ArticleBMC bioinformatics2013
Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
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- Re-evaluation of publicly available gene-expression databases using machine-learning yields a maximum prognostic power in breast cancer.Scientific reports · 2023Article
- MiR-520d-3p suppresses the proliferation and epithelial-mesenchymal transition of cervical cancer cells by targeting ZFP36L2.Heliyon · 2023Article
- Potential prognostic and predictive value of UBE2N, IMPDH1, DYNC1LI1 and HRASLS2 in colorectal cancer stool specimens.Biomedical reports · 2023Article
- Application of Artificial Intelligence Techniques to Predict Risk of Recurrence of Breast Cancer: A Systematic Review.Journal of personalized medicine · 2022Review
- Demystifying DPP III Catalyzed Peptide Hydrolysis-Computational Study of the Complete Catalytic Cycle of Human DPP III Catalyzed Tynorphin Hydrolysis.International journal of molecular sciences · 2022Article
- High expression of ZFP36L2 correlates with the prognosis and immune infiltration in lower-grade glioma.Frontiers in genetics · 2022Article
- Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer.Scientific reports · 2021Article
- Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development.International journal of environmental research and public health · 2021Article
- Over-Expression of Centromere Protein U Participates in the Malignant Neoplastic Progression of Breast Cancer.Frontiers in oncology · 2021Article
- Genome wide meta-analysis of cDNA datasets reveals new target gene signatures of colorectal cancer based on systems biology approach.Journal of biological research (Thessalonike, Greece) · 2020Article
- Constructing cancer patient-specific and group-specific gene networks with multi-omics data.BMC medical genomics · 2020Article
- Article
- Comparative evaluation of network features for the prediction of breast cancer metastasis.BMC medical genomics · 2020Article
- Six novel immunoglobulin genes as biomarkers for better prognosis in triple-negative breast cancer by gene co-expression network analysis.Scientific reports · 2019Article
- Cascaded Wx: A Novel Prognosis-Related Feature Selection Framework in Human Lung Adenocarcinoma Transcriptomes.Frontiers in genetics · 2019Article
- The effect of centromere protein U silencing by lentiviral mediated RNA interference on the proliferation and apoptosis of breast cancer.Oncology letters · 2018Article
- BAG3 Overexpression and Cytoprotective Autophagy Mediate Apoptosis Resistance in Chemoresistant Breast Cancer Cells.Neoplasia (New York, N.Y.) · 2018Article
- PCM-SABRE: a platform for benchmarking and comparing outcome prediction methods in precision cancer medicine.BMC bioinformatics · 2017Article
- Article
Corrections and comments
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Authors and funding
13 authors at 7 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann-Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression.
resultsThe DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence.
conclusionsThe 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence.
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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.