A molecular computational model improves the preoperative diagnosis of thyroid nodules

BMC Cancer

Sara Tomei¹  Ivo Marchetti²  Katia Zavaglia¹  Francesca Lessi¹  Alessandro Apollo¹  Paolo Aretini¹  Giancarlo Di Coscio²  Generoso Bevilacqua²  Chiara Mazzanti¹ 

[1] Division of Surgical, Molecular, and Ultrastructural Pathology, Section of Molecular Pathology, University of Pisa and Pisa University Hospital, Via Roma 57, Pisa, 56100, Italy

[2] Section of Cytopathology, University of Pisa and Pisa University Hospital, Via Roma 57, Pisa, 56100, Italy

关键词: Preoperative diagnosis;    Computational model;    Area under the curve (AUC);    Fine-needle aspiration (FNA);    Thyroid;   

Others  :  1128593
DOI  :  10.1186/1471-2407-12-396

 received in 2012-01-29, accepted in 2012-07-31,  发布年份 2012

【 摘 要 】

Background

Thyroid nodules with indeterminate cytological features on fine needle aspiration (FNA) cytology have a 20% risk of thyroid cancer. The aim of the current study was to determine the diagnostic utility of an 8-gene assay to distinguish benign from malignant thyroid neoplasm.

Methods

The mRNA expression level of 9 genes (KIT, SYNGR2, C21orf4, Hs.296031, DDI2, CDH1, LSM7, TC1, NATH) was analysed by quantitative PCR (q-PCR) in 93 FNA cytological samples. To evaluate the diagnostic utility of all the genes analysed, we assessed the area under the curve (AUC) for each gene individually and in combination. BRAF exon 15 status was determined by pyrosequencing. An 8-gene computational model (Neural Network Bayesian Classifier) was built and a multiple-variable analysis was then performed to assess the correlation between the markers.

Results

The AUC for each significant marker ranged between 0.625 and 0.900, thus all the significant markers, alone and in combination, can be used to distinguish between malignant and benign FNA samples. The classifier made up of KIT, CDH1, LSM7, C21orf4, DDI2, TC1, Hs.296031 and BRAF had a predictive power of 88.8%. It proved to be useful for risk stratification of the most critical cytological group of the indeterminate lesions for which there is the greatest need of accurate diagnostic markers.

Conclusion

The genetic classification obtained with this model is highly accurate at differentiating malignant from benign thyroid lesions and might be a useful adjunct in the preoperative management of patients with thyroid nodules.

【 授权许可】

   
2012 Tomei et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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【 参考文献 】

• [1]Mazzaferri EL: Thyroid cancer in thyroid nodules: finding a needle in the haystack. Am J Med 1992, 93(4):359-362.
• [2]Ross DS: Nonpalpable thyroid nodules–managing an epidemic. J Clin Endocrinol Metab 2002, 87(5):1938-1940.
• [3]Segev DL, Clark DP, Zeiger MA, Umbricht C: Beyond the suspicious thyroid fine needle aspirate. A review. Acta Cytol 2003, 47(5):709-722.
• [4]Baloch ZW, LiVolsi VA: The quest for a magic tumor marker: continuing saga in the diagnosis of the follicular lesions of thyroid. Am J Clin Pathol 2002, 118(2):165-166.
• [5]Shibru D, Hwang J, Khanafshar E, Duh QY, Clark OH, Kebebew E: Does the 3-gene diagnostic assay accurately distinguish benign from malignant thyroid neoplasms? Cancer 2008, 113(5):930-935.
• [6]Mazzanti C, Zeiger MA, Costouros NG, Umbricht C, Westra WH, Smith D, Somervell H, Bevilacqua G, Alexander HR, Libutti SK: Using gene expression profiling to differentiate benign versus malignant thyroid tumors. Cancer Res 2004, 64(8):2898-2903.
• [7]Tomei S, Mazzanti C, Marchetti I, Rossi L, Zavaglia K, Lessi F, Apollo A, Aretini P, Di Coscio G, Bevilacqua G: c-KIT receptor expression is strictly associated with the biological behaviour of thyroid nodules. J Transl Med 2012, 10(1):7. BioMed Central Full Text
• [8]Arnesen T, Betts MJ, Pendino F, Liberles DA, Anderson D, Caro J, Kong X, Varhaug JE, Lillehaug JR: Characterization of hARD2, a processed hARD1 gene duplicate, encoding a human protein N-alpha-acetyltransferase. BMC Biochem 2006, 7:13. BioMed Central Full Text
• [9]Chua EL, Young L, Wu WM, Turtle JR, Dong Q: Cloning of TC-1 (C8orf4), a novel gene found to be overexpressed in thyroid cancer. Genomics 2000, 69(3):342-347.
• [10]Fluge O, Bruland O, Akslen LA, Varhaug JE, Lillehaug JR: NATH, a novel gene overexpressed in papillary thyroid carcinomas. Oncogene 2002, 21(33):5056-5068.
• [11]Sargent DJ: Comparison of artificial neural networks with other statistical approaches: results from medical data sets. Cancer 2001, 91(8 Suppl):1636-1642.
• [12]Kedra D, Pan HQ, Seroussi E, Fransson I, Guilbaud C, Collins JE, Dunham I, Blennow E, Roe BA, Piehl F, et al.: Characterization of the human synaptogyrin gene family. Hum Genet 1998, 103(2):131-141.
• [13]Olesen C, Nyeng P, Kalisz M, Jensen TH, Moller M, Tommerup N, Byskov AG: Global gene expression analysis in fetal mouse ovaries with and without meiosis and comparison of selected genes with meiosis in the testis. Cell Tissue Res 2007, 328(1):207-221.
• [14]Tharun S, He W, Mayes AE, Lennertz P, Beggs JD, Parker R: Yeast Sm-like proteins function in mRNA decapping and decay. Nature 2000, 404(6777):515-518.
• [15]Conte N, Charafe-Jauffret E, Delaval B, Adelaide J, Ginestier C, Geneix J, Isnardon D, Jacquemier J, Birnbaum D: Carcinogenesis and translational controls: TACC1 is down-regulated in human cancers and associates with mRNA regulators. Oncogene 2002, 21(36):5619-5630.
• [16]Moldrich RX, Laine J, Visel A, Beart PM, Laffaire J, Rossier J, Potier MC: Transmembrane protein 50b (C21orf4), a candidate for Down syndrome neurophenotypes, encodes an intracellular membrane protein expressed in the rodent brain. Neuroscience 2008, 154(4):1255-1266.
• [17]Hardy RG, Vicente-Duenas C, Gonzalez-Herrero I, Anderson C, Flores T, Hughes S, Tselepis C, Ross JA, Sanchez-Garcia I: Snail family transcription factors are implicated in thyroid carcinogenesis. Am J Pathol 2007, 171(3):1037-1046.
• [18]Hoque MO, Rosenbaum E, Westra WH, Xing M, Ladenson P, Zeiger MA, Sidransky D, Umbricht CB: Quantitative assessment of promoter methylation profiles in thyroid neoplasms. J Clin Endocrinol Metab 2005, 90(7):4011-4018.
• [19]Sunde M, McGrath KC, Young L, Matthews JM, Chua EL, Mackay JP, Death AK: TC-1 is a novel tumorigenic and natively disordered protein associated with thyroid cancer. Cancer Res 2004, 64(8):2766-2773.
