title: A three-gene panel that distinguishes benign from malignant thyroid nodules.
abstract: Reliable preoperative diagnosis of malignant thyroid tumors remains challenging because of the inconclusive cytological examination of fine-needle aspiration biopsies. Although numerous studies have successfully demonstrated the use of high-throughput molecular diagnostics in cancer prediction, the application of microarrays in routine clinical use remains limited. Our aim was, therefore, to identify a small subset of genes to develop a practical and inexpensive diagnostic tool for clinical use. We developed a two-step feature selection method composed of a linear models for microarray data (LIMMA) linear model and an iterative Bayesian model averaging model to identify a suitable gene set signature. Using one public dataset for training, we discovered a three-gene signature dipeptidyl-peptidase 4 (DPP4), secretogranin V (SCG5) and carbonic anhydrase XII (CA12). We then evaluated the robustness of our gene set using three other independent public datasets. The gene signature accuracy was 85.7, 78.8 and 85.7%, respectively. For experimental validation, we collected 70 thyroid samples from surgery and our three-gene signature method achieved an accuracy of 94.3% by quantitative polymerase chain reaction (QPCR) experiment. Furthermore, immunohistochemistry in 29 samples showed proteins expressed by these three genes are also differentially expressed in thyroid samples. Our protocol discovered a robust three-gene signature that can distinguish benign from malignant thyroid tumors, which will have daily clinical application.
authors.affiliation: Shanghai Institute of Medical Genetics, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, China; Key Laboratory of Molecular Embryology, Ministry of Health and Shanghai Key Laboratory of Embryo and Reproduction Engineering, Shanghai, China; Department of Laboratory Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China.
authors.forename: Bing
authors.initials: B
authors.lastname: Zheng
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.forename: Jianlei
authors.initials: J
authors.lastname: Gu
authors.forename: Yao
authors.initials: Y
authors.lastname: Lu
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
authors.forename: Min
authors.initials: M
authors.lastname: Li
authors.forename: Hui
authors.initials: H
authors.lastname: Lu
mesh_terms: D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor / Q000235:genetics*; D002256:Carbonic Anhydrases / Q000235:genetics / Q000378:metabolism; D066264:Datasets as Topic; D003937:Diagnosis, Differential; D018819:Dipeptidyl Peptidase 4 / Q000235:genetics / Q000378:metabolism; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008297:Male; D008875:Middle Aged; D051859:Neuroendocrine Secretory Protein 7B2 / Q000235:genetics / Q000378:metabolism; D011379:Prognosis; D015203:Reproducibility of Results; D013964:Thyroid Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology*; D016606:Thyroid Nodule / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology*; D055815:Young Adult
chemical_list: D014408:Biomarkers, Tumor; D051859:Neuroendocrine Secretory Protein 7B2; C497678:SCG5 protein, human; C042807:DPP4 protein, human; D018819:Dipeptidyl Peptidase 4; D002256:Carbonic Anhydrases; C477378:carbonic anhydrase XII
keywords: biomarkers; diagnostic panel; machine learning; prediction model; thyroid cancer