ArticleOncotarget2016
Development of a prediction model for radiosensitivity using the expression values of genes and long non-coding RNAs.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed.
- Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model.Cancers · 2019Article
- Long Non-Coding RNAs in Gliomas: From Molecular Pathology to Diagnostic Biomarkers and Therapeutic Targets.International journal of molecular sciences · 2018Review
- PR-LncRNA signature regulates glioma cell activity through expression of SOX factors.Scientific reports · 2018Article
- A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy.Medical physics · 2018Article
- Differential miRNA expression profiling reveals miR-205-3p to be a potential radiosensitizer for low- dose ionizing radiation in DLD-1 cells.Oncotarget · 2018Article
- Machine Learning and Radiogenomics: Lessons Learned and Future Directions.Frontiers in oncology · 2018Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiotherapy has become a popular and standard approach for treating cancer patients because it greatly improves patient survival. However, some of the patients receiving radiotherapy suffer from adverse effects and do not obtain survival benefits. This may be attributed to the fact that most radiation treatment plans are designed based on cancer type, without consideration of each individual's radiosensitivity. A model for predicting radiosensitivity would help to address this issue. In this study, the expression levels of both genes and long non-coding RNAs (lncRNAs) were used to build such a prediction model. Analysis of variance and Tukey's honest significant difference tests (P < 0.001) were utilized in immortalized B cells (GSE26835) to identify differentially expressed genes and lncRNAs after irradiation. A total of 41 genes and lncRNAs associated with radiation exposure were revealed by a network analysis algorithm. To develop a predictive model for radiosensitivity, the expression profiles of NCI-60 cell lines along, with their radiation parameters, were analyzed. A genetic algorithm was proposed to identify 20 predictors, and the support vector machine algorithm was used to evaluate their prediction performance. The model was applied to 2 datasets of glioblastoma, The Cancer Genome Atlas and GSE16011, and significantly better survival was observed in patients with greater predicted radiosensitivity.
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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.