Dataset Information

Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers.

ABSTRACT: Aberrant DNA methylation has emerged as a hallmark in several cancers and contributes to risk, oncogenesis, progression, and prognosis. In this study, we performed imputation-based and conventional methylome-wide association analyses for breast cancer (BrCa) and prostate cancer (PrCa). The imputation-based approach identified DNA methylation at cytosine-phosphate-guanine sites (CpGs) associated with BrCa and PrCa risk utilising genome-wide association summary statistics (N_BrCa = 228,951, N_PrCa = 140,254) and prebuilt methylation prediction models, while the conventional approach identified CpG associations utilising TCGA and GEO experimental methylation data (N_BrCa = 621, N_PrCa = 241). Enrichment analysis of the association results implicated 77 and 81 genetically influenced CpGs for BrCa and PrCa, respectively. Furthermore, analysis of differential gene expression around these CpGs suggests a genome-epigenome-transcriptome mechanistic relationship. Conditional analyses identified multiple independent secondary SNP associations (P_cond < 0.05) around 28 BrCa and 22 PrCa CpGs. Cross-cancer analysis identified eight common CpGs, including a strong therapeutic target in SREBF1 (17p11.2)-a key player in lipid metabolism. These findings highlight the utility of integrative analysis of multi-omic cancer data to identify robust biomarkers and understand their regulatory effects on cancer risk.

SUBMITTER: Sathyanarayanan A

PROVIDER: S-EPMC9203749 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers.

Sathyanarayanan Anita A Tanha Hamzeh M HM Mehta Divya D Nyholt Dale R DR

Communications biology 20220616 1

Aberrant DNA methylation has emerged as a hallmark in several cancers and contributes to risk, oncogenesis, progression, and prognosis. In this study, we performed imputation-based and conventional methylome-wide association analyses for breast cancer (BrCa) and prostate cancer (PrCa). The imputation-based approach identified DNA methylation at cytosine-phosphate-guanine sites (CpGs) associated with BrCa and PrCa risk utilising genome-wide association summary statistics (N<sub>BrCa</sub> = 228,9 ...[more]

PMID: 35710732

Similar Datasets

Project description:BackgroundPredicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease. The objective of our study was to identify prostate cancer prognostic markers in patients 'sera using a multi-omics discovery platform.MethodsPre-surgical serum samples collected from a longitudinal, racially diverse, prostate cancer patient cohort (N = 382) were examined. Linear Regression and Bayesian computational approaches integrated with multi-omics, were used to select markers to predict biochemical recurrence (BCR). BCR-free survival was modeled using unadjusted Kaplan-Meier estimation curves and multivariable Cox proportional hazards analysis, adjusted for key pathologic variables. Receiver operating characteristic (ROC) curve statistics were used to examine the predictive value of markers in discriminating BCR events from non-events. The findings were further validated by creating a training set (N = 267) and testing set (N = 115) from the cohort.ResultsAmong 382 patients, 72 (19%) experienced a BCR event in a median follow-up time of 6.9 years. Two proteins-Tenascin C (TNC) and Apolipoprotein A1V (Apo-AIV), one metabolite-1-Methyladenosine (1-MA) and one phospholipid molecular species phosphatidic acid (PA) 18:0-22:0 showed a cumulative predictive performance of AUC = 0.78 [OR (95% CI) = 6.56 (2.98-14.40), P < 0.05], in differentiating patients with and without BCR event. In the validation set all four metabolites consistently reproduced an equivalent performance with high negative predictive value (NPV; > 80%) for BCR. The combination of pTstage and Gleason score with the analytes, further increased the sensitivity [AUC = 0.89, 95% (CI) = 4.45-32.05, P < 0.05], with an increased NPV (0.96) and OR (12.4) for BCR. The panel of markers combined with the pathological parameters demonstrated a more accurate prediction of BCR than the pathological parameters alone in prostate cancer.ConclusionsIn this study, a panel of serum analytes were identified that complemented pathologic patient features in predicting prostate cancer progression. This panel offers a new opportunity to complement current prognostic markers and to monitor the potential impact of primary treatment versus surveillance on patient oncological outcome.

Project description:BackgroundUrinary bladder cancer (UBC) is a common malignancy of the urinary tract; however, the mechanism underlying its high recurrence and responses to immunotherapy remains unclear, making clinical outcome predictions difficult. Epigenetic alterations, especially DNA methylation, play important roles in bladder cancer development and are increasingly being investigated as biomarkers for diagnostic or prognostic predictions. However, little is known about hydroxymethylation since previous studies based on bisulfite-sequencing approaches could not differentiate between 5mC and 5hmC signals, resulting in entangled methylation results.MethodsTissue samples of bladder cancer patients who underwent laparoscopic radical cystectomy (LRC), partial cystectomy (PC), or transurethral resection of bladder tumor (TURBT) were collected. We utilized a multi-omics approach to analyze both primary and recurrent bladder cancer samples. By integrating various techniques including RNA sequencing, oxidative reduced-representation bisulfite sequencing (oxRRBS), reduced-representation bisulfite sequencing (RRBS), and whole exome sequencing, a comprehensive analysis of the genome, transcriptome, methylome, and hydroxymethylome landscape of these cancers was possible.ResultsBy whole exome sequencing, we identified driver mutations involved in the development of UBC, including those in FGFR3, KDMTA, and KDMT2C. However, few of these driver mutations were associated with the down-regulation of programmed death-ligand 1 (PD-L1) or recurrence in UBC. By integrating RRBS and oxRRBS data, we identified fatty acid oxidation-related genes significantly enriched in 5hmC-associated transcription alterations in recurrent bladder cancers. We also observed a series of 5mC hypo differentially methylated regions (DMRs) in the gene body of NFATC1, which is highly involved in T-cell immune responses in bladder cancer samples with high expression of PD-L1. Since 5mC and 5hmC alternations are globally anti-correlated, RRBS-seq-based markers that combine the 5mC and 5hmC signals, attenuate cancer-related signals, and therefore, are not optimal as clinical biomarkers.ConclusionsBy multi-omics profiling of UBC samples, we showed that epigenetic alternations are more involved compared to genetic mutations in the PD-L1 regulation and recurrence of UBC. As proof of principle, we demonstrated that the combined measurement of 5mC and 5hmC levels by the bisulfite-based method compromises the prediction accuracy of epigenetic biomarkers.

Dataset Information

Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers.

Publications

Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets