ArticleEndocrine-related cancer2007
A multi-gene approach to differentiate papillary thyroid carcinoma from benign lesions: gene selection using support vector machines with bootstrapping.
Article in Endocrine-related cancer, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 73 citations in OpenAlex.
- Identification of Differentially Expressed Kinase and Screening Potential Anticancer Drugs in Papillary Thyroid Carcinoma.Disease markers · 2016Pooled it
- Identification of SERPINA1 as single marker for papillary thyroid carcinoma through microarray meta analysis and quantification of its discriminatory power in independent validation.BMC medical genomics · 2011Pooled it
- Pan-cancer analysis of UDP-glucose 6-dehydrogenase in human tumors and its function in hepatocellular carcinoma.World journal of gastrointestinal oncology · 2025Article
- Article
- TIM3 activates the ERK1/2 pathway to promote invasion and migration of thyroid tumors.PloS one · 2024Article
- Data Integration-Possibilities of Molecular and Clinical Data Fusion on the Example of Thyroid Cancer Diagnostics.International journal of molecular sciences · 2022Article
- Differences in Gene Expression Profile of Primary Tumors in Metastatic and Non-Metastatic Papillary Thyroid Carcinoma-Do They Exist?International journal of molecular sciences · 2020Article
- Papillary Thyroid Cancer: Genetic Alterations and Molecular Biomarker Investigations.International journal of medical sciences · 2019Review
- Novel biomarkerFuture science OA · 2018Article
- Gene signature of the post-Chernobyl papillary thyroid cancer.European journal of nuclear medicine and molecular imaging · 2016Article
- Proteomics of thyroid tumours provides new insights into their molecular composition and changes associated with malignancy.Scientific reports · 2016Article
- Expression profiles of pivotal microRNAs and targets in thyroid papillary carcinoma: an analysis of The Cancer Genome Atlas.OncoTargets and therapy · 2015Article
- Application of metabolomics in thyroid cancer research.International journal of endocrinology · 2015Review
- Interleukins as markers of inflammation in malignant and benign thyroid disease.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2014Article
- Comparison of metabolic ratios of urinary estrogens between benign and malignant thyroid tumors in postmenopausal women.BMC clinical pathology · 2013Article
- Stable feature selection and classification algorithms for multiclass microarray data.Biology direct · 2012Article
- Three-gene molecular diagnostic model for thyroid cancer.Thyroid : official journal of the American Thyroid Association · 2012Article
- Iodothyronine deiodinases and cancer.Journal of endocrinological investigation · 2011Review
- Strategy to find molecular signatures in a small series of rare cancers: validation for radiation-induced breast and thyroid tumors.PloS one · 2011Article
- Prediction of breast cancer by profiling of urinary RNA metabolites using Support Vector Machine-based feature selection.BMC cancer · 2009Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Selection of novel molecular markers is an important goal of cancer genomics studies. The aim of our analysis was to apply the multivariate bioinformatical tools to rank the genes - potential markers of papillary thyroid cancer (PTC) according to their diagnostic usefulness. We also assessed the accuracy of benign/malignant classification, based on gene expression profiling, for PTC. We analyzed a 180-array dataset (90 HG-U95A and 90 HG-U133A oligonucleotide arrays), which included a collection of 57 PTCs, 61 benign thyroid tumors, and 62 apparently normal tissues. Gene selection was carried out by the support vector machines method with bootstrapping, which allowed us 1) ranking the genes that were most important for classification quality and appeared most frequently in the classifiers (bootstrap-based feature ranking, BBFR); 2) ranking the samples, and thus detecting cases that were most difficult to classify (bootstrap-based outlier detection). The accuracy of PTC diagnosis was 98.5% for a 20-gene classifier, its 95% confidence interval (CI) was 95.9-100%, with the lower limit of CI exceeding 95% already for five genes. Only 5 of 180 samples (2.8%) were misclassified in more than 10% of bootstrap iterations. We specified 43 genes which are most suitable as molecular markers of PTC, among them some well-known PTC markers (MET, fibronectin 1, dipeptidylpeptidase 4, or adenosine A1 receptor) and potential new ones (UDP-galactose-4-epimerase, cadherin 16, gap junction protein 3, sushi, nidogen, and EGF-like domains 1, inhibitor of DNA binding 3, RUNX1, leiomodin 1, F-box protein 9, and tripartite motif-containing 58). The highest ranking gene, metallophosphoesterase domain-containing protein 2, achieved 96.7% of the maximum BBFR score.
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