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Search: WFRF:(Ljungberg Börje 1949 ) > Journal article > Combining epigeneti...

Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma

Andersson-Evelönn, Emma, 1983- (author)
Umeå universitet,Patologi
Vidman, Linda (author)
Umeå universitet,Institutionen för matematik och matematisk statistik
Källberg, David, 1982- (author)
Umeå universitet,Institutionen för matematik och matematisk statistik,Statistik
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Landfors, Mattias, 1977- (author)
Umeå universitet,Patologi
Liu, Xijia (author)
Umeå universitet,Institutionen för matematik och matematisk statistik
Ljungberg, Börje, Professor, 1949- (author)
Umeå universitet,Urologi och andrologi
Hultdin, Magnus (author)
Umeå universitet,Patologi
Rydén, Patrik (author)
Umeå universitet,Institutionen för matematik och matematisk statistik
Degerman, Sofie, 1977- (author)
Umeå universitet,Patologi,Institutionen för klinisk mikrobiologi
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 (creator_code:org_t)
2020-11-13
2020
English.
In: Journal of Translational Medicine. - : Springer Science and Business Media LLC. - 1479-5876 .- 1479-5876. ; 18:1
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • Background: Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables.Methods: A novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression.Results: The “triple classifier” which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress (pCIP5yr) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis.Conclusions: The triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Urologi och njurmedicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Urology and Nephrology (hsv//eng)

Keyword

Clear cell renal cell carcinoma
Classification
DNA methylation
Prognosis
Directed cluster analysis

Publication and Content Type

ref (subject category)
art (subject category)

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