Dataset Information

Identification of Key Gene Signatures Associated With Bone Metastasis in Castration-Resistant Prostate Cancer Using Co-Expression Analysis.

ABSTRACT: About 80-90% of castration-resistant prostate cancer (CRPC) patients would develop bone metastasis. However, the molecular mechanisms of bone metastasis are still not clear. This study aimed to detect the differences between the tumor and normal samples in bone after metastatic colonization. Four transcriptional datasets (GSE32269, GSE101607, GSE29650, and GSE74685) were obtained from the GEO database. 1983 differentially expressed genes (DEGs) were first identified between tumor and normal marrow samples in GSE32269. Most of the top 10 up-regulated DEGs are related with prostate cancer, and the top 10 down-regulated DEGs are mainly related with bone development. Seven co-expression modules were then detected based on the 1469 DEGs shared by the four datasets. Three of them were found highly preserved among the four datasets. Enrichment analysis showed that the three modules were respectively enriched in Cell adhesion molecules (CAMs), Leukocyte transendothelial migration and cell cycle, which might play significantly important roles in the tumor development in bone marrow. Ten, 17, and 99 hub genes for each module were then identified. And four genes (C3AR1, IL10RA, LY86, and MS4A6A) were detect to be tightly related to progression of bone metastatic CRPC. ROC curve was plotted and AUC was calculated to distinguish tumor and normal bone marrow samples as well as bone and non-bone metastatic CRPCs. The present study identified key genes and modules involved in bone metastatic CRPCs, which may provide new insights and biomarkers for understanding of the molecular mechanisms of bone metastatic CRPC.

SUBMITTER: Yu Z

PROVIDER: S-EPMC7884857 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of Key Gene Signatures Associated With Bone Metastasis in Castration-Resistant Prostate Cancer Using Co-Expression Analysis.

Yu Zhongxiang Z Zou Hanlin H Wang Huihao H Li Qi Q Yu Dong D

Frontiers in oncology 20210202

About 80-90% of castration-resistant prostate cancer (CRPC) patients would develop bone metastasis. However, the molecular mechanisms of bone metastasis are still not clear. This study aimed to detect the differences between the tumor and normal samples in bone after metastatic colonization. Four transcriptional datasets (GSE32269, GSE101607, GSE29650, and GSE74685) were obtained from the GEO database. 1983 differentially expressed genes (DEGs) were first identified between tumor and normal marr ...[more]

PMID: 33604283

Similar Datasets

Project description:BACKGROUND:Biomarkers are urgently needed to dissect the heterogeneity of prostate cancer between patients to improve treatment and accelerate drug development. We analysed blood mRNA expression arrays to identify patients with metastatic castration-resistant prostate cancer with poorer outcome. METHODS:Whole blood was collected into PAXgene tubes from patients with castration-resistant prostate cancer and patients with prostate cancer selected for active surveillance. In stage I (derivation set), patients with castration-resistant prostate cancer were used as cases and patients under active surveillance were used as controls. These patients were recruited from The Royal Marsden Hospital NHS Foundation Trust (Sutton, UK) and The Beatson West of Scotland Cancer Centre (Glasgow, UK). In stage II (validation-set), patients with castration-resistant prostate cancer recruited from the Memorial Sloan-Kettering Cancer Center (New York, USA) were assessed. Whole-blood RNA was hybridised to Affymetrix U133plus2 microarrays. Expression profiles were analysed with Bayesian latent process decomposition (LPD) to identify RNA expression profiles associated with castration-resistant prostate cancer subgroups; these profiles were then confirmed by quantative reverse transcriptase (qRT) PCR studies and correlated with overall survival in both the test-set and validation-set. FINDINGS:LPD analyses of the mRNA expression data divided the evaluable patients in stage I (n=94) into four groups. All patients in LPD1 (14 of 14) and most in LPD2 (17 of 18) had castration-resistant prostate cancer. Patients with castration-resistant prostate cancer and those under active surveillance comprised LPD3 (15 of 31 castration-resistant prostate cancer) and LDP4 (12 of 21 castration-resistant prostate cancer). Patients with castration-resistant prostate cancer in the LPD1 subgroup had features associated with worse prognosis and poorer overall survival than patients with castration-resistant prostate cancer in other LPD subgroups (LPD1 overall survival 10·7 months [95% CI 4·1-17·2] vs non-LPD1 25·6 months [18·0-33·4]; p<0·0001). A nine-gene signature verified by qRT-PCR classified patients into this LPD1 subgroup with a very low percentage of misclassification (1·2%). The ten patients who were initially unclassifiable by the LPD analyses were subclassified by this signature. We confirmed the prognostic utility of this nine-gene signature in the validation castration-resistant prostate cancer cohort, where LPD1 membership was also associated with worse overall survival (LPD1 9·2 months [95% CI 2·1-16·4] vs non-LPD1 21·6 months [7·5-35·6]; p=0·001), and remained an independent prognostic factor in multivariable analyses for both cohorts. INTERPRETATION:Our results suggest that whole-blood gene profiling could identify gene-expression signatures that stratify patients with castration-resistant prostate cancer into distinct prognostic groups. FUNDING:AstraZeneca, Experimental Cancer Medicine Centre, Prostate Cancer Charity, Prostate Cancer Foundation.

Dataset Information

Identification of Key Gene Signatures Associated With Bone Metastasis in Castration-Resistant Prostate Cancer Using Co-Expression Analysis.

Publications

Identification of Key Gene Signatures Associated With Bone Metastasis in Castration-Resistant Prostate Cancer Using Co-Expression Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets