Dataset Information

Distinct Lipidomic Landscapes Associated with Clinical Stages of Urothelial Cancer of the Bladder.

ABSTRACT: BACKGROUND:The first global lipidomic profiles associated with urothelial cancer of the bladder (UCB) and its clinical stages associated with progression were identified. OBJECTIVE:To identify lipidomic signatures associated with survival and different clinical stages of UCB. DESIGN, SETTING, AND PARTICIPANTS:Pathologically confirmed 165 bladder-derived tissues (126 UCB, 39 benign adjacent or normal bladder tissues). UCB tissues included Ta (n=16), T1 (n=30), T2 (n=43), T3 (n=27), and T4 (n=9); lymphovascular invasion (LVI) positive (n=52) and negative (n=69); and lymph node status N0 (n=28), N1 (n=11), N2 (n=9), N3 (n=3), and Nx (n=75). RESULTS AND LIMITATIONS:UCB tissues have higher levels of phospholipids and fatty acids, and reduced levels of triglycerides compared with benign tissues. A total of 59 genes associated with altered lipids in UCB strongly correlate with patient survival in an UCB public dataset. Within UCB, there was a progressive decrease in the levels of phosphatidylserine (PS), phosphatidylethanolamines (PEs), and phosphocholines, whereas an increase in the levels of diacylglycerols (DGs) with tumor stage. Transcript and protein expression of phosphatidylserine synthase 1, which converts DGs to PSs, decreased progressively with tumor stage. Levels of DGs and lyso-PEs were significantly elevated in tumors with LVI and lymph node involvement, respectively. Lack of carcinoma in situ and treatment information is the limitation of our study. CONCLUSIONS:To date, this is the first study describing the global lipidomic profiles associated with UCB and identifies lipids associated with tumor stages, LVI, and lymph node status. Our data suggest that triglycerides serve as the primary energy source in UCB, while phospholipid alterations could affect membrane structure and/or signaling associated with tumor progression. PATIENT SUMMARY:Lipidomic alterations identified in this study set the stage for characterization of pathways associated with these altered lipids that, in turn, could inform the development of first-of-its-kind lipid-based noninvasive biomarkers and novel therapeutic targets for aggressive urothelial cancer of the bladder.

SUBMITTER: Piyarathna DWB

PROVIDER: S-EPMC5650548 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Distinct Lipidomic Landscapes Associated with Clinical Stages of Urothelial Cancer of the Bladder.

Piyarathna Danthasinghe Waduge Badrajee DWB Rajendiran Thekkelnaycke M TM Putluri Vasanta V Vantaku Venkatrao V Soni Tanu T von Rundstedt Friedrich-Carl FC Donepudi Sri Ramya SR Jin Feng F Maity Suman S Ambati Chandrashekar R CR Dong Jianrong J Gödde Daniel D Roth Stephan S Störkel Stephan S Degener Stephan S Michailidis George G Lerner Seth P SP Pennathur Subramaniam S Lotan Yair Y Coarfa Cristian C Sreekumar Arun A Putluri Nagireddy N

European urology focus 20170420 6

<h4>Background</h4>The first global lipidomic profiles associated with urothelial cancer of the bladder (UCB) and its clinical stages associated with progression were identified.<h4>Objective</h4>To identify lipidomic signatures associated with survival and different clinical stages of UCB.<h4>Design, setting, and participants</h4>Pathologically confirmed 165 bladder-derived tissues (126 UCB, 39 benign adjacent or normal bladder tissues). UCB tissues included Ta (n=16), T1 (n=30), T2 (n=43), T3 ...[more]

