Dataset Information

Whole Exome and Transcriptome RNA-Sequencing Model for the Diagnosis of Prostate Cancer.

ABSTRACT: In our previous study, we developed a genome-wide DNA methylation model for the diagnosis of prostate cancer, and we pointed out that a considerable average error is associated with the current method for the diagnosis of prostate cancer, which is predicated on pathological assessment of biopsied tissue. In this study, we utilized whole exome and transcriptome RNA-sequencing (RNA-seq) data that were derived from 468 tumor samples and 51 normal samples of prostatic tissue, and we analyzed over 20,000 genes per sample. We were able to develop a mathematical model that classified tumor tissue versus normal tissue with a high accuracy. The overall sensitivity was 97.01%, and the overall specificity was 94.12%. The input variables to the model were the mRNA expression values of the following nine genes: ANGPT1, MED21, AOX1, PLP2, HPN, HPN-AS1, EPHA10, NKX2-3, and LRFN1. The model was validated with unknown samples, with a 10-fold cross-validation, and a leave-one-out cross-validation. We present here a genomic model, based on a whole exome and transcriptome RNA-seq analysis of biopsied prostatic tissue, that could be utilized in the diagnosis of prostate cancer.

SUBMITTER: Nikas JB

PROVIDER: S-EPMC6964263 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Whole Exome and Transcriptome RNA-Sequencing Model for the Diagnosis of Prostate Cancer.

Nikas Jason B JB Mitanis Nikos T NT Nikas Emily G EG

ACS omega 20191231 1

In our previous study, we developed a genome-wide DNA methylation model for the diagnosis of prostate cancer, and we pointed out that a considerable average error is associated with the current method for the diagnosis of prostate cancer, which is predicated on pathological assessment of biopsied tissue. In this study, we utilized whole exome and transcriptome RNA-sequencing (RNA-seq) data that were derived from 468 tumor samples and 51 normal samples of prostatic tissue, and we analyzed over 20 ...[more]

PMID: 31956794

Similar Datasets

Project description:Inherited peripheral neuropathies (IPNs) are a group of related diseases primarily affecting the peripheral motor and sensory neurons. They include the hereditary sensory neuropathies (HSN), hereditary motor neuropathies (HMN), and Charcot-Marie-Tooth disease (CMT). Using whole-exome sequencing (WES) to achieve a genetic diagnosis is particularly suited to IPNs, where over 80 genes are involved with weak genotype-phenotype correlations beyond the most common genes. We performed WES for 110 index patients with IPN where the genetic cause was undetermined after previous screening for mutations in common genes selected by phenotype and mode of inheritance. We identified 41 missense sequence variants in the known IPN genes in our cohort of 110 index patients. Nine variants (8%), identified in the genes MFN2, GJB1, BSCL2, and SETX, are previously reported mutations and considered to be pathogenic in these families. Twelve novel variants (11%) in the genes NEFL, TRPV4, KIF1B, BICD2, and SETX are implicated in the disease but require further evidence of pathogenicity. The remaining 20 variants were confirmed as polymorphisms (not causing the disease) and are detailed here to help interpret sequence variants identified in other family studies. Validation using segregation, normal controls, and bioinformatics tools was valuable as supporting evidence for sequence variants implicated in disease. In addition, we identified one SETX sequence variant (c.7640T>C), previously reported as a putative mutation, which we have confirmed as a nonpathogenic rare polymorphism. This study highlights the advantage of using WES for genetic diagnosis in highly heterogeneous diseases such as IPNs and has been particularly powerful in this cohort where genetic diagnosis could not be achieved due to phenotype and mode of inheritance not being previously obvious. However, first tier testing for common genes in clinically well-defined cases remains important and will account for most positive results.

Dataset Information

Whole Exome and Transcriptome RNA-Sequencing Model for the Diagnosis of Prostate Cancer.

Publications

Whole Exome and Transcriptome RNA-Sequencing Model for the Diagnosis of Prostate Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets