colorectal cancer GEM by merging 374 personalized GEMs from the Human Pathology Atlas and used it as a platform for systematic investigation of the difference between tumor and normal samples. The reconstructed model revealed the metabolic reprogramming in glutathione as well as the arginine and proline metabolism in response to tumor occurrence. In addition, six genes including ODC1, SMS, SRM, RRM2, SMOX, and SAT1 associated with arginine and proline metabolism were found to be key players in this metabolic alteration. We also investigated these genes in independent colorectal cancer patients and cell lines and found that many of these genes showed elevated level in colorectal cancer and exhibited adverse effect in patients. Therefore, these genes could be promising therapeutic targets for treatment of a specific colon cancer patient group.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.