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Cancer Epidemiol Biomarkers Prev. 2019 Jan;28(1):198-207. doi: 10.1158/1055-9965.EPI-18-0491. Epub 2018 Sep 27.

Epigenetically Silenced Candidate Tumor Suppressor Genes in Prostate Cancer: Identified by Modeling Methylation Stratification and Applied to Progression Prediction.

Author information

1
Department of Computer Science, Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, Louisiana.
2
Tulane School of Medicine, Tulane Cancer Center, Tulane University, New Orleans, Louisiana.
3
Department of Biostatistics and Data Science, Center for Bioinformatics and Genomics, Tulane University, New Orleans, Louisiana.
4
Department of Computer Science, Bioinformatics Facility of Xavier NIH RCMI Cancer Research Center, Xavier University of Louisiana, New Orleans, Louisiana. kzhang@xula.edu.

Abstract

BACKGROUND:

Recent studies have shown that epigenetic alterations, especially the hypermethylated promoters of tumor suppressor genes (TSGs), contribute to prostate cancer progression and metastasis. This article proposes a novel algorithm to identify epigenetically silenced TSGs (epi-TSGs) for prostate cancer.

METHODS:

Our method is based on the perception that the promoter CpG island(s) of a typical epi-TSG has a stratified methylation profile over tumor samples. In other words, we assume that the methylation profile resembles the combination of a binary distribution of a driver mutation and a continuous distribution representing measurement noise and intratumor heterogeneity.

RESULTS:

Applying the proposed algorithm and an existing method to The Cancer Genome Atlas prostate cancer data, we identify 57 candidate epi-TSGs. Over one third of these epi-TSGs have been reported to carry potential tumor suppression functions. The negative correlations between the expression levels and methylation levels of these genes are validated on external independent datasets. We further find that the expression profiling of these genes is a robust predictive signature for Gleason scores, with the AUC statistic ranging from 0.75 to 0.79. The identified signature also shows prediction strength for tumor progression stages, biochemical recurrences, and metastasis events.

CONCLUSIONS:

We propose a novel method for pinpointing candidate epi-TSGs in prostate cancer. The expression profiling of the identified epi-TSGs demonstrates significant prediction strength for tumor progression.

IMPACT:

The proposed epi-TSGs identification method can be adapted to other cancer types beyond prostate cancer. The identified clinically significant epi-TSGs would shed light on the carcinogenesis of prostate adenocarcinomas.

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