Project description:Purpose: Follicular lymphoma is a common lymphoma of adults. Although its course is often indolent, a substantial proportion of patients have a poor prognosis, often due to rapid progression or transformation to a more aggressive lymphoma. Currently available clinical prognostic scores, such as the follicular lymphoma international prognostic index, are not able to optimally predict transformation or poor outcome. Experimental Design: Gene expression profiling was done on primary lymphoma biopsy samples. Results: Using a statistically conservative approach, predictive interaction analysis, we have identified pairs of interacting genes that predict poor outcome, measured as death within 5 years of diagnosis. The best gene pair performs >1,000-fold better than any single gene or the follicular lymphoma international prognostic index in our data set. Many gene pairs achieve outcome prediction accuracies exceeding 85% in extensive cross-validation and noise sensitivity computational analyses.Many genes repeatedly appear in top-ranking pairs, suggesting that they reproducibly provide predictive capability. Conclusions:The evidence reported here may provide the basis for an expression-based, multigene test for predicting poor follicular lymphoma outcomes. Keywords: Comparative genomics

2008-10-15 | GSE13029 | GEO

NMR-based metabolomic profiling can differentiate follicular lymphoma from benign lymph node tissues and may be predictive of outcome.

Project description: Not available

| S-EPMC9117304 | biostudies-literature

Baseline SUVmax is related to tumor cell proliferation and patient outcome in follicular lymphoma.

Project description: Not available

| S-EPMC8719066 | biostudies-literature

Treatment dependence of prognostic gene expression signatures in de novo follicular lymphoma.

Project description: Not available

| S-EPMC8120142 | biostudies-literature

Gene Expression Signature Predictive of Neuroendocrine Transformation in Prostate Adenocarcinoma.

Project description: Not available