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Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors
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| Zeitschriftentitel: | Cancer Research |
|---|---|
| Personen und Körperschaften: | , , , , , , , , , |
| In: | Cancer Research, 64, 2004, 8, S. 2898-2903 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
American Association for Cancer Research (AACR)
|
| Schlagwörter: |
| author_facet |
Mazzanti, Chiara Zeiger, Martha A. Costourous, Nick Umbricht, Christopher Westra, William H Smith, Danelle Somervell, Helina Bevilacqua, Generoso Alexander, H. Richard Libutti, Steven K. Mazzanti, Chiara Zeiger, Martha A. Costourous, Nick Umbricht, Christopher Westra, William H Smith, Danelle Somervell, Helina Bevilacqua, Generoso Alexander, H. Richard Libutti, Steven K. |
|---|---|
| author |
Mazzanti, Chiara Zeiger, Martha A. Costourous, Nick Umbricht, Christopher Westra, William H Smith, Danelle Somervell, Helina Bevilacqua, Generoso Alexander, H. Richard Libutti, Steven K. |
| spellingShingle |
Mazzanti, Chiara Zeiger, Martha A. Costourous, Nick Umbricht, Christopher Westra, William H Smith, Danelle Somervell, Helina Bevilacqua, Generoso Alexander, H. Richard Libutti, Steven K. Cancer Research Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors Cancer Research Oncology |
| author_sort |
mazzanti, chiara |
| spelling |
Mazzanti, Chiara Zeiger, Martha A. Costourous, Nick Umbricht, Christopher Westra, William H Smith, Danelle Somervell, Helina Bevilacqua, Generoso Alexander, H. Richard Libutti, Steven K. 0008-5472 1538-7445 American Association for Cancer Research (AACR) Cancer Research Oncology http://dx.doi.org/10.1158/0008-5472.can-03-3811 <jats:title>Abstract</jats:title> <jats:p>DNA microarrays allow quick and complete evaluation of a cell’s transcriptional activity. Expression genomics is very powerful in that it can generate expression data for a large number of genes simultaneously across multiple samples. In cancer research, an intriguing application of expression arrays includes assessing the molecular components of the neoplastic process and utilizing the data for cancer classification (Miller LD, et al. Cancer Cell 2002;2:353–61). Classification of human cancers into distinct groups based on their molecular profile rather than their histological appearance may prove to be more relevant to specific cancer diagnoses and cancer treatment regimes. Several attempts to formulate a consensus about classification and treatment of thyroid carcinoma based on standard histopathological analysis have resulted in published guidelines for diagnosis and initial disease management (Sherman SI. Lancet 2003;361:501–11). In the past few decades, no improvement has been made in the differential diagnosis of thyroid tumors by fine needle aspiration biopsy, specifically suspicious or indeterminate thyroid lesions, suggesting that a new approach to this should be explored. Therefore, in this study, we developed a gene expression approach to diagnose benign versus malignant thyroid lesions in 73 patients with thyroid tumors. We successfully built a 10 and 6 gene model able to differentiate benign versus malignant thyroid tumors. Our results support the premise that a molecular classification system for thyroid tumors is possible, and this in turn may provide a more accurate diagnostic tool for the clinician managing patients with suspicious thyroid lesions.</jats:p> Using Gene Expression Profiling to Differentiate Benign <b> <i>versus</i> </b> Malignant Thyroid Tumors Cancer Research |
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| title |
Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_unstemmed |
Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_full |
Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_fullStr |
Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_full_unstemmed |
Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_short |
Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_sort |
using gene expression profiling to differentiate benign <b>
<i>versus</i>
</b> malignant thyroid tumors |
| topic |
Cancer Research Oncology |
| url |
http://dx.doi.org/10.1158/0008-5472.can-03-3811 |
| publishDate |
2004 |
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2898-2903 |
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<jats:title>Abstract</jats:title>
<jats:p>DNA microarrays allow quick and complete evaluation of a cell’s transcriptional activity. Expression genomics is very powerful in that it can generate expression data for a large number of genes simultaneously across multiple samples. In cancer research, an intriguing application of expression arrays includes assessing the molecular components of the neoplastic process and utilizing the data for cancer classification (Miller LD, et al. Cancer Cell 2002;2:353–61). Classification of human cancers into distinct groups based on their molecular profile rather than their histological appearance may prove to be more relevant to specific cancer diagnoses and cancer treatment regimes. Several attempts to formulate a consensus about classification and treatment of thyroid carcinoma based on standard histopathological analysis have resulted in published guidelines for diagnosis and initial disease management (Sherman SI. Lancet 2003;361:501–11). In the past few decades, no improvement has been made in the differential diagnosis of thyroid tumors by fine needle aspiration biopsy, specifically suspicious or indeterminate thyroid lesions, suggesting that a new approach to this should be explored. Therefore, in this study, we developed a gene expression approach to diagnose benign versus malignant thyroid lesions in 73 patients with thyroid tumors. We successfully built a 10 and 6 gene model able to differentiate benign versus malignant thyroid tumors. Our results support the premise that a molecular classification system for thyroid tumors is possible, and this in turn may provide a more accurate diagnostic tool for the clinician managing patients with suspicious thyroid lesions.</jats:p> |
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| author | Mazzanti, Chiara, Zeiger, Martha A., Costourous, Nick, Umbricht, Christopher, Westra, William H, Smith, Danelle, Somervell, Helina, Bevilacqua, Generoso, Alexander, H. Richard, Libutti, Steven K. |
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| description | <jats:title>Abstract</jats:title> <jats:p>DNA microarrays allow quick and complete evaluation of a cell’s transcriptional activity. Expression genomics is very powerful in that it can generate expression data for a large number of genes simultaneously across multiple samples. In cancer research, an intriguing application of expression arrays includes assessing the molecular components of the neoplastic process and utilizing the data for cancer classification (Miller LD, et al. Cancer Cell 2002;2:353–61). Classification of human cancers into distinct groups based on their molecular profile rather than their histological appearance may prove to be more relevant to specific cancer diagnoses and cancer treatment regimes. Several attempts to formulate a consensus about classification and treatment of thyroid carcinoma based on standard histopathological analysis have resulted in published guidelines for diagnosis and initial disease management (Sherman SI. Lancet 2003;361:501–11). In the past few decades, no improvement has been made in the differential diagnosis of thyroid tumors by fine needle aspiration biopsy, specifically suspicious or indeterminate thyroid lesions, suggesting that a new approach to this should be explored. Therefore, in this study, we developed a gene expression approach to diagnose benign versus malignant thyroid lesions in 73 patients with thyroid tumors. We successfully built a 10 and 6 gene model able to differentiate benign versus malignant thyroid tumors. Our results support the premise that a molecular classification system for thyroid tumors is possible, and this in turn may provide a more accurate diagnostic tool for the clinician managing patients with suspicious thyroid lesions.</jats:p> |
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| spelling | Mazzanti, Chiara Zeiger, Martha A. Costourous, Nick Umbricht, Christopher Westra, William H Smith, Danelle Somervell, Helina Bevilacqua, Generoso Alexander, H. Richard Libutti, Steven K. 0008-5472 1538-7445 American Association for Cancer Research (AACR) Cancer Research Oncology http://dx.doi.org/10.1158/0008-5472.can-03-3811 <jats:title>Abstract</jats:title> <jats:p>DNA microarrays allow quick and complete evaluation of a cell’s transcriptional activity. Expression genomics is very powerful in that it can generate expression data for a large number of genes simultaneously across multiple samples. In cancer research, an intriguing application of expression arrays includes assessing the molecular components of the neoplastic process and utilizing the data for cancer classification (Miller LD, et al. Cancer Cell 2002;2:353–61). Classification of human cancers into distinct groups based on their molecular profile rather than their histological appearance may prove to be more relevant to specific cancer diagnoses and cancer treatment regimes. Several attempts to formulate a consensus about classification and treatment of thyroid carcinoma based on standard histopathological analysis have resulted in published guidelines for diagnosis and initial disease management (Sherman SI. Lancet 2003;361:501–11). In the past few decades, no improvement has been made in the differential diagnosis of thyroid tumors by fine needle aspiration biopsy, specifically suspicious or indeterminate thyroid lesions, suggesting that a new approach to this should be explored. Therefore, in this study, we developed a gene expression approach to diagnose benign versus malignant thyroid lesions in 73 patients with thyroid tumors. We successfully built a 10 and 6 gene model able to differentiate benign versus malignant thyroid tumors. Our results support the premise that a molecular classification system for thyroid tumors is possible, and this in turn may provide a more accurate diagnostic tool for the clinician managing patients with suspicious thyroid lesions.</jats:p> Using Gene Expression Profiling to Differentiate Benign <b> <i>versus</i> </b> Malignant Thyroid Tumors Cancer Research |
| spellingShingle | Mazzanti, Chiara, Zeiger, Martha A., Costourous, Nick, Umbricht, Christopher, Westra, William H, Smith, Danelle, Somervell, Helina, Bevilacqua, Generoso, Alexander, H. Richard, Libutti, Steven K., Cancer Research, Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors, Cancer Research, Oncology |
| title | Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_full | Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_fullStr | Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_full_unstemmed | Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_short | Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| title_sort | using gene expression profiling to differentiate benign <b> <i>versus</i> </b> malignant thyroid tumors |
| title_unstemmed | Using Gene Expression Profiling to Differentiate Benign versus Malignant Thyroid Tumors |
| topic | Cancer Research, Oncology |
| url | http://dx.doi.org/10.1158/0008-5472.can-03-3811 |