Dataset Information

Convergence of Prognostic Gene Signatures Suggests Underlying Mechanisms of Human Prostate Cancer Progression.

ABSTRACT: The highly heterogeneous clinical course of human prostate cancer has prompted the development of multiple RNA biomarkers and diagnostic tools to predict outcome for individual patients. Biomarker discovery is often unstable with, for example, small changes in discovery dataset configuration resulting in large alterations in biomarker composition. Our hypothesis, which forms the basis of this current study, is that highly significant overlaps occurring between gene signatures obtained using entirely different approaches indicate genes fundamental for controlling cancer progression. For prostate cancer, we found two sets of signatures that had significant overlaps suggesting important genes (p < 10-34 for paired overlaps, hypergeometrical test). These overlapping signatures defined a core set of genes linking hormone signalling (HES6-AR), cell cycle progression (Prolaris) and a molecular subgroup of patients (PCS1) derived by Non Negative Matrix Factorization (NNMF) of control pathways, together designated as SIG-HES6. The second set (designated SIG-DESNT) consisted of the DESNT diagnostic signature and a second NNMF signature PCS3. Stratifications using SIG-HES6 (HES6, PCS1, Prolaris) and SIG-DESNT (DESNT) classifiers frequently detected the same individual high-risk cancers, indicating that the underlying mechanisms associated with SIG-HES6 and SIG-DESNT may act together to promote aggressive cancer development. We show that the use of combinations of a SIG-HES6 signature together with DESNT substantially increases the ability to predict poor outcome, and we propose a model for prostate cancer development involving co-operation between the SIG-HES6 and SIG-DESNT pathways that has implication for therapeutic design.

SUBMITTER: Luca BA

PROVIDER: S-EPMC7397325 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Convergence of Prognostic Gene Signatures Suggests Underlying Mechanisms of Human Prostate Cancer Progression.

Luca Bogdan-Alexandru BA Moulton Vincent V Ellis Christopher C Connell Shea P SP Brewer Daniel S DS Cooper Colin S CS

Genes 20200716 7

The highly heterogeneous clinical course of human prostate cancer has prompted the development of multiple RNA biomarkers and diagnostic tools to predict outcome for individual patients. Biomarker discovery is often unstable with, for example, small changes in discovery dataset configuration resulting in large alterations in biomarker composition. Our hypothesis, which forms the basis of this current study, is that highly significant overlaps occurring between gene signatures obtained using enti ...[more]

PMID: 32708551

Similar Datasets

Project description:Most patients with pharmacoresistant mesial temporal lobe epilepsy (MTLE) have hippocampal sclerosis on the postoperative histopathological examination. Although most patients with MTLE do not refer to a family history of the disease, familial forms of MTLE have been reported. We studied surgical specimens from patients with MTLE who had epilepsy surgery for medically intractable seizures. We assessed and compared gene expression profiles of the tissue lesion found in patients with familial MTLE (n = 3) and sporadic MTLE (n = 5). In addition, we used data from control hippocampi obtained from a public database (n = 7). We obtained expression profiles using the Human Genome U133 Plus 2.0 (Affymetrix) microarray platform. Overall, the molecular profile identified in familial MTLE differed from that in sporadic MTLE. In the tissue of patients with familial MTLE, we found an over-representation of the biological pathways related to protein response, mRNA processing, and synaptic plasticity and function. In sporadic MTLE, the gene expression profile suggests that the inflammatory response is highly activated. In addition, we found enrichment of gene sets involved in inflammatory cytokines and mediators and chemokine receptor pathways in both groups. However, in sporadic MTLE, we also found enrichment of epidermal growth factor signaling, prostaglandin synthesis and regulation, and microglia pathogen phagocytosis pathways. Furthermore, based on the gene expression signatures, we identified different potential compounds to treat patients with familial and sporadic MTLE. To our knowledge, this is the first study assessing the mRNA profile in surgical tissue obtained from patients with familial MTLE and comparing it with sporadic MTLE. Our results clearly show that, despite phenotypic similarities, both forms of MTLE present distinct molecular signatures, thus suggesting different underlying molecular mechanisms that may require distinct therapeutic approaches.

Project description:Leprosy and psoriasis rarely coexist, the specific molecular mechanisms underlying their mutual exclusion have not been extensively investigated. This study aimed to reveal the underlying mechanism responsible for the mutual exclusion between psoriasis and leprosy. We obtained leprosy and psoriasis data from ArrayExpress and GEO database. Differential expression analysis was conducted separately on the leprosy and psoriasis using DEseq2. Differentially expressed genes (DEGs) with opposite expression patterns in psoriasis and leprosy were identified, which could potentially involve in their mutual exclusion. Enrichment analysis was performed on these candidate mutually exclusive genes, and a protein-protein interaction (PPI) network was constructed to identify hub genes. The expression of these hub genes was further validated in an external dataset to obtain the critical mutually exclusive genes. Additionally, immune cell infiltration in psoriasis and leprosy was analyzed using single-sample gene set enrichment analysis (ssGSEA), and the correlation between critical mutually exclusive genes and immune cells was also examined. Finally, the expression pattern of critical mutually exclusive genes was evaluated in a single-cell transcriptome dataset. We identified 1098 DEGs in the leprosy dataset and 3839 DEGs in the psoriasis dataset. 48 candidate mutually exclusive genes were identified by taking the intersection. Enrichment analysis revealed that these genes were involved in cholesterol metabolism pathways. Through PPI network analysis, we identified APOE, CYP27A1, FADS1, and SOAT1 as hub genes. APOE, CYP27A1, and SOAT1 were subsequently validated as critical mutually exclusive genes on both internal and external datasets. Analysis of immune cell infiltration indicated higher abundance of 16 immune cell types in psoriasis and leprosy compared to normal controls. The abundance of 6 immune cell types in psoriasis and leprosy positively correlated with the expression levels of APOE and CYP27A1. Single-cell data analysis demonstrated that critical mutually exclusive genes were predominantly expressed in Schwann cells and fibroblasts. This study identified APOE, CYP27A1, and SOAT1 as critical mutually exclusive genes. Cholesterol metabolism pathway illustrated the possible mechanism of the inverse association of psoriasis and leprosy. The findings of this study provide a basis for identifying mechanisms and therapeutic targets for psoriasis.

Dataset Information

Convergence of Prognostic Gene Signatures Suggests Underlying Mechanisms of Human Prostate Cancer Progression.

Publications

Convergence of Prognostic Gene Signatures Suggests Underlying Mechanisms of Human Prostate Cancer Progression.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets