Dataset Information

Unsupervised analysis of combined lipid and coagulation data reveals coagulopathy subtypes among dialysis patients.

ABSTRACT: Hemodialysis (HD) and peritoneal dialysis (PD) are the primary means of managing end stage renal disease (ESRD). However, these treatment modalities are associated with the onset of coagulation abnormalities. Effective management of coagulation risk among these patients requires the identification of surrogate markers that provide an early indication of the coagulation abnormalities. The role of sphingolipids in the manifestation and prediction of coagulation abnormalities among dialysis patients have never been investigated. Herein, we report the first instance of an in depth investigation into the sphingolipid changes among ESRD patients undergoing HD and PD. The results reveal distinct differences in terms of perturbations to specific sphingolipid biosynthetic pathways that are highly dependent on the treatment modality. Our studies also demonstrated strong correlation between specific sphingolipids and coagulation parameters, such as HexCer(d18:1/26:0) and maximal amplitude (MA), SM(d18:1/24:1) and tissue factor pathway inhibitor, and sphingosine 1-phosphate d18:1 and FX (Spearman ? of 0.93, 0.89, and -0.89, respectively). Furthermore, our study revealed the potential for using HexCer(d18:1/22:0), HexCer(d18:1/24:0), and HexCer(d18:1/26:0) (r² = 0.71, 0.82, and 0.63, respectively) and coagulation parameter MA (r² = 0.7) for successful diagnosis of differential coagulopathies among ESRD patients undergoing HD, providing an opportunity toward personalized disease management.

SUBMITTER: Contaifer D

PROVIDER: S-EPMC5335589 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Unsupervised analysis of combined lipid and coagulation data reveals coagulopathy subtypes among dialysis patients.

Contaifer Daniel D Carl Daniel E DE Warncke Urszula Osinska UO Martin Erika J EJ Mohammed Bassem M BM Van Tassell Benjamin B Brophy Donald F DF Chalfant Charles E CE Wijesinghe Dayanjan S DS

Journal of lipid research 20161219 3

Hemodialysis (HD) and peritoneal dialysis (PD) are the primary means of managing end stage renal disease (ESRD). However, these treatment modalities are associated with the onset of coagulation abnormalities. Effective management of coagulation risk among these patients requires the identification of surrogate markers that provide an early indication of the coagulation abnormalities. The role of sphingolipids in the manifestation and prediction of coagulation abnormalities among dialysis patient ...[more]

PMID: 27993949

Similar Datasets

Project description:Purpose: To identify novel breast cancer subtypes by extracting quantitative imaging phenotypes of the tumor and surrounding parenchyma and to elucidate the underlying biologic underpinnings and evaluate the prognostic capacity for predicting recurrence-free survival (RFS).Experimental Design: We retrospectively analyzed dynamic contrast-enhanced MRI data of patients from a single-center discovery cohort (n = 60) and an independent multicenter validation cohort (n = 96). Quantitative image features were extracted to characterize tumor morphology, intratumor heterogeneity of contrast agent wash-in/wash-out patterns, and tumor-surrounding parenchyma enhancement. On the basis of these image features, we used unsupervised consensus clustering to identify robust imaging subtypes and evaluated their clinical and biologic relevance. We built a gene expression-based classifier of imaging subtypes and tested their prognostic significance in five additional cohorts with publically available gene expression data but without imaging data (n = 1,160).Results: Three distinct imaging subtypes, that is, homogeneous intratumoral enhancing, minimal parenchymal enhancing, and prominent parenchymal enhancing, were identified and validated. In the discovery cohort, imaging subtypes stratified patients with significantly different 5-year RFS rates of 79.6%, 65.2%, 52.5% (log-rank P = 0.025) and remained as an independent predictor after adjusting for clinicopathologic factors (HR, 2.79; P = 0.016). The prognostic value of imaging subtypes was further validated in five independent gene expression cohorts, with average 5-year RFS rates of 88.1%, 74.0%, 59.5% (log-rank P from <0.0001 to 0.008). Each imaging subtype was associated with specific dysregulated molecular pathways that can be therapeutically targeted.Conclusions: Imaging subtypes provide complimentary value to established histopathologic or molecular subtypes and may help stratify patients with breast cancer. Clin Cancer Res; 23(13); 3334-42. ©2017 AACR.

