Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection
Background Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular marke...
Ausführliche Beschreibung
Autor*in: |
Zhang, Xiuming [verfasserIn] Bai, Yanfeng [verfasserIn] Xu, Lei [verfasserIn] Zhang, Buyi [verfasserIn] Feng, Shi [verfasserIn] Xu, Liming [verfasserIn] Zhang, Han [verfasserIn] Xu, Linjie [verfasserIn] Yang, Pengfei [verfasserIn] Niu, Tianye [verfasserIn] Zheng, Shusen [verfasserIn] Liu, Jimin [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Übergeordnetes Werk: |
Enthalten in: Hepatology international - New York, NY : Springer, 2007, 13(2019), 6 vom: 17. Sept., Seite 715-725 |
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Übergeordnetes Werk: |
volume:13 ; year:2019 ; number:6 ; day:17 ; month:09 ; pages:715-725 |
Links: |
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DOI / URN: |
10.1007/s12072-019-09978-9 |
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Katalog-ID: |
SPR024212210 |
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245 | 1 | 0 | |a Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection |
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520 | |a Background Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. Methods We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic and a recurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. Results The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. Conclusion This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. The developed classifiers stratified HCC patients into high- and low-probability survival or recurrent groups, which can help clinicians judge whether adjuvant therapy is beneficial post-operatively. | ||
650 | 4 | |a Hepatocellular carcinoma |7 (dpeaa)DE-He213 | |
650 | 4 | |a Prognosis |7 (dpeaa)DE-He213 | |
650 | 4 | |a Recurrence |7 (dpeaa)DE-He213 | |
650 | 4 | |a Predicting classifiers |7 (dpeaa)DE-He213 | |
650 | 4 | |a Immunomarkers |7 (dpeaa)DE-He213 | |
700 | 1 | |a Bai, Yanfeng |e verfasserin |4 aut | |
700 | 1 | |a Xu, Lei |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Buyi |e verfasserin |4 aut | |
700 | 1 | |a Feng, Shi |e verfasserin |4 aut | |
700 | 1 | |a Xu, Liming |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Han |e verfasserin |4 aut | |
700 | 1 | |a Xu, Linjie |e verfasserin |4 aut | |
700 | 1 | |a Yang, Pengfei |e verfasserin |4 aut | |
700 | 1 | |a Niu, Tianye |e verfasserin |4 aut | |
700 | 1 | |a Zheng, Shusen |e verfasserin |4 aut | |
700 | 1 | |a Liu, Jimin |e verfasserin |4 aut | |
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10.1007/s12072-019-09978-9 doi (DE-627)SPR024212210 (SPR)s12072-019-09978-9-e DE-627 ger DE-627 rakwb eng 610 ASE 44.87 bkl Zhang, Xiuming verfasserin aut Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. Methods We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic and a recurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. Results The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. Conclusion This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. The developed classifiers stratified HCC patients into high- and low-probability survival or recurrent groups, which can help clinicians judge whether adjuvant therapy is beneficial post-operatively. Hepatocellular carcinoma (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Recurrence (dpeaa)DE-He213 Predicting classifiers (dpeaa)DE-He213 Immunomarkers (dpeaa)DE-He213 Bai, Yanfeng verfasserin aut Xu, Lei verfasserin aut Zhang, Buyi verfasserin aut Feng, Shi verfasserin aut Xu, Liming verfasserin aut Zhang, Han verfasserin aut Xu, Linjie verfasserin aut Yang, Pengfei verfasserin aut Niu, Tianye verfasserin aut Zheng, Shusen verfasserin aut Liu, Jimin verfasserin aut Enthalten in Hepatology international New York, NY : Springer, 2007 13(2019), 6 vom: 17. Sept., Seite 715-725 (DE-627)524620733 (DE-600)2270316-0 1936-0541 nnns volume:13 year:2019 number:6 day:17 month:09 pages:715-725 https://dx.doi.org/10.1007/s12072-019-09978-9 lizenzpflichtig 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.87 ASE AR 13 2019 6 17 09 715-725 |
spelling |
10.1007/s12072-019-09978-9 doi (DE-627)SPR024212210 (SPR)s12072-019-09978-9-e DE-627 ger DE-627 rakwb eng 610 ASE 44.87 bkl Zhang, Xiuming verfasserin aut Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. Methods We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic and a recurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. Results The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. Conclusion This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. The developed classifiers stratified HCC patients into high- and low-probability survival or recurrent groups, which can help clinicians judge whether adjuvant therapy is beneficial post-operatively. Hepatocellular carcinoma (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Recurrence (dpeaa)DE-He213 Predicting classifiers (dpeaa)DE-He213 Immunomarkers (dpeaa)DE-He213 Bai, Yanfeng verfasserin aut Xu, Lei verfasserin aut Zhang, Buyi verfasserin aut Feng, Shi verfasserin aut Xu, Liming verfasserin aut Zhang, Han verfasserin aut Xu, Linjie verfasserin aut Yang, Pengfei verfasserin aut Niu, Tianye verfasserin aut Zheng, Shusen verfasserin aut Liu, Jimin verfasserin aut Enthalten in Hepatology international New York, NY : Springer, 2007 13(2019), 6 vom: 17. Sept., Seite 715-725 (DE-627)524620733 (DE-600)2270316-0 1936-0541 nnns volume:13 year:2019 number:6 day:17 month:09 pages:715-725 https://dx.doi.org/10.1007/s12072-019-09978-9 lizenzpflichtig 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.87 ASE AR 13 2019 6 17 09 715-725 |
