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| 001 | 166141 | ||
| 005 | 20240229123220.0 | ||
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| 100 | 1 | _ | |a Gao, Xin |0 P:(DE-He78)8218df9f6f41792399cd3a29b587e4e7 |b 0 |e First author |
| 245 | _ | _ | |a Whole blood DNA methylation aging markers predict colorectal cancer survival: a prospective cohort study. |
| 260 | _ | _ | |a [S.l.] |c 2020 |b BioMed Central |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a Blood DNA methylation-based aging algorithms predict mortality in the general population. We investigated the prognostic value of five established DNA methylation aging algorithms for patients with colorectal cancer (CRC).AgeAccelHorvath, AgeAccelHannum, DNAmMRscore, AgeAccelPheno and AgeAccelGrim were constructed using whole blood epi-genomic data from 2206 CRC patients. After a median follow-up of 6.2 years, 1079 deaths were documented, including 596 from CRC. Associations of the aging algorithms with survival outcomes were evaluated using the Cox regression and survival curves. Harrell's C-statistics were computed to investigate predictive performance.Adjusted hazard ratios (95% confidence intervals) of all-cause mortality for patients in the third compared to the first tertile were 1.66 (1.32, 2.09) for the DNAmMRscore, 1.35 (1.14, 1.59) for AgeAccelPheno and 1.65 (1.37, 2.00) for AgeAccelGrim, even after adjustment for age, sex and stage. AgeAccelHorvath and AgeAccelHannum were not associated with all-cause or CRC-specific mortality. In stage-specific analyses, associations were much stronger for patients with early or intermediate stage cancers (stages I, II and III) than for patients with metastatic (stage IV) cancers. Associations were weaker and less often statistically significant for CRC-specific mortality. Adding DNAmMRscore, AgeAccelPheno or AgeAccelGrim to models including age, sex and tumor stage improved predictive performance moderately.DNAmMRscore, AgeAccelPheno and AgeAccelGrim could serve as non-invasive CRC prognostic biomarkers independent of other commonly used markers. Further research should aim for tailoring and refining such algorithms for CRC patients and to explore their value for enhanced prediction of treatment success and treatment decisions. |
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| 700 | 1 | _ | |a Zhang, Yan |0 P:(DE-He78)6a8f87626cb610618a60d742677284cd |b 1 |
| 700 | 1 | _ | |a Boakye, Daniel |0 P:(DE-He78)657300dfd28903ec8149ca9bf5e7968d |b 2 |
| 700 | 1 | _ | |a Li, Xiangwei |0 P:(DE-He78)70ce269695a19b94f3f8b0bca12ec49b |b 3 |
| 700 | 1 | _ | |a Chang-Claude, Jenny |0 P:(DE-He78)c259d6cc99edf5c7bc7ce22c7f87c253 |b 4 |
| 700 | 1 | _ | |a Hoffmeister, Michael |0 P:(DE-He78)6c5d058b7552d071a7fa4c5e943fff0f |b 5 |
| 700 | 1 | _ | |a Brenner, Hermann |0 P:(DE-He78)90d5535ff896e70eed81f4a4f6f22ae2 |b 6 |e Last author |
| 773 | _ | _ | |a 10.1186/s13148-020-00977-4 |g Vol. 12, no. 1, p. 184 |0 PERI:(DE-600)2553921-8 |n 1 |p 184 |t Clinical epigenetics |v 12 |y 2020 |x 1868-7083 |
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