PMID: 28753886

Similar Datasets

Project description:Background: Numerous studies have shown that infiltrating eosinophils play a key role in the tumor progression of bladder urothelial carcinoma (BLCA). However, the roles of eosinophils and associated hub genes in clinical outcomes and immunotherapy are not well known. Methods: BLCA patient data were extracted from the TCGA database. The tumor immune microenvironment (TIME) was revealed by the CIBERSORT algorithm. Candidate modules and hub genes associated with eosinophils were identified by weighted gene co-expression network analysis (WGCNA). The external GEO database was applied to validate the above results. TIME-related genes with prognostic significance were screened by univariate Cox regression analysis, lasso regression, and multivariate Cox regression analysis. The patient's risk score (RS) was calculated and divided subjects into high-risk group (HRG) and low-risk group (LRG). The nomogram was developed based on the risk signature. Models were validated via receiver operating characteristic (ROC) curves and calibration curves. Differences between HRG and LRG in clinical features and tumor mutational burden (TMB) were compared. The Immune Phenomenon Score (IPS) was calculated to estimate the immunotherapeutic significance of RS. Half-maximal inhibitory concentrations (IC50s) of chemotherapeutic drugs were predicted by the pRRophetic algorithm. Results: 313 eosinophil-related genes were identified by WGCNA. Subsequently, a risk signature containing 9 eosinophil-related genes (AGXT, B3GALT2, CCDC62, CLEC1B, CLEC2D, CYP19A1, DNM3, SLC5A9, SLC26A8) was finally developed via multiplex analysis and screening. Age (p < 0.001), grade (p < 0.001), and RS (p < 0.001) were independent predictors of survival in BLCA patients. Based on the calibration curve, our risk signature nomogram was confirmed as a good predictor of BLCA patients' prognosis at 1, 3, and 5 years. The association analysis of RS and immunotherapy indicated that low-risk patients were more credible for novel immune checkpoint inhibitors (ICI) immunotherapy. The chemotherapeutic drug model suggests that RS has an effect on the drug sensitivity of patients. Conclusions: In conclusion, the eosinophil-based RS can be used as a reliable clinical predictor and provide insights into the precise treatment of BLCA.

Project description:BackgroundDespite the importance of tumor-infiltrating T lymphocytes (TILs) in cancer biology, the relationship between TIL phenotypes and their prognostic relevance for localized non-small-cell lung cancer (NSCLC) has not been well established.Patients and methodsFresh tumor and normal adjacent tissue was prospectively collected from 150 patients with localized NSCLC. Tissue was comprehensively characterized by high-dimensional flow cytometry of TILs integrated with immunogenomic data from multiplex immunofluorescence, T-cell receptor sequencing, exome sequencing, RNA sequencing, targeted proteomics, and clinicopathologic features.ResultsWhile neither the magnitude of TIL infiltration nor specific TIL subsets were significantly prognostic alone, the integration of high-dimensional flow cytometry data identified two major immunotypes (IM1 and IM2) that were predictive of recurrence-free survival independent of clinical characteristics. IM2 was associated with poor prognosis and characterized by the presence of proliferating TILs expressing cluster of differentiation 103, programmed cell death protein 1, T-cell immunoglobulin and mucin-domain containing protein 3, and inducible T-cell costimulator. Conversely, IM1 was associated with good prognosis and differentiated by an abundance of CD8+ T cells expressing cytolytic enzymes, CD4+ T cells lacking the expression of inhibitory receptors, and increased levels of B-cell infiltrates and tertiary lymphoid structures. While increased B-cell infiltration was associated with good prognosis, the best prognosis was observed in patients with tumors exhibiting high levels of both B cells and T cells. These findings were validated in patient tumors from The Cancer Genome Atlas.ConclusionsOur study suggests that although the number of infiltrating T cells is not associated with patient survival, the nature of the infiltrating T cells, resolved in distinct TIL immunotypes, is prognostically relevant in NSCLC and may inform therapeutic approaches to clinical care.

Dataset Information

Distinct Lipidomic Landscapes Associated with Clinical Stages of Urothelial Cancer of the Bladder.

Publications

Distinct Lipidomic Landscapes Associated with Clinical Stages of Urothelial Cancer of the Bladder.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets