A robust biomarker of differential correlations improves the diagnosis of cytologically indeterminate thyroid cancers

Gómez-Rueda, Hugo; Palacios-Corona, Rebeca; Gutiérrez-Hermosillo, Hugo; Treviño, Víctor

doi:10.3892/ijmm.2016.2534

Cited by 20 publications

(17 citation statements)

References 55 publications

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“…This highlights that the use of high-performing biomarkers (epithelial) alone is insufficient to reach optimal diagnostic performance, and including apparently poor-performing biomarkers (mostly inflammatory) seems to be key for correct ITN classification. Consistently, previous gene classifiers have been reported to include a combination of both epithelial and inflammatory biomarkers, where poorly performing genes may help to recognize stromal or inflammatory patterns of cell populations associated with different histopathology subtypes ( 11 , 26 , 27 ).…”

Section: Discussionsupporting

confidence: 66%

A 10-Gene Classifier for Indeterminate Thyroid Nodules: Development and Multicenter Accuracy Study

González

Martı́nez

Vargas-Salas

et al. 2017

Thyroid

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Background: In most of the world, diagnostic surgery remains the most frequent approach for indeterminate thyroid cytology. Although several molecular tests are available for testing in centralized commercial laboratories in the United States, there are no available kits for local laboratory testing. The aim of this study was to develop a prototype in vitro diagnostic (IVD) gene classifier for the further characterization of nodules with an indeterminate thyroid cytology.Methods: In a first stage, the expression of 18 genes was determined by quantitative polymerase chain reaction (qPCR) in a broad histopathological spectrum of 114 fresh-tissue biopsies. Expression data were used to train several classifiers by supervised machine learning approaches. Classifiers were tested in an independent set of 139 samples. In a second stage, the best classifier was chosen as a model to develop a multiplexed-qPCR IVD prototype assay, which was tested in a prospective multicenter cohort of fine-needle aspiration biopsies.Results: In tissue biopsies, the best classifier, using only 10 genes, reached an optimal and consistent performance in the ninefold cross-validated testing set (sensitivity 93% and specificity 81%). In the multicenter cohort of fine-needle aspiration biopsy samples, the 10-gene signature, built into a multiplexed-qPCR IVD prototype, showed an area under the curve of 0.97, a positive predictive value of 78%, and a negative predictive value of 98%. By Bayes' theorem, the IVD prototype is expected to achieve a positive predictive value of 64–82% and a negative predictive value of 97–99% in patients with a cancer prevalence range of 20–40%.Conclusions: A new multiplexed-qPCR IVD prototype is reported that accurately classifies thyroid nodules and may provide a future solution suitable for local reference laboratory testing.

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Section: Discussionsupporting

confidence: 66%

A 10-Gene Classifier for Indeterminate Thyroid Nodules: Development and Multicenter Accuracy Study

González

Martı́nez

Vargas-Salas

et al. 2017

Thyroid

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“…DNA2 nuclease has a role in the mechanism of double strand break repair and its mutation has been reported in gastric and colorectal carcinomas [ 50 ]. In malignant thyroid nodules, a different expression of GALE has been reported [ 51 ], while SLC4A4 has been included in a 15-gene profile proposed as diagnostic biomarkers of thyroid tumour [ 52 ].…”

Section: Resultsmentioning

confidence: 99%

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis

et al. 2018

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BackgroundModern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network.ResultsWe present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data.A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer.Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools.Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study.ConclusionsOur analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4423-x) contains supplementary material, which is available to authorized users.

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“…In addition, SEMA3D had superior diagnostic accuracy independently of the cytology in six datasets including The Cancer Genome Atlas (TCGA) thyroid dataset. This gene exhibited differences in the correlation coefficients between benign and malignant samples and could be an effective clinical biomarker for diagnosis of thyroid cancer [ 86 ]. Similarly, Steffen et al [ 87 ] assembled a miRNA–protein target network for 677 human miRNAs and 18,880 targets which are listed in the TargetScan ( ).…”

Section: Discussionmentioning

confidence: 99%

Molecular Network-Based Identification of Competing Endogenous RNAs in Thyroid Carcinoma

Dai

et al. 2018

Genes

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RNAs may act as competing endogenous RNAs (ceRNAs), a critical mechanism in determining gene expression regulations in many cancers. However, the roles of ceRNAs in thyroid carcinoma remains elusive. In this study, we have developed a novel pipeline called Molecular Network-based Identification of ceRNA (MNIceRNA) to identify ceRNAs in thyroid carcinoma. MNIceRNA first constructs micro RNA (miRNA)–messenger RNA (mRNA)long non-coding RNA (lncRNA) networks from miRcode database and weighted correlation network analysis (WGCNA), based on which to identify key drivers of differentially expressed RNAs between normal and tumor samples. It then infers ceRNAs of the identified key drivers using the long non-coding competing endogenous database (lnCeDB). We applied the pipeline into The Cancer Genome Atlas (TCGA) thyroid carcinoma data. As a result, 598 lncRNAs, 1025 mRNAs, and 90 microRNA (miRNAs) were inferred to be differentially expressed between normal and thyroid cancer samples. We then obtained eight key driver miRNAs, among which hsa-mir-221 and hsa-mir-222 were key driver RNAs identified by both miRNA–mRNA–lncRNA and WGCNA network. In addition, hsa-mir-375 was inferred to be significant for patients’ survival with 34 associated ceRNAs, among which RUNX2, DUSP6 and SEMA3D are known oncogenes regulating cellular proliferation and differentiation in thyroid cancer. These ceRNAs are critical in revealing the secrets behind thyroid cancer progression and may serve as future therapeutic biomarkers.

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A robust biomarker of differential correlations improves the diagnosis of cytologically indeterminate thyroid cancers

Cited by 20 publications

References 55 publications

A 10-Gene Classifier for Indeterminate Thyroid Nodules: Development and Multicenter Accuracy Study

A 10-Gene Classifier for Indeterminate Thyroid Nodules: Development and Multicenter Accuracy Study

Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis

Molecular Network-Based Identification of Competing Endogenous RNAs in Thyroid Carcinoma

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