ArticleScientific reports2015
Flux balance analysis predicts essential genes in clear cell renal cell carcinoma metabolism.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 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
55 citing papers in PubMed, 108 citations in OpenAlex.
- Cancer-associated fibroblasts drive metabolic heterogeneity in colorectal cancer cells: predictions from metabolic modeling.NPJ systems biology and applications · 2026Article
- Model-driven exploration of lactose and galactose metabolism via an oxidoreductive pathway in Sungouiella intermedia for cell factory applications.Microbial cell factories · 2026Article
- Higher order synthetic lethals are keys to minimize cancer treatment effects on non-tumor cells.PloS one · 2026Article
- A constraint-based framework for exploring the impact of multireaction dependencies on metabolic functions.NPJ systems biology and applications · 2025Article
- A prognostic microRNA-based signature for localized clear cell renal cell carcinoma: the Bio-miR study.British journal of cancer · 2025Article
- Integrative systems biology approaches for analyzing microbiome dysbiosis and species interactions.Briefings in bioinformatics · 2025Review
- Genome-scale models in human metabologenomics.Nature reviews. Genetics · 2025Review
- Merging metabolic modeling and imaging for screening therapeutic targets in colorectal cancer.NPJ systems biology and applications · 2025Article
- Merging Metabolic Modeling and Imaging for Screening Therapeutic Targets in Colorectal Cancer.bioRxiv : the preprint server for biology · 2024Article
- Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.NPJ systems biology and applications · 2024Article
- Machine learning methods for predicting essential metabolic genes from Plasmodium falciparum genome-scale metabolic network.PloS one · 2024Article
- Article
- Hybrid computational models of multicellular tumour growth considering glucose metabolism.Computational and structural biotechnology journal · 2023Review
- Identifying essential genes in genome-scale metabolic models of consensus molecular subtypes of colorectal cancer.PloS one · 2023Article
- Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis.Bioprocess and biosystems engineering · 2022Article
- Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth.Scientific reports · 2022Article
- Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0.Nature communications · 2022Article
- Linear programming based gene expression model (LPM-GEM) predicts the carbon source for Bacillus subtilis.BMC bioinformatics · 2022Article
- Nicotinamide-N-methyltransferase is a promising metabolic drug target for primary and metastatic clear cell renal cell carcinoma.Clinical and translational medicine · 2022Article
- Required Gene Set for Autotrophic Growth ofApplied and environmental microbiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Flux balance analysis is the only modelling approach that is capable of producing genome-wide predictions of gene essentiality that may aid to unveil metabolic liabilities in cancer. Nevertheless, a systemic validation of gene essentiality predictions by flux balance analysis is currently missing. Here, we critically evaluated the accuracy of flux balance analysis in two cancer types, clear cell renal cell carcinoma (ccRCC) and prostate adenocarcinoma, by comparison with large-scale experiments of gene essentiality in vitro. We found that in ccRCC, but not in prostate adenocarcinoma, flux balance analysis could predict essential metabolic genes beyond random expectation. Five of the identified metabolic genes, AGPAT6, GALT, GCLC, GSS, and RRM2B, were predicted to be dispensable in normal cell metabolism. Hence, targeting these genes may selectively prevent ccRCC growth. Based on our analysis, we discuss the benefits and limitations of flux balance analysis for gene essentiality predictions in cancer metabolism, and its use for exposing metabolic liabilities in ccRCC, whose emergent metabolic network enforces outstanding anabolic requirements for cellular proliferation.
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.