Gene expression profiling of papillary thyroid carcinomas in Korean patients by oligonucleotide microarrays.
10.4174/jkss.2012.82.5.271
- Author:
Ki Wook CHUNG
1
;
Seok Won KIM
;
Sun Wook KIM
Author Information
1. Center for Thyroid Cancer, Research Institute and Hospital, National Cancer Center, Goyang, Korea.
- Publication Type:Original Article
- Keywords:
Thyroid neoplasms;
Papillary;
Microarray;
Gene expression profiling
- MeSH:
Factor IX;
Gene Expression;
Gene Expression Profiling;
Humans;
Incidence;
Korea;
Oligonucleotide Array Sequence Analysis;
Support Vector Machine;
Thyroid Gland;
Thyroid Neoplasms
- From:Journal of the Korean Surgical Society
2012;82(5):271-280
- CountryRepublic of Korea
- Language:English
-
Abstract:
PURPOSE: The incidence of papillary thyroid carcinomas (PTCs) is rapidly increasing in Korea. Analyzing the gene expression profiling (GEP) of PTCs will facilitate the advent of new methods in diagnosis, prognostication, and treatment. We performed this study to find the GEP of Korean PTCs. METHODS: We performed oligonucleotide microarray analysis with 19 PTCs and 7 normal thyroid glands. Differentially expressed genes were selected using a t-test (|fold| >3) and adjusted Benjamini-Hochberg false discovery rate P-value < 0.01. Quantitative reverse transcription-polymerase chain reaction (QRT-PCR) was used to validate microarray data. A classification model was developed by support vector machine (SVM) algorithm to diagnose PTCs based on molecular signatures. RESULTS: We identified 79 differentially expressed genes (70 up-regulated and 9 down-regulated) according to the criteria. QRT-PCR for five genes (CDH3, NGEF, PROS1, TGFA, MET) was confirmatory of the microarray data. Hierarchical cluster analysis and a classification model by the SVM algorithm accurately differentiated PTCs from normal thyroid gland based on GEP. CONCLUSION: A disease classification model showed excellent accuracy in diagnosing PTCs, thus showing the possibility of molecular diagnosis in the future. This GEP could serve as baseline data for further investigation in the management of PTCs based on molecular signatures.
