Dataset Information

Multi-omics Integration Analysis Robustly Predicts High-Grade Patient Survival and Identifies CPT1B Effect on Fatty Acid Metabolism in Bladder Cancer.

ABSTRACT: PURPOSE:The perturbation of metabolic pathways in high-grade bladder cancer has not been investigated. We aimed to identify a metabolic signature in high-grade bladder cancer by integrating unbiased metabolomics, lipidomics, and transcriptomics to predict patient survival and to discover novel therapeutic targets. EXPERIMENTAL DESIGN:We performed high-resolution liquid chromatography mass spectrometry (LC-MS) and bioinformatic analysis to determine the global metabolome and lipidome in high-grade bladder cancer. We further investigated the effects of impaired metabolic pathways using in vitro and in vivo models. RESULTS:We identified 519 differential metabolites and 19 lipids that were differentially expressed between low-grade and high-grade bladder cancer using the NIST MS metabolomics compendium and lipidblast MS/MS libraries, respectively. Pathway analysis revealed a unique set of biochemical pathways that are highly deregulated in high-grade bladder cancer. Integromics analysis identified a molecular gene signature associated with poor patient survival in bladder cancer. Low expression of CPT1B in high-grade tumors was associated with low FAO and low acyl carnitine levels in high-grade bladder cancer, which were confirmed using tissue microarrays. Ectopic expression of the CPT1B in high-grade bladder cancer cells led to reduced EMT in in vitro, and reduced cell proliferation, EMT, and metastasis in vivo. CONCLUSIONS:Our study demonstrates a novel approach for the integration of metabolomics, lipidomics, and transcriptomics data, and identifies a common gene signature associated with poor survival in patients with bladder cancer. Our data also suggest that impairment of FAO due to downregulation of CPT1B plays an important role in the progression toward high-grade bladder cancer and provide potential targets for therapeutic intervention.

SUBMITTER: Vantaku V

PROVIDER: S-EPMC6571061 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Multi-omics Integration Analysis Robustly Predicts High-Grade Patient Survival and Identifies CPT1B Effect on Fatty Acid Metabolism in Bladder Cancer.

Vantaku Venkatrao V Dong Jianrong J Ambati Chandrashekar R CR Perera Dimuthu D Donepudi Sri Ramya SR Amara Chandra Sekhar CS Putluri Vasanta V Ravi Shiva Shankar SS Robertson Matthew J MJ Piyarathna Danthasinghe Waduge Badrajee DWB Villanueva Mariana M von Rundstedt Friedrich-Carl FC Karanam Balasubramanyam B Ballester Leomar Y LY Terris Martha K MK Bollag Roni J RJ Lerner Seth P SP Apolo Andrea B AB Villanueva Hugo H Lee MinJae M Sikora Andrew G AG Lotan Yair Y Sreekumar Arun A Coarfa Cristian C Putluri Nagireddy N

Clinical cancer research : an official journal of the American Association for Cancer Research 20190307 12

<h4>Purpose</h4>The perturbation of metabolic pathways in high-grade bladder cancer has not been investigated. We aimed to identify a metabolic signature in high-grade bladder cancer by integrating unbiased metabolomics, lipidomics, and transcriptomics to predict patient survival and to discover novel therapeutic targets.<h4>Experimental design</h4>We performed high-resolution liquid chromatography mass spectrometry (LC-MS) and bioinformatic analysis to determine the global metabolome and lipido ...[more]

PMID: 30846479

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Project description:ObjectivePost-operative biochemical relapse (BCR) continues to occur in a significant percentage of patients with localized prostate cancer (PCa). Current stratification methods are not adequate to identify high-risk patients. The present study exploits the ability of deep learning (DL) algorithms using the H2O package to combine multi-omics data to resolve this problem.MethodsFive-omics data from 417 PCa patients from The Cancer Genome Atlas (TCGA) were used to construct the DL-based, relapse-sensitive model. Among them, 265 (63.5%) individuals experienced BCR. Five additional independent validation sets were applied to assess its predictive robustness. Bioinformatics analyses of two relapse-associated subgroups were then performed for identification of differentially expressed genes (DEGs), enriched pathway analysis, copy number analysis and immune cell infiltration analysis.ResultsThe DL-based model, with a significant difference (P = 6e-9) between two subgroups and good concordance index (C-index = 0.767), were proven to be robust by external validation. 1530 DEGs including 678 up- and 852 down-regulated genes were identified in the high-risk subgroup S2 compared with the low-risk subgroup S1. Enrichment analyses found five hallmark gene sets were up-regulated while 13 were down-regulated. Then, we found that DNA damage repair pathways were significantly enriched in the S2 subgroup. CNV analysis showed that 30.18% of genes were significantly up-regulated and gene amplification on chromosomes 7 and 8 was significantly elevated in the S2 subgroup. Moreover, enrichment analysis revealed that some DEGs and pathways were associated with immunity. Three tumor-infiltrating immune cell (TIIC) groups with a higher proportion in the S2 subgroup (p = 1e-05, p = 8.7e-06, p = 0.00014) and one TIIC group with a higher proportion in the S1 subgroup (P = 1.3e-06) were identified.ConclusionWe developed a novel, robust classification for understanding PCa relapse. This study validated the effectiveness of deep learning technique in prognosis prediction, and the method may benefit patients and prevent relapse by improving early detection and advancing early intervention.

Dataset Information

Multi-omics Integration Analysis Robustly Predicts High-Grade Patient Survival and Identifies CPT1B Effect on Fatty Acid Metabolism in Bladder Cancer.

Publications

Multi-omics Integration Analysis Robustly Predicts High-Grade Patient Survival and Identifies CPT1B Effect on Fatty Acid Metabolism in Bladder Cancer.

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