| Home > Publications database > Blood markers of oxidative stress are strongly associated with poorer prognosis in colorectal cancer patients > print |
| 001 | 163861 | ||
| 005 | 20240229123158.0 | ||
| 024 | 7 | _ | |a 10.1002/ijc.33018 |2 doi |
| 024 | 7 | _ | |a 0020-7136 |2 ISSN |
| 024 | 7 | _ | |a 1097-0215 |2 ISSN |
| 024 | 7 | _ | |a altmetric:80386284 |2 altmetric |
| 024 | 7 | _ | |a pmid:32319674 |2 pmid |
| 037 | _ | _ | |a DKFZ-2020-02095 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Boakye, Daniel |0 P:(DE-He78)657300dfd28903ec8149ca9bf5e7968d |b 0 |e First author |
| 245 | _ | _ | |a Blood markers of oxidative stress are strongly associated with poorer prognosis in colorectal cancer patients |
| 260 | _ | _ | |a Bognor Regis |c 2020 |b Wiley-Liss |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1608117481_20752 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a Int. J. Cancer. 2020;147:2373–2386#EA:C070#LA:C070# |
| 520 | _ | _ | |a Oxidative stress has been implicated in the initiation of several cancers, including colorectal cancer (CRC). Whether it also plays a role in CRC prognosis is unclear. We assessed the associations of two oxidative stress biomarkers (Diacron's reactive oxygen metabolites [d-ROMs] and total thiol level [TTL]) with CRC prognosis. CRC patients who were diagnosed in 2003 to 2012 and recruited into a population-based study in Germany (n = 3361) were followed for up to 6 years. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for the associations of d-ROMs and TTL (measured from blood samples collected shortly after CRC diagnosis) with overall survival (OS) and disease-specific survival (DSS) were estimated using multivariable Cox regression. Particularly pronounced associations of higher d-ROMs with lower survival were observed in stage IV patients, with patients in the highest (vs lowest) tertile having much lower OS (HR = 1.52, 95% CI = 1.14-2.04) and DSS (HR = 1.61, 95% CI = 1.20-2.17). For TTL, strong inverse associations of TTL with mortality were observed within all stages. In patients of all stages, those in the highest (vs lowest) quintile had substantially higher OS (HR = 0.48, 95% CI = 0.38-0.62) and DSS (HR = 0.52, 95% CI = 0.39-0.69). The addition of these biomarkers to models that included age, sex, tumor stage and subsite significantly improved the prediction of CRC prognosis. The observed strong associations of higher d-ROMs and lower TTL levels with poorer prognosis even in stage IV patients suggest that oxidative stress contributes significantly to premature mortality in CRC patients and demonstrate a large potential of these biomarkers in enhancing the prediction of CRC prognosis beyond tumor stage. |
| 536 | _ | _ | |a 313 - Cancer risk factors and prevention (POF3-313) |0 G:(DE-HGF)POF3-313 |c POF3-313 |f POF III |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Jansen, Lina |0 P:(DE-He78)bbfe0ebad1e3b608bca2b49d4f86bd09 |b 1 |
| 700 | 1 | _ | |a Schöttker, Ben |0 P:(DE-He78)c67a12496b8aac150c0eef888d808d46 |b 2 |
| 700 | 1 | _ | |a Jansen, Eugene H. J. M. |b 3 |
| 700 | 1 | _ | |a Schneider, Martin |0 P:(DE-He78)0d37cc734b95fed555f2244d6fee6320 |b 4 |
| 700 | 1 | _ | |a Halama, Niels |0 P:(DE-He78)0a4053be7ffd6aa9bef69de28753a601 |b 5 |
| 700 | 1 | _ | |a Gào, Xin |0 P:(DE-He78)8218df9f6f41792399cd3a29b587e4e7 |b 6 |
| 700 | 1 | _ | |a Chang‐Claude, Jenny |0 P:(DE-HGF)0 |b 7 |
| 700 | 1 | _ | |a Hoffmeister, Michael |0 P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f |b 8 |u dkfz |
| 700 | 1 | _ | |a Brenner, Hermann |0 P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2 |b 9 |e Last author |
| 773 | _ | _ | |a 10.1002/ijc.33018 |g Vol. 147, no. 9, p. 2373 - 2386 |0 PERI:(DE-600)1474822-8 |n 9 |p 2373 - 2386 |t International journal of cancer |v 147 |y 2020 |x 1097-0215 |
| 909 | C | O | |p VDB |o oai:inrepo02.dkfz.de:163861 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 0 |6 P:(DE-He78)657300dfd28903ec8149ca9bf5e7968d |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 1 |6 P:(DE-He78)bbfe0ebad1e3b608bca2b49d4f86bd09 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 2 |6 P:(DE-He78)c67a12496b8aac150c0eef888d808d46 |
| 910 | 1 | _ | |a External Institute |0 I:(DE-HGF)0 |k Extern |b 4 |6 P:(DE-He78)0d37cc734b95fed555f2244d6fee6320 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 5 |6 P:(DE-He78)0a4053be7ffd6aa9bef69de28753a601 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 6 |6 P:(DE-He78)8218df9f6f41792399cd3a29b587e4e7 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 7 |6 P:(DE-HGF)0 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 8 |6 P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 9 |6 P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2 |
| 913 | 1 | _ | |a DE-HGF |b Gesundheit |l Krebsforschung |1 G:(DE-HGF)POF3-310 |0 G:(DE-HGF)POF3-313 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-300 |4 G:(DE-HGF)POF |v Cancer risk factors and prevention |x 0 |
| 914 | 1 | _ | |y 2020 |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |d 2020-02-26 |w ger |
| 915 | _ | _ | |a DEAL Wiley |0 StatID:(DE-HGF)3001 |2 StatID |d 2020-02-26 |w ger |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2020-02-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2020-02-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0310 |2 StatID |b NCBI Molecular Biology Database |d 2020-02-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2020-02-26 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0110 |2 StatID |b Science Citation Index |d 2020-02-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2020-02-26 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |d 2020-02-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2020-02-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2020-02-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |d 2020-02-26 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2020-02-26 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b INT J CANCER : 2018 |d 2020-02-26 |
| 915 | _ | _ | |a IF < 5 |0 StatID:(DE-HGF)9900 |2 StatID |d 2020-02-26 |
| 920 | 1 | _ | |0 I:(DE-He78)D240-20160331 |k D240 |l Translationale Immuntherapie |x 0 |
| 920 | 1 | _ | |0 I:(DE-He78)C070-20160331 |k C070 |l C070 Klinische Epidemiologie und Alternf. |x 1 |
| 920 | 1 | _ | |0 I:(DE-He78)C120-20160331 |k C120 |l Präventive Onkologie |x 2 |
| 920 | 1 | _ | |0 I:(DE-He78)HD01-20160331 |k HD01 |l DKTK HD zentral |x 3 |
| 920 | 1 | _ | |0 I:(DE-He78)C020-20160331 |k C020 |l C020 Epidemiologie von Krebs |x 4 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)D240-20160331 |
| 980 | _ | _ | |a I:(DE-He78)C070-20160331 |
| 980 | _ | _ | |a I:(DE-He78)C120-20160331 |
| 980 | _ | _ | |a I:(DE-He78)HD01-20160331 |
| 980 | _ | _ | |a I:(DE-He78)C020-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|