allfields_unstemmed |
10.1007/s12072-019-09978-9 doi (DE-627)SPR024212210 (SPR)s12072-019-09978-9-e DE-627 ger DE-627 rakwb eng 610 ASE 44.87 bkl Zhang, Xiuming verfasserin aut Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. Methods We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic and a recurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. Results The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. Conclusion This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. The developed classifiers stratified HCC patients into high- and low-probability survival or recurrent groups, which can help clinicians judge whether adjuvant therapy is beneficial post-operatively. Hepatocellular carcinoma (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Recurrence (dpeaa)DE-He213 Predicting classifiers (dpeaa)DE-He213 Immunomarkers (dpeaa)DE-He213 Bai, Yanfeng verfasserin aut Xu, Lei verfasserin aut Zhang, Buyi verfasserin aut Feng, Shi verfasserin aut Xu, Liming verfasserin aut Zhang, Han verfasserin aut Xu, Linjie verfasserin aut Yang, Pengfei verfasserin aut Niu, Tianye verfasserin aut Zheng, Shusen verfasserin aut Liu, Jimin verfasserin aut Enthalten in Hepatology international New York, NY : Springer, 2007 13(2019), 6 vom: 17. Sept., Seite 715-725 (DE-627)524620733 (DE-600)2270316-0 1936-0541 nnns volume:13 year:2019 number:6 day:17 month:09 pages:715-725 https://dx.doi.org/10.1007/s12072-019-09978-9 lizenzpflichtig 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.87 ASE AR 13 2019 6 17 09 715-725 |
allfieldsGer |
10.1007/s12072-019-09978-9 doi (DE-627)SPR024212210 (SPR)s12072-019-09978-9-e DE-627 ger DE-627 rakwb eng 610 ASE 44.87 bkl Zhang, Xiuming verfasserin aut Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. Methods We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic and a recurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. Results The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. Conclusion This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. The developed classifiers stratified HCC patients into high- and low-probability survival or recurrent groups, which can help clinicians judge whether adjuvant therapy is beneficial post-operatively. Hepatocellular carcinoma (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Recurrence (dpeaa)DE-He213 Predicting classifiers (dpeaa)DE-He213 Immunomarkers (dpeaa)DE-He213 Bai, Yanfeng verfasserin aut Xu, Lei verfasserin aut Zhang, Buyi verfasserin aut Feng, Shi verfasserin aut Xu, Liming verfasserin aut Zhang, Han verfasserin aut Xu, Linjie verfasserin aut Yang, Pengfei verfasserin aut Niu, Tianye verfasserin aut Zheng, Shusen verfasserin aut Liu, Jimin verfasserin aut Enthalten in Hepatology international New York, NY : Springer, 2007 13(2019), 6 vom: 17. Sept., Seite 715-725 (DE-627)524620733 (DE-600)2270316-0 1936-0541 nnns volume:13 year:2019 number:6 day:17 month:09 pages:715-725 https://dx.doi.org/10.1007/s12072-019-09978-9 lizenzpflichtig 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.87 ASE AR 13 2019 6 17 09 715-725 |
allfieldsSound |
10.1007/s12072-019-09978-9 doi (DE-627)SPR024212210 (SPR)s12072-019-09978-9-e DE-627 ger DE-627 rakwb eng 610 ASE 44.87 bkl Zhang, Xiuming verfasserin aut Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. Methods We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic and a recurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. Results The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. Conclusion This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. The developed classifiers stratified HCC patients into high- and low-probability survival or recurrent groups, which can help clinicians judge whether adjuvant therapy is beneficial post-operatively. Hepatocellular carcinoma (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Recurrence (dpeaa)DE-He213 Predicting classifiers (dpeaa)DE-He213 Immunomarkers (dpeaa)DE-He213 Bai, Yanfeng verfasserin aut Xu, Lei verfasserin aut Zhang, Buyi verfasserin aut Feng, Shi verfasserin aut Xu, Liming verfasserin aut Zhang, Han verfasserin aut Xu, Linjie verfasserin aut Yang, Pengfei verfasserin aut Niu, Tianye verfasserin aut Zheng, Shusen verfasserin aut Liu, Jimin verfasserin aut Enthalten in Hepatology international New York, NY : Springer, 2007 13(2019), 6 vom: 17. Sept., Seite 715-725 (DE-627)524620733 (DE-600)2270316-0 1936-0541 nnns volume:13 year:2019 number:6 day:17 month:09 pages:715-725 https://dx.doi.org/10.1007/s12072-019-09978-9 lizenzpflichtig 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_32 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_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 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_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.87 ASE AR 13 2019 6 17 09 715-725 |
language |
English |
source |
Enthalten in Hepatology international 13(2019), 6 vom: 17. Sept., Seite 715-725 volume:13 year:2019 number:6 day:17 month:09 pages:715-725 |
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Enthalten in Hepatology international 13(2019), 6 vom: 17. Sept., Seite 715-725 volume:13 year:2019 number:6 day:17 month:09 pages:715-725 |
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findex.gbv.de |
topic_facet |
Hepatocellular carcinoma Prognosis Recurrence Predicting classifiers Immunomarkers |
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610 |
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false |
container_title |
Hepatology international |
authorswithroles_txt_mv |
Zhang, Xiuming @@aut@@ Bai, Yanfeng @@aut@@ Xu, Lei @@aut@@ Zhang, Buyi @@aut@@ Feng, Shi @@aut@@ Xu, Liming @@aut@@ Zhang, Han @@aut@@ Xu, Linjie @@aut@@ Yang, Pengfei @@aut@@ Niu, Tianye @@aut@@ Zheng, Shusen @@aut@@ Liu, Jimin @@aut@@ |
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2019-09-17T00:00:00Z |
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3610 |
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SPR024212210 |
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englisch |
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The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. Methods We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic and a recurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. Results The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. Conclusion This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. 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Zhang, Xiuming |
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Zhang, Xiuming ddc 610 bkl 44.87 misc Hepatocellular carcinoma misc Prognosis misc Recurrence misc Predicting classifiers misc Immunomarkers Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection |
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610 ASE 44.87 bkl Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection Hepatocellular carcinoma (dpeaa)DE-He213 Prognosis (dpeaa)DE-He213 Recurrence (dpeaa)DE-He213 Predicting classifiers (dpeaa)DE-He213 Immunomarkers (dpeaa)DE-He213 |
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ddc 610 bkl 44.87 misc Hepatocellular carcinoma misc Prognosis misc Recurrence misc Predicting classifiers misc Immunomarkers |
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Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection |
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Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection |
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clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection |
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Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection |
abstract |
Background Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. Methods We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic and a recurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. Results The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. Conclusion This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. The developed classifiers stratified HCC patients into high- and low-probability survival or recurrent groups, which can help clinicians judge whether adjuvant therapy is beneficial post-operatively. |
abstractGer |
Background Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. Methods We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic and a recurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. Results The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. Conclusion This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. The developed classifiers stratified HCC patients into high- and low-probability survival or recurrent groups, which can help clinicians judge whether adjuvant therapy is beneficial post-operatively. |
abstract_unstemmed |
Background Approximately 50% hepatocellular carcinoma (HCC) patients die within 5 year after surgical resection. The present staging systems do not fully allow to accurately predict the HCC prognosis and recurrence. This study aimed to identify clinicopathological characteristics and molecular markers to establish classifiers to predict the 5-year overall survival (OS) and the 3-year recurrence in HCC patients post-operatively. Methods We enrolled 647 HCC patients from two institutions, underwent surgical resection and divided the patients into one training and two validation cohorts. Clinicopathologic characteristics and tumor protein expression of 29 biomarkers by immunohistochemical (IHC) analysis were used to develop and validate a prognostic and a recurrent classifier, using the maximum relevance minimum redundancy algorithm jointly with the multivariable regression method. Results The prognostic classifier distinguished HCC patients into high- and low-probability survival groups with significant differences in 5-year OS rate in all three cohorts (training cohort: 57.36% vs. 22.97%; p < 0.0001; internal validation cohort: 61.90% vs. 28.85%; p < 0.0001; independent validation cohort: 64.28% vs. 22.45%; p < 0.0001). The recurrent classifier also demonstrated good discrimination in all three cohorts. Conclusion This study presented a prognostic classifier and a recurrent classifier using clinicopathologic and IHC characteristics. The developed classifiers stratified HCC patients into high- and low-probability survival or recurrent groups, which can help clinicians judge whether adjuvant therapy is beneficial post-operatively. |
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Clinical and morpho-molecular classifiers for prediction of hepatocellular carcinoma prognosis and recurrence after surgical resection |
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|
score |
7.402337 |