Project description:Background: Breast cancer (BC) is a highly heterogeneous cancer. The interaction between immune system and BC is complex, widespread yet unclear. In this study, we aimed to reveal the heterogeneity of host systemic immune response to BC and understand the possible mechanisms that may drive the heterogeneity using transcriptomic data from peripheral blood mononuclear cells (PBMCs). Methods: Transcriptome-wide gene expressions of PBMCs in 33 BC patients were generated by RNA sequencing. An unsupervised clustering algorithm was employed to discover PBMC transcriptome subtypes among BC patients. Association analysis between PBMC subtypes and age, clinical stage, abundance of immune cells, and other clinical factors was performed to understand the underlying biological processes that may drive this heterogeneity. Immune gene signature identification and in silico survival analysis were performed to investigate the potential clinical implications of these PBMC subtypes. The findings were validated using the whole blood transcriptomes of an independent cohort. Results: We observed that established BC subtypes were not associated with PBMC gene expression profiles. Instead, we discovered and validated two new BC subtypes using PBMC transcriptome, which have distinct immune cell proportions, especially for lymphocytes (P = 5.22 × 10-12) and neutrophils (P = 1.13 × 10-14). Enrichment analysis of differentially expressed genes revealed that these two subtypes had distinct patterns of immune responses, including osteoclast differentiation and interleukin-10 signaling pathway. We developed two immune gene signatures that can differentiate these two BC PBMC subtypes. Further analysis suggested they had the ability to predict the clinical outcome of BC patients. Conclusions: PBMC transcriptome profiles can classify BC patients into two distinct subtypes. These two subtypes are mainly shaped by different immune cell abundance, which may have implications on clinical outcomes.

Project description:Psychiatric disorders show heterogeneous symptoms and trajectories, with current nosology not accurately reflecting their molecular etiology and the variability and symptomatic overlap within and between diagnostic classes. This heterogeneity impedes timely and targeted treatment. Our study aimed to identify psychiatric patient clusters that share clinical and genetic features and may profit from similar therapies. We used high-dimensional data clustering on deep clinical data to identify transdiagnostic groups in a discovery sample (N = 1250) of healthy controls and patients diagnosed with depression, bipolar disorder, schizophrenia, schizoaffective disorder, and other psychiatric disorders. We observed five diagnostically mixed clusters and ordered them based on severity. The least impaired cluster 0, containing most healthy controls, showed general well-being. Clusters 1-3 differed predominantly regarding levels of maltreatment, depression, daily functioning, and parental bonding. Cluster 4 contained most patients diagnosed with psychotic disorders and exhibited the highest severity in many dimensions, including medication load. Depressed patients were present in all clusters, indicating that we captured different disease stages or subtypes. We replicated all but the smallest cluster 1 in an independent sample (N = 622). Next, we analyzed genetic differences between clusters using polygenic scores (PGS) and the psychiatric family history. These genetic variables differed mainly between clusters 0 and 4 (prediction area under the receiver operating characteristic curve (AUC) = 81%; significant PGS: cross-disorder psychiatric risk, schizophrenia, and educational attainment). Our results confirm that psychiatric disorders consist of heterogeneous subtypes sharing molecular factors and symptoms. The identification of transdiagnostic clusters advances our understanding of the heterogeneity of psychiatric disorders and may support the development of personalized treatments.

Dataset Information

Unsupervised analysis of combined lipid and coagulation data reveals coagulopathy subtypes among dialysis patients.

Publications

Unsupervised analysis of combined lipid and coagulation data reveals coagulopathy subtypes among dialysis patients.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure