ArticleBMC cancer2015
A microRNA molecular modeling extension for prediction of colorectal cancer treatment.
Article in BMC cancer, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Prediction of Radiation Pneumonitis Using Genome-Scale Flux Analysis of RNA-Seq Derived From Peripheral Blood.Frontiers in medicine · 2021Article
- Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases.International journal of molecular sciences · 2019Review
- AutoAnalyze in Systems Biology.Bioinformatics and biology insights · 2019Article
- Identification of tRNA-derived ncRNAs in TCGA and NCI-60 panel cell lines and development of the public database tRFexplorer.Database : the journal of biological databases and curation · 2019Article
- Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction.Oncotarget · 2018Article
- The LncRNA Connectivity Map: Using LncRNA Signatures to Connect Small Molecules, LncRNAs, and Diseases.Scientific reports · 2017Article
- Understanding microRNA-mediated gene regulatory networks through mathematical modelling.Nucleic acids research · 2016Article
- hERG1 positivity and Glut-1 negativity identifies high-risk TNM stage I and II colorectal cancer patients, regardless of adjuvant chemotherapy.OncoTargets and therapy · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral studies show that the regulatory impact of microRNAs (miRNAs) is an essential contribution to the pathogenesis of colorectal cancer (CRC). The expression levels of diverse miRNAs are associated with specific clinical diagnoses and prognoses of CRC. However, this association reveals very little actionable information with regard to how or whether to treat a CRC patient. To address this problem, we use miRNA expression data along with other molecular information to predict individual response of CRC cell lines and CRC patients.
methodsA strategy has been developed to join four types of information: molecular, kinetic, genetic and treatment data for prediction of individual treatment response of CRC.
resultsInformation on miRNA regulation, including miRNA target regulation and transcriptional regulation of miRNA, in integrated into an in silico molecular model for colon cancer. This molecular model is applied to study responses of seven CRC cell lines from NCI-60 to ten agents targeting signaling pathways. Predictive results of models without and with implemented miRNA information are compared and advantages are shown for the extended model. Finally, the extended model was applied to the data of 22 CRC patients to predict response to treatments of sirolimus and LY294002. The in silico results can also replicate the oncogenic and tumor suppression roles of miRNA on the therapeutic response as reported in the literature.
conclusionsIn summary, the results reveal that detailed molecular events can be combined with individual genetic data, including gene/miRNA expression data, to enhance in silico prediction of therapeutic response of individual CRC tumors. The study demonstrates that miRNA information can be applied as actionable information regarding individual therapeutic response.
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.