Meta-Analysis and Meta-Review of Thyroid Cancer Gene Expression Profiling Studies Identifies Important Diagnostic Biomarkers

2006 ◽  
Vol 24 (31) ◽  
pp. 5043-5051 ◽  
Author(s):  
Obi L. Griffith ◽  
Adrienne Melck ◽  
Steven J.M. Jones ◽  
Sam M. Wiseman
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Expression Profiling ◽  
Meta Analysis ◽  
Thyroid Tumors ◽  
Raw Data ◽  
Diagnostic Biomarkers ◽  
Meta Review ◽  
Average Fold Change ◽  
Clinically Significant

Purpose An estimated 4% to 7% of the population will develop a clinically significant thyroid nodule during their lifetime. In many cases, preoperative diagnoses by needle biopsy are inconclusive. Thus, there is a clear need for improved diagnostic tests to distinguish malignant from benign thyroid tumors. The recent development of high-throughput molecular analytic techniques should allow the rapid evaluation of new diagnostic markers. However, researchers are faced with an overwhelming number of potential markers from numerous thyroid cancer expression profiling studies. Materials and Methods To address this challenge, we have carried out a comprehensive meta-review of thyroid cancer biomarkers from 21 published studies. A gene ranking system that considers the number of comparisons in agreement, total number of samples, average fold-change and direction of change was devised. Results We have observed that genes are consistently reported by multiple studies at a highly significant rate (P < .05). Comparison with a meta-analysis of studies reprocessed from raw data showed strong concordance with our method. Conclusion Our approach represents a useful method for identifying consistent gene expression markers when raw data are unavailable. A review of the top 12 candidates revealed well known thyroid cancer markers such as MET, TFF3, SERPINA1, TIMP1, FN1, and TPO as well as relatively novel or uncharacterized genes such as TGFA, QPCT, CRABP1, FCGBP, EPS8 and PROS1. These candidates should help to develop a panel of markers with sufficient sensitivity and specificity for the diagnosis of thyroid tumors in a clinical setting.

scholarly journals Notch Signaling Is Involved in Expression of Thyrocyte Differentiation Markers and Is Down-Regulated in Thyroid Tumors

2008 ◽  
Vol 93 (10) ◽  
pp. 4080-4087 ◽  
Author(s):  
E. Ferretti ◽  
E. Tosi ◽  
A. Po ◽  
A. Scipioni ◽  
R. Morisi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Notch Signaling ◽  
Notch Pathway ◽  
Human Thyroid ◽  
Thyroid Tumors ◽  
Follicular Cells ◽  
Differentiation Markers ◽  
Thyroid Follicular Cells ◽  
Thyroid Cancer Cells

Context: Notch genes encode receptors for a signaling pathway that regulates cell growth and differentiation in various contexts, but the role of Notch signaling in thyroid follicular cells has never been fully published. Objective: The objective of the study was to characterize the expression of Notch pathway components in thyroid follicular cells and Notch signaling activities in normal and transformed thyrocytes. Design/Setting and Patients: Expression of Notch pathway components and key markers of thyrocyte differentiation was analyzed in murine and human thyroid tissues (normal and tumoral) by quantitative RT-PCR and immunohistochemistry. The effects of Notch overexpression in human thyroid cancer cells and FTRL-5 cells were explored with analysis of gene expression, proliferation assays, and experiments involving transfection of a luciferase reporter construct containing human NIS promoter regions. Results: Notch receptors are expressed during the development of murine thyrocytes, and their expression levels parallel those of thyroid differentiation markers. Notch signaling characterized also normal adult thyrocytes and is regulated by TSH. Notch pathway components are variably expressed in human normal thyroid tissue and thyroid tumors, but expression levels are clearly reduced in undifferentiated tumors. Overexpression of Notch-1 in thyroid cancer cells restores differentiation, reduces cell growth rates, and stimulates NIS expression via a direct action on the NIS promoter. Conclusion: Notch signaling is involved in the determination of thyroid cell fate and is a direct regulator of thyroid-specific gene expression. Its deregulation may contribute to the loss of differentiation associated with thyroid tumorigenesis.

scholarly journals NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis

2019 ◽  
Vol 47 (W1) ◽  
pp. W234-W241 ◽  
Author(s):  
Guangyan Zhou ◽  
Othman Soufan ◽  
Jessica Ewald ◽  
Robert E W Hancock ◽  
Niladri Basu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Visual Analytics ◽  
Regulatory Networks ◽  
Meta Analysis ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Perform Gene Expression ◽  
And Performance

Abstract The growing application of gene expression profiling demands powerful yet user-friendly bioinformatics tools to support systems-level data understanding. NetworkAnalyst was first released in 2014 to address the key need for interpreting gene expression data within the context of protein-protein interaction (PPI) networks. It was soon updated for gene expression meta-analysis with improved workflow and performance. Over the years, NetworkAnalyst has been continuously updated based on community feedback and technology progresses. Users can now perform gene expression profiling for 17 different species. In addition to generic PPI networks, users can now create cell-type or tissue specific PPI networks, gene regulatory networks, gene co-expression networks as well as networks for toxicogenomics and pharmacogenomics studies. The resulting networks can be customized and explored in 2D, 3D as well as Virtual Reality (VR) space. For meta-analysis, users can now visually compare multiple gene lists through interactive heatmaps, enrichment networks, Venn diagrams or chord diagrams. In addition, users have the option to create their own data analysis projects, which can be saved and resumed at a later time. These new features are released together as NetworkAnalyst 3.0, freely available at https://www.networkanalyst.ca.

scholarly journals A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors

BMC Genomics ◽  
2006 ◽  
Vol 7 (1) ◽  
Author(s):  
Naoto Yukinawa ◽  
Shigeyuki Oba ◽  
Kikuya Kato ◽  
Kazuya Taniguchi ◽  
Kyoko Iwao-Koizumi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Probabilistic Model ◽  
Expression Profiling ◽  
Thyroid Tumors ◽  
Model Application
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