Modelling gene expression profiles related to prostate tumor progression using binary states
Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expressio...
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| Autor*in: |
Martinez, Emmanuel [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013 |
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| Schlagwörter: |
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| Anmerkung: |
© Martinez and Trevino; licensee BioMed Central Ltd. 2013 |
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| Übergeordnetes Werk: |
Enthalten in: Theoretical biology and medical modelling - London : BioMed Central, 2004, 10(2013), 1 vom: 31. Mai |
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| Übergeordnetes Werk: |
volume:10 ; year:2013 ; number:1 ; day:31 ; month:05 |
| Links: |
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| DOI / URN: |
10.1186/1742-4682-10-37 |
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SPR029129230 |
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| 520 | |a Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression. Methods We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets. Results We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings. Conclusions Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies. | ||
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10.1186/1742-4682-10-37 doi (DE-627)SPR029129230 (SPR)1742-4682-10-37-e DE-627 ger DE-627 rakwb eng Martinez, Emmanuel verfasserin aut Modelling gene expression profiles related to prostate tumor progression using binary states 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Martinez and Trevino; licensee BioMed Central Ltd. 2013 Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression. Methods We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets. Results We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings. Conclusions Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies. Tumor Progression (dpeaa)DE-He213 Synthetic Dataset (dpeaa)DE-He213 Malignancy Progression (dpeaa)DE-He213 Prostatic Intraepithelial Neoplasia (dpeaa)DE-He213 Binary State (dpeaa)DE-He213 Trevino, Victor aut Enthalten in Theoretical biology and medical modelling London : BioMed Central, 2004 10(2013), 1 vom: 31. Mai (DE-627)39178482X (DE-600)2156462-0 1742-4682 nnns volume:10 year:2013 number:1 day:31 month:05 https://dx.doi.org/10.1186/1742-4682-10-37 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2013 1 31 05 |
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10.1186/1742-4682-10-37 doi (DE-627)SPR029129230 (SPR)1742-4682-10-37-e DE-627 ger DE-627 rakwb eng Martinez, Emmanuel verfasserin aut Modelling gene expression profiles related to prostate tumor progression using binary states 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Martinez and Trevino; licensee BioMed Central Ltd. 2013 Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression. Methods We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets. Results We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings. Conclusions Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies. Tumor Progression (dpeaa)DE-He213 Synthetic Dataset (dpeaa)DE-He213 Malignancy Progression (dpeaa)DE-He213 Prostatic Intraepithelial Neoplasia (dpeaa)DE-He213 Binary State (dpeaa)DE-He213 Trevino, Victor aut Enthalten in Theoretical biology and medical modelling London : BioMed Central, 2004 10(2013), 1 vom: 31. Mai (DE-627)39178482X (DE-600)2156462-0 1742-4682 nnns volume:10 year:2013 number:1 day:31 month:05 https://dx.doi.org/10.1186/1742-4682-10-37 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2013 1 31 05 |
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10.1186/1742-4682-10-37 doi (DE-627)SPR029129230 (SPR)1742-4682-10-37-e DE-627 ger DE-627 rakwb eng Martinez, Emmanuel verfasserin aut Modelling gene expression profiles related to prostate tumor progression using binary states 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Martinez and Trevino; licensee BioMed Central Ltd. 2013 Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression. Methods We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets. Results We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings. Conclusions Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies. Tumor Progression (dpeaa)DE-He213 Synthetic Dataset (dpeaa)DE-He213 Malignancy Progression (dpeaa)DE-He213 Prostatic Intraepithelial Neoplasia (dpeaa)DE-He213 Binary State (dpeaa)DE-He213 Trevino, Victor aut Enthalten in Theoretical biology and medical modelling London : BioMed Central, 2004 10(2013), 1 vom: 31. Mai (DE-627)39178482X (DE-600)2156462-0 1742-4682 nnns volume:10 year:2013 number:1 day:31 month:05 https://dx.doi.org/10.1186/1742-4682-10-37 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2013 1 31 05 |
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10.1186/1742-4682-10-37 doi (DE-627)SPR029129230 (SPR)1742-4682-10-37-e DE-627 ger DE-627 rakwb eng Martinez, Emmanuel verfasserin aut Modelling gene expression profiles related to prostate tumor progression using binary states 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Martinez and Trevino; licensee BioMed Central Ltd. 2013 Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression. Methods We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets. Results We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings. Conclusions Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies. Tumor Progression (dpeaa)DE-He213 Synthetic Dataset (dpeaa)DE-He213 Malignancy Progression (dpeaa)DE-He213 Prostatic Intraepithelial Neoplasia (dpeaa)DE-He213 Binary State (dpeaa)DE-He213 Trevino, Victor aut Enthalten in Theoretical biology and medical modelling London : BioMed Central, 2004 10(2013), 1 vom: 31. Mai (DE-627)39178482X (DE-600)2156462-0 1742-4682 nnns volume:10 year:2013 number:1 day:31 month:05 https://dx.doi.org/10.1186/1742-4682-10-37 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2013 1 31 05 |
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10.1186/1742-4682-10-37 doi (DE-627)SPR029129230 (SPR)1742-4682-10-37-e DE-627 ger DE-627 rakwb eng Martinez, Emmanuel verfasserin aut Modelling gene expression profiles related to prostate tumor progression using binary states 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Martinez and Trevino; licensee BioMed Central Ltd. 2013 Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression. Methods We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets. Results We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings. Conclusions Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies. Tumor Progression (dpeaa)DE-He213 Synthetic Dataset (dpeaa)DE-He213 Malignancy Progression (dpeaa)DE-He213 Prostatic Intraepithelial Neoplasia (dpeaa)DE-He213 Binary State (dpeaa)DE-He213 Trevino, Victor aut Enthalten in Theoretical biology and medical modelling London : BioMed Central, 2004 10(2013), 1 vom: 31. Mai (DE-627)39178482X (DE-600)2156462-0 1742-4682 nnns volume:10 year:2013 number:1 day:31 month:05 https://dx.doi.org/10.1186/1742-4682-10-37 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2013 1 31 05 |
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modelling gene expression profiles related to prostate tumor progression using binary states |
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Modelling gene expression profiles related to prostate tumor progression using binary states |
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Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression. Methods We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets. Results We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings. Conclusions Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies. © Martinez and Trevino; licensee BioMed Central Ltd. 2013 |
| abstractGer |
Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression. Methods We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets. Results We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings. Conclusions Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies. © Martinez and Trevino; licensee BioMed Central Ltd. 2013 |
| abstract_unstemmed |
Background Cancer is a complex disease commonly characterized by the disrupted activity of several cancer-related genes such as oncogenes and tumor-suppressor genes. Previous studies suggest that the process of tumor progression to malignancy is dynamic and can be traced by changes in gene expression. Despite the enormous efforts made for differential expression detection and biomarker discovery, few methods have been designed to model the gene expression level to tumor stage during malignancy progression. Such models could help us understand the dynamics and simplify or reveal the complexity of tumor progression. Methods We have modeled an on-off state of gene activation per sample then per stage to select gene expression profiles associated to tumor progression. The selection is guided by statistical significance of profiles based on random permutated datasets. Results We show that our method identifies expected profiles corresponding to oncogenes and tumor suppressor genes in a prostate tumor progression dataset. Comparisons with other methods support our findings and indicate that a considerable proportion of significant profiles is not found by other statistical tests commonly used to detect differential expression between tumor stages nor found by other tailored methods. Ontology and pathway analysis concurred with these findings. Conclusions Results suggest that our methodology may be a valuable tool to study tumor malignancy progression, which might reveal novel cancer therapies. © Martinez and Trevino; licensee BioMed Central Ltd. 2013 |
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| score |
7.399292 |