Predicting survival from microarray data—a comparative study

Bøvelstad, Hege; Nygård, Ståle; Størvold, Hege Leite; Aldrin, Magne; Borgan, Ørnulf; Frigessi, Arnoldo; Lingjærde, Ole Christian

doi:10.1093/bioinformatics/btm305

Cited by 266 publications

(309 citation statements)

References 23 publications

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“…1,33,35 To test whether the continuous actual eGFR is statistically related to the expression values of the ortholog geneset in glomerular and/or tubular interstitial compartments, we applied regression analysis using a ridge regression model and leave-one-out cross validation as previously described. 29,36 The glomerular expression dataset did not achieve statistically significant relationship results of the actual MDRD eGFR. In contrast, the tubular interstitial expression levels for 30 human orthologs assessed by 45 probesets provided a highly-significant relationship of continuous actual eGFR with a cross-validated R 2 of 0.53 (r ϭ 0.74, P Ͻ 0.001) ( Figure 3A).…”

Section: Development Of a Renal Gene Expression Signature Related To mentioning

confidence: 90%

Renal Gene and Protein Expression Signatures for Prediction of Kidney Disease Progression

Eichinger

Bitzer

et al. 2009

The American Journal of Pathology

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Section: Development Of a Renal Gene Expression Signature Related To mentioning

confidence: 90%

Renal Gene and Protein Expression Signatures for Prediction of Kidney Disease Progression

Eichinger

Bitzer

et al. 2009

The American Journal of Pathology

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“…Lasso regularization [52,53] was applied to identify CpGs for which methylation level predicted survival ( cv.glmnet function in the R package glmnet ) [54]. This approach gives a signature of targets that together capture the variation that is associated with survival of patients.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide DNA methylation profiles in progression to in situand invasive carcinoma of the breast with impact on gene transcription and prognosis

et al. 2014

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BackgroundDuctal carcinoma in situ (DCIS) of the breast is a precursor of invasive breast carcinoma. DNA methylation alterations are thought to be an early event in progression of cancer, and may prove valuable as a tool in clinical decision making and for understanding neoplastic development.ResultsWe generate genome-wide DNA methylation profiles of 285 breast tissue samples representing progression of cancer, and validate methylation changes between normal and DCIS in an independent dataset of 15 normal and 40 DCIS samples. We also validate a prognostic signature on 583 breast cancer samples from The Cancer Genome Atlas. Our analysis reveals that DNA methylation profiles of DCIS are radically altered compared to normal breast tissue, involving more than 5,000 genes. Changes between DCIS and invasive breast carcinoma involve around 1,000 genes. In tumors, DNA methylation is associated with gene expression of almost 3,000 genes, including both negative and positive correlations. A prognostic signature based on methylation level of 18 CpGs is associated with survival of breast cancer patients with invasive tumors, as well as with survival of patients with DCIS and mixed lesions of DCIS and invasive breast carcinoma.ConclusionsThis work demonstrates that changes in the epigenome occur early in the neoplastic progression, provides evidence for the possible utilization of DNA methylation-based markers of progression in the clinic, and highlights the importance of epigenetic changes in carcinogenesis.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-014-0435-x) contains supplementary material, which is available to authorized users.

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“…Reviews of these approaches can be found in Bøvelstad et al (2007) and Witten and Tibshirani (2010).…”

Section: Stringing and Functional Cox Regression For Predicting Survimentioning

confidence: 99%

Stringing High-Dimensional Data for Functional Analysis

Chen

Müller

et al. 2011

Journal of the American Statistical Association

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Predicting survival from microarray data—a comparative study

Abstract: Matlab and R code for the prediction methods are available at http://www.med.uio.no/imb/stat/bmms/software/microsurv/.

Cited by 266 publications

References 23 publications

Renal Gene and Protein Expression Signatures for Prediction of Kidney Disease Progression

Renal Gene and Protein Expression Signatures for Prediction of Kidney Disease Progression

Genome-wide DNA methylation profiles in progression to in situand invasive carcinoma of the breast with impact on gene transcription and prognosis

Stringing High-Dimensional Data for Functional Analysis

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