• [20]Ginzinger DG: Gene quantification using real-time quantitative PCR: an emerging technology hits the mainstream. Exp Hematol 2002, 30(6):503-512.
• [21]Ohlsson L, Hammarstrom ML, Israelsson A, Naslund L, Oberg A, Lindmark G, Hammarstrom S: Biomarker selection for detection of occult tumour cells in lymph nodes of colorectal cancer patients using real-time quantitative RT-PCR. Br J Cancer 2006, 95(2):218-225.
• [22]Schroder CP, Ruiters MH, de Jong S, Tiebosch AT, Wesseling J, Veenstra R, de Vries J, Hoekstra HJ, de Leij LF, de Vries EG: Detection of micrometastatic breast cancer by means of real time quantitative RT-PCR and immunostaining in perioperative blood samples and sentinel nodes. Int J Cancer 2003, 106(4):611-618.
• [23]Rosen J, He M, Umbricht C, Alexander HR, Dackiw AP, Zeiger MA, Libutti SK: A six-gene model for differentiating benign from malignant thyroid tumors on the basis of gene expression. Surgery 2005, 138(6):1050-1056. discussion 1056–1057
• [24]Baranwal S, Alahari SK: Molecular mechanisms controlling E-cadherin expression in breast cancer. Biochem Biophys Res Commun 2009, 384(1):6-11.
• [25]Cavallaro U, Christofori G: Cell adhesion and signalling by cadherins and Ig-CAMs in cancer. Nat Rev Cancer 2004, 4(2):118-132.
• [26]Schmalhofer O, Brabletz S, Brabletz T: E-cadherin, beta-catenin, and ZEB1 in malignant progression of cancer. Cancer Metastasis Rev 2009, 28(1–2):151-166.
• [27]Jung Y, Bang S, Choi K, Kim E, Kim Y, Kim J, Park J, Koo H, Moon RT, Song K, et al.: TC1 (C8orf4) enhances the Wnt/beta-catenin pathway by relieving antagonistic activity of Chibby. Cancer Res 2006, 66(2):723-728.
• [28]Rodvold KA, Pryka RD, Kuehl PG, Blum RA, Donahue P: Bayesian forecasting of serum gentamicin concentrations in intensive care patients. Clin Pharmacokinet 1990, 18(5):409-418.
• [29]Wakefield J, Racine-Poon A: An application of Bayesian population pharmacokinetic/pharmacodynamic models to dose recommendation. Stat Med 1995, 14(9–10):971-986.
• [30]Burnside ES, Rubin DL, Fine JP, Shachter RD, Sisney GA, Leung WK: Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience. Radiology 2006, 240(3):666-673.
• [31]Christiansen CL, Wang F, Barton MB, Kreuter W, Elmore JG, Gelfand AE, Fletcher SW: Predicting the cumulative risk of false-positive mammograms. J Natl Cancer Inst 2000, 92(20):1657-1666.
• [32]Edwards FH, Schaefer PS, Cohen AJ, Bellamy RF, Thompson L, Graeber GM, Barry MJ: Use of artificial intelligence for the preoperative diagnosis of pulmonary lesions. Ann Thorac Surg 1989, 48(4):556-559.
• [33]Burd RS, Ouyang M, Madigan D: Bayesian logistic injury severity score: a method for predicting mortality using international classification of disease-9 codes. Acad Emerg Med 2008, 15(5):466-475.
• [34]Fazio VW, Tekkis PP, Remzi F, Lavery IC: Assessment of operative risk in colorectal cancer surgery: the Cleveland Clinic Foundation colorectal cancer model. Dis Colon Rectum 2004, 47(12):2015-2024.
• [35]Biagioli B, Scolletta S, Cevenini G, Barbini E, Giomarelli P, Barbini P: A multivariate Bayesian model for assessing morbidity after coronary artery surgery. Crit Care 2006, 10(3):R94. BioMed Central Full Text
• [36]Edwards FH, Peterson RF, Bridges C, Ceithaml EL: 1988: use of a Bayesian statistical model for risk assessment in coronary artery surgery. Updated in 1995. Ann Thorac Surg 1995, 59(6):1611-1612.
• [37]Hoot N, Aronsky D: Using Bayesian networks to predict survival of liver transplant patients. AMIA Annu Symp Proc 2005, 2005:345-349.
• [38]Lenihan CR, O'Kelly P, Mohan P, Little D, Walshe JJ, Kieran NE, Conlon PJ: MDRD-estimated GFR at one year post-renal transplant is a predictor of long-term graft function. Ren Fail 2008, 30(4):345-352.