ArticleCancer informatics2010
Identification of predictive pathways for non-hodgkin lymphoma prognosis.
Article in Cancer informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Impact of DNA polymorphisms in key DNA base excision repair proteins on cancer risk.Human & experimental toxicology · 2012Pooled it
- Integrative analysis of prognosis data on multiple cancer subtypes.Biometrics · 2014Article
- Stratified pathway analysis to identify gene sets associated with oral contraceptive use and breast cancer.Cancer informatics · 2014Article
- A discovery study of daunorubicin induced cardiotoxicity in a sample of acute myeloid leukemia patients prioritizes P450 oxidoreductase polymorphisms as a potential risk factor.Frontiers in genetics · 2013Article
- Integrative analysis of cancer prognosis data with multiple subtypes using regularized gradient descent.Genetic epidemiology · 2012Article
- Adjusting confounders in ranking biomarkers: a model-based ROC approach.Briefings in bioinformatics · 2012Article
- The link between genetic polymorphism of glutathione-S-transferases, GSTM1, and GSTT1 and diffuse large B-cell lymphoma in Egypt.Journal of cancer research and clinical oncology · 2012Article
- Risk Factors of Follicular Lymphoma.Expert opinion on medical diagnostics · 2012Article
- Risk Factors of Non-Hodgkin Lymphoma.Expert opinion on medical diagnostics · 2011Article
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Authors and funding
10 authors.
Funding
Abstract
Despite decades of intensive research, NHL (non-Hodgkin lymphoma) still remains poorly understood and is largely incurable. Recent molecular studies suggest that genomic variants measured with SNPs (single nucleotide polymorphisms) in genes may have additional predictive power for NHL prognosis beyond clinical risk factors. We analyzed a genetic association study. The prognostic cohort consisted of 346 patients, among whom 138 had DLBCL (diffuse large B-cell lymphoma) and 101 had FL ( follicular lymphoma). For DLBCL, we analyzed 1229 SNPs which represented 122 KEGG pathways. For FL, we analyzed 1228 SNPs which represented 122 KEGG pathways. Unlike in existing studies, we targeted at identifying pathways with significant additional predictive power beyond clinical factors. In addition, we accounted for the joint effects of multiple SNPs within pathways, whereas some existing studies drew pathway-level conclusions based on separate analysis of individual SNPs. For DLBCL, we identified four pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 2.535, 2.220, 2.094, 2.453, and 2.512, respectively. As a comparison, the clinical factors had a median of the prediction logrank statistics around 0.552. For FL, we identified two pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 4.320 and 3.532, respectively. As a comparison, the clinical factors had a median of the prediction logrank statistics around 1.212. For NHL overall, we identified three pathways, which, combined with the clinical factors, had medians of the prediction logrank statistics as 5.722, 5.314, and 5.441, respective. As a comparison, the clinical factors had a median of the prediction logrank statistics around 4.411. The identified pathways have sound biological bases. In addition, they are different from those identified using existing approaches. They may provide further insights into the biological mechanisms underlying the prognosis of NHL.
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