Project description:BackgroundMost malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs.MethodsUntargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers.FindingsA panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866.InterpretationThe present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput.FundingA full list of funding bodies that contributed to this study can be found in the Acknowledgments section.
Project description:Muscle invasive bladder cancer (MIBC) is a heterogeneous disease with a high recurrence rate and poor clinical outcomes. Molecular subtype provides a new framework for the study of MIBC heterogeneity. Clinically, MIBC can be classified as basal and luminal subtypes, they display different clinical and pathological characteristics, but the molecular mechanism is still unclear. Lipidomic and metabolomic molecules have recently been considered to play an important role in the genesis and development of tumors, especially as potential biomarkers. Their different expression profiles in basal and luminal subtypes provide clues for the molecular mechanism of basal and luminal subtypes and the discovery of new biomarkers. Herein, we stratified MIBC patients into basal and luminal subtype using a MIBC classifier based on transcriptome expression profiles. We qualitatively and quantitatively analyzed the lipids and metabolites of basal and luminal MIBC subtypes, and identified differential lipid and metabolite profiles of them. Our results suggest that free fatty acids (FFA) and sulfatides (SL), which are closely associated with immune and stromal cell types, can contribute to the diagnosis of basal and luminal subtypes of MIBC. Moreover, we showe that glycerophosphocholine (GCP)/imidazoles and nucleosides/imidazoles ratios can accurately distinguish the basal and luminal tumors. Overall, by integrating transcriptomic, lipidomic, and metabolomic data, our study reveals specific biomarkers to differentially diagnose basal and luminal MIBC subtypes and may provide a basis for precision therapy of MIBC.
Project description:We investigate the underlying mechanism of the CP inhibition on F. prausnitzii by analysing its effects at the transcriptomic and lipidomic levels.
Project description:Recent studies have shown that microRNAs (miRNAs) are implicated in the development of postmenopausal osteoporosis, implying potential biomarkers. We performed a microarray-based expression scanning to search for potential circulating miRNA biomarkers for postmenopausal osteoporosis as whole blood obtianed from patients was used.
Project description:Integrative Transcriptomic, Lipidomic, and Metabolomic Analysis Reveals Potential Biomarkers of Basal and Luminal Muscle Invasive Bladder