ArticleOncology letters2019
Identification of a 13-gene-based classifier as a potential biomarker to predict the effects of fluorouracil-based chemotherapy in colorectal cancer.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- High-accuracy prediction of colorectal cancer chemotherapy efficacy using machine learning applied to gene expression data.Frontiers in physiology · 2023Article
- Big Data to Knowledge: Application of Machine Learning to Predictive Modeling of Therapeutic Response in Cancer.Current genomics · 2021Review
- Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.International journal of molecular sciences · 2021Review
- Molecular determinants of response to 5-fluorouracil-based chemotherapy in colorectal cancer: The undisputable role of micro-ribonucleic acids.World journal of gastrointestinal oncology · 2020Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of the current study was to develop a predictor classifier for response to fluorouracil-based chemotherapy in patients with advanced colorectal cancer (CRC) using microarray gene expression profiles of primary CRC tissues. Using two expression profiles downloaded from the Gene Expression Omnibus database, differentially expressed genes (DEGs) between responders and non-responders to fluorouracil-based chemotherapy were identified. A total of 791 DEGs, including 303 that were upregulated and 488 that were downregulated in responders, were identified. Functional enrichment analysis revealed that the DEGs were primarily involved in 'cell mitosis', 'DNA replication' and 'cell cycle' signaling pathways. Following feature selection using two methods, a random forest classifier for response to fluorouracil-based chemotherapy with 13 DEGs was constructed. The accuracy of the 13-gene classifier was 0.930 in the training set and 0.810 in the validation set. The receiver operating characteristic curve analysis revealed that the area under the curve was 1.000 in the training set and 0.873 in the validation set (P=0.227). The 13-gene-based classifier described in the current study may be used as a potential biomarker to predict the effects of fluorouracil-based chemotherapy in patients with CRC.
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Registered trials
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