Dataset Information

Usefulness of the top-scoring pairs of genes for prediction of prostate cancer progression.

ABSTRACT: Prediction of cancer progression after radical prostatectomy is one of the most challenging problems in the management of prostate cancer. Gene-expression profiling is widely used to identify genes associated with such progression. Usually candidate genes are identified according to a gene-by-gene comparison of expression. Recent reports suggested that relative expression of a gene pair more efficiently predicts cancer progression than single-gene analysis does. The top-scoring pair (TSP) algorithm classifies phenotypes according to the relative expression of a pair of genes. We applied the TSP approach to predict, which patients would experience systemic tumor progression after radical prostatectomy. Relative expression of TPD52L2/SQLE and CEACAM1/BRCA1 gene pairs identified those patients with more than 99% specificity but relatively low sensitivity (approximately 10%). These two gene pairs were validated in three independent data sets. In addition, combining two pairs of genes improved sensitivity without compromising specificity. Functional annotation of the TSP genes showed that they cluster by a limited number of biological functions and pathways, suggesting that relatively lower expression of genes from specific pathways can predict cancer progression. In conclusion, comparative analysis of the expression of two genes may be a simple and effective classifier for prediction of prostate cancer progression. In summary, the TSP approach can be used to identify patients whose prostate cancer will progress after they undergo radical prostatectomy. Two gene pairs can predict which men would experience progression to the metastatic form of the disease. However, because our analysis was based on a relatively small number of genes, a larger study will be needed to identify the best predictors of disease outcome overall.

SUBMITTER: Zhao H

PROVIDER: S-EPMC4136464 | biostudies-literature | 2010 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Usefulness of the top-scoring pairs of genes for prediction of prostate cancer progression.

Zhao H H Logothetis C J CJ Gorlov I P IP

Prostate cancer and prostatic diseases 20100413 3

Prediction of cancer progression after radical prostatectomy is one of the most challenging problems in the management of prostate cancer. Gene-expression profiling is widely used to identify genes associated with such progression. Usually candidate genes are identified according to a gene-by-gene comparison of expression. Recent reports suggested that relative expression of a gene pair more efficiently predicts cancer progression than single-gene analysis does. The top-scoring pair (TSP) algori ...[more]

PMID: 20386565

Similar Datasets

Project description:BackgroundThe widely used k top scoring pair (k-TSP) algorithm is a simple yet powerful parameter-free classifier. It owes its success in many cancer microarray datasets to an effective feature selection algorithm that is based on relative expression ordering of gene pairs. However, its general robustness does not extend to some difficult datasets, such as those involving cancer outcome prediction, which may be due to the relatively simple voting scheme used by the classifier. We believe that the performance can be enhanced by separating its effective feature selection component and combining it with a powerful classifier such as the support vector machine (SVM). More generally the top scoring pairs generated by the k-TSP ranking algorithm can be used as a dimensionally reduced subspace for other machine learning classifiers.ResultsWe developed an approach integrating the k-TSP ranking algorithm (TSP) with other machine learning methods, allowing combination of the computationally efficient, multivariate feature ranking of k-TSP with multivariate classifiers such as SVM. We evaluated this hybrid scheme (k-TSP+SVM) in a range of simulated datasets with known data structures. As compared with other feature selection methods, such as a univariate method similar to Fisher's discriminant criterion (Fisher), or a recursive feature elimination embedded in SVM (RFE), TSP is increasingly more effective than the other two methods as the informative genes become progressively more correlated, which is demonstrated both in terms of the classification performance and the ability to recover true informative genes. We also applied this hybrid scheme to four cancer prognosis datasets, in which k-TSP+SVM outperforms k-TSP classifier in all datasets, and achieves either comparable or superior performance to that using SVM alone. In concurrence with what is observed in simulation, TSP appears to be a better feature selector than Fisher and RFE in some of the cancer datasetsConclusionsThe k-TSP ranking algorithm can be used as a computationally efficient, multivariate filter method for feature selection in machine learning. SVM in combination with k-TSP ranking algorithm outperforms k-TSP and SVM alone in simulated datasets and in some cancer prognosis datasets. Simulation studies suggest that as a feature selector, it is better tuned to certain data characteristics, i.e. correlations among informative genes, which is potentially interesting as an alternative feature ranking method in pathway analysis.

Project description:MOTIVATION:Supervised machine learning is widely applied to transcriptomic data to predict disease diagnosis, prognosis or survival. Robust and interpretable classifiers with high accuracy are usually favored for their clinical and translational potential. The top scoring pair (TSP) algorithm is an example that applies a simple rank-based algorithm to identify rank-altered gene pairs for classifier construction. Although many classification methods perform well in cross-validation of single expression profile, the performance usually greatly reduces in cross-study validation (i.e. the prediction model is established in the training study and applied to an independent test study) for all machine learning methods, including TSP. The failure of cross-study validation has largely diminished the potential translational and clinical values of the models. The purpose of this article is to develop a meta-analytic top scoring pair (MetaKTSP) framework that combines multiple transcriptomic studies and generates a robust prediction model applicable to independent test studies. RESULTS:We proposed two frameworks, by averaging TSP scores or by combining P-values from individual studies, to select the top gene pairs for model construction. We applied the proposed methods in simulated data sets and three large-scale real applications in breast cancer, idiopathic pulmonary fibrosis and pan-cancer methylation. The result showed superior performance of cross-study validation accuracy and biomarker selection for the new meta-analytic framework. In conclusion, combining multiple omics data sets in the public domain increases robustness and accuracy of the classification model that will ultimately improve disease understanding and clinical treatment decisions to benefit patients. AVAILABILITY AND IMPLEMENTATION:An R package MetaKTSP is available online. (http://tsenglab.biostat.pitt.edu/software.htm). CONTACT:ctseng@pitt.edu SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Project description:BACKGROUND: Over-diagnosis and treatment of prostate cancer has been a major problem in prostate cancer care and management. Currently the most relevant prognostic factor to predict a patient's risk of death due to prostate cancer is the Gleason score of the biopsied tissue samples. However, pathological analysis is subjective, and the Gleason score is only a qualitative estimate of the cancer malignancy. Molecular biomarkers and diagnostic tests that can accurately predict prostate tumor aggressiveness are rather limited. METHOD: We report here for the first time the development of a nanoparticle test that not only can distinguish prostate cancer from normal and benign conditions, but also has the potential to predict the aggressiveness of prostate cancer quantitatively. To conduct the test, a prostate tissue lysate sample is spiked into a blood serum or human IgG solution and the spiked sample is incubated with a citrate-protected gold nanoparticle solution. IgG is known to adsorb to citrate-protected gold nanoparticles to form a "protein corona" on the nanoparticle surface. From this study, we discovered that certain tumor-specific molecules can interact with IgG and change the adsorption behavior of IgG to the gold nanoparticles. This change is reflected in the nanoparticle size of the assay solution and detected by a dynamic light scattering technique. Assay data were analyzed by one-way ANOVA for multiple variant analysis, and using the Student t-test or nonparametric Mann-Whitney U-tests for pairwise analyses. RESULTS: An inverse, quantitative correlation of the average nanoparticle size of the assay solution with tumor status and histological diagnostic grading was observed from the nanoparticle test. IgG solutions spiked with prostate tumor tissue exhibit significantly smaller nanoparticle size than the solutions spiked with normal and benign tissues. The higher grade the tumor is, the smaller the nanoparticle size is. The test particularly revealed large differences among the intermediate Grade 2 tumors, and suggested the need to treat them differently. CONCLUSION: Development of a new nanoparticle test may provide a quantitative measure of the prostate cancer aggressiveness. If validated in a larger study of patients with prostate cancer, this test could become a new diagnostic tool in conjunction with Gleason Score pathology diagnostics to better distinguish aggressive cancer from indolent tumor.

Dataset Information

Usefulness of the top-scoring pairs of genes for prediction of prostate cancer progression.

Publications

Usefulness of the top-scoring pairs of genes for prediction of prostate cancer progression.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets