Association of Pro-apoptotic Bad Gene Expression Changes with Benign Thyroid Nodules

Aim: To investigate the clinical roles of LINC00152 and SNHG12 in papillary thyroid carcinoma (PTC). Methods: LINC00152 and SNHG12 expression was sought and analysis in gene expression omnibus, The Cancer Genome Atlas and GEPIA datasets. Tumor and adjacent normal tissues were collected from 97 PTC and 44 benign thyroid nodules patients. The expression was evaluated by quantitative real-time polymerase chain reaction. The association between the expression level and clinicopathologic characteristics was analyzed by χ2 test. Receiver operating characteristic curves were plotted to evaluate the diagnostic value. Results: The expression of SNHG12 and LINC00152 were significantly higher in PTC tissues than in adjacent normal tissues not only in gene expression omnibus database but the validated samples. More interesting, LINC00152 expression level was also significantly higher in PTC tissues than that in benign thyroid nodules. The upregulation of LINC00152 and SNHG12 was associated with the malignant progression of PTC. Receiver operating characteristic curve analysis also demonstrated that there was a good trend, which indicates that they may have certain diagnostic value. Conclusion: LINC00152 and SNHG12 might serve as serve as potential related molecules of PTC.

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A gene expression signature that distinguishes malignant from benign thyroid nodules.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.e21011 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. e21011-e21011

Author(s):

Song Tian ◽

John DiCarlo ◽

Jiaye Yu ◽

George J Quellhorst ◽

Raymond K Blanchard ◽

...

Keyword(s):

Gene Expression ◽

Random Forest ◽

Thyroid Nodule ◽

Thyroid Nodules ◽

Gene Expression Signature ◽

Support Vector ◽

Data Set ◽

Expression Signature ◽

Benign Thyroid Nodules ◽

Benign Thyroid

e21011 Background: Thyroid nodules can be detected in as high as 67% of the population. Distinguishing thyroid cancers from benign lesions is crucial for determining an appropriate treatment plan. For years a gene expression signature for discriminating malignant from benign thyroid nodules has been sought by clinicians. In this study, multivariate bioinformatics tools were used to generate a qPCR based gene expression signature for determining malignancy in thyroid nodules. Methods: Multiple mathematical models, such as Random Forest, Support Vector Machine (SVM), and Nearest Shrunken Centroid (NSC), were used to analyze published microarray data sets and select 366 putative classifier (biomarker) mRNA targets. The selected 366 genes were further evaluated for their expression pattern by real-time PCR using a panel of 49 pathology assessed thyroid nodule samples (fresh frozen, 23 malignant and 26 benign). Results: Using the qPCR data set, Random Forest was compared with SVM and NSC classifier methods and was found to be more successful in finding genes with better discriminative powers. A Random Forest method identified a panel of 7 genes together with 5 reference genes as a gene expression signature for thyroid malignancy, which led to the development of a companion classifying algorithm to provide a probability score to assess malignancy of thyroid nodules. In our limited sample set, this signature was shown to distinguish malignant and benign thyroid nodules with 92% accuracy and 100% specificity. Conclusions: Our results suggest that a combination of multiple bioinformatics analysis tools is the proper approach for biomarker candidate selection from high-throughput gene expression data. As demonstrated here, panel of 12 genes and a companion classification algorithm has the potential to successfully discriminate malignant thyroid nodule with high accuracy and specificity. This panel of twelve genes is for molecular biology applications only.

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Use of the Afirma® Gene Expression Classifier for Preoperative Identification of Benign Thyroid Nodules with Indeterminate Fine Needle Aspiration Cytopathology

PLoS Currents ◽

10.1371/currents.eogt.e557cbb5c7e4f66568ce582a373057e7 ◽

2013 ◽

Cited By ~ 2

Author(s):

Syed Z. Ali ◽

Stephanie A. Fish ◽

Richard Lanman ◽

Gregory W. Randolph ◽

Julie Ann Sosa

Keyword(s):

Gene Expression ◽

Fine Needle Aspiration ◽

Thyroid Nodules ◽

Needle Aspiration ◽

Fine Needle ◽

Gene Expression Classifier ◽

Benign Thyroid Nodules ◽

Benign Thyroid

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MON-516 Quantitative Analysis of Differential Enigma/PDLIM7 Gene Expression in Thyroid Cancer vs. Benign Nodules

Journal of the Endocrine Society ◽

10.1210/jendso/bvaa046.1459 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

Author(s):

Kari Roberts ◽

Sang Hee K Choi ◽

Ethan Frank ◽

David Foulad ◽

Saeid Mirshahidi ◽

...

Keyword(s):

Gene Expression ◽

Thyroid Cancer ◽

Mrna Expression ◽

Cancer Progression ◽

Thyroid Nodules ◽

Quantitative Difference ◽

Expression Level ◽

Published Data ◽

Benign Thyroid Nodules ◽

Benign Thyroid

Abstract Thyroid cancer incidence is rising worldwide. Although fine-needle aspiration biopsy (FNAB) is an accurate modality for evaluating thyroid nodules, up to 25% of FNABs still yield indeterminate results. There is a considerable number of diagnostic thyroidectomies for benign disease as a result of indeterminate FNAB. A more accurate and time-efficient diagnostic approach for analyzing indeterminate thyroid nodules may reduce diagnostic thyroidectomy. Recently, the osteogenic protein, Enigma, has been associated with different cancer types, including thyroid cancer progression and calcification through its interaction with bone morphogenic protein-1 (BMP-1), and tyrosine kinases linked to mitogenic signaling pathways [1, 2]. Our published data on Enigma protein analysis with immunohistochemistry showed promising results in discriminating between malignant versus benign thyroid nodules and demonstrated a correlation with thyroid cancer staging [3]. In this study, we are investigating Enigma at a gene expression level by real-time (RT-qPCR), which is a quantitative and more time-efficient method that requires smaller samples (FNA) than immunohistochemistry. We analyzed Enigma mRNA expression levels to determine if Enigma-qPCR could be used as a diagnostic tool to improve the accuracy of FNAB in both malignant and benign thyroid tissues. We extracted mRNA/DNA/proteins from fresh malignant and benign thyroid nodules using a QIAGEN DNA/RNA/Protein Kit. We ran isolated pure mRNA through Enigma-qPCR. The results showed that the Enigma-mRNA expression was 3-fold higher in malignant as compared to benign thyroid tissues. This finding supports our previous Enigma immunohistochemistry data and shows a relative quantitative difference in Enigma-mRNA expression level between malignant and benign thyroid nodules. We conclude that Enigma-RT-qPCR can be used to effectively determine malignancies in FNAB samples derived from thyroid nodules. This method could potentially enhance the diagnostic accuracy of indeterminate nodules and decrease diagnostic thyroidectomies and associated morbidity. [1] C.R. Jung, J.H. Lim, Y. Choi, D.G. Kim, K.J. Kang, S.M. Noh, D.S. Im, Enigma negatively regulates p53 through MDM2 and promotes tumor cell survival in mice, The Journal of clinical investigation 120(12) (2010) 4493-506. [2] Y.J. Kim, H.J. Hwang, J.G. Kang, C.S. Kim, S.H. Ihm, M.G. Choi, S.J. Lee, Enigma Plays Roles in Survival of Thyroid Carcinoma Cells through PI3K/AKT Signaling and Survivin, Anticancer research 38(6) (2018) 3515-3525. [3] A.A. Firek, M.C. Perez, A. Gonda, L. Lei, I. Munir, A.A. Simental, F.E. Carr, B.J. Becerra, M. De Leon, S. Khan, Pathologic significance of a novel oncoprotein in thyroid cancer progression, Head & neck 39(12) (2017) 2459-2469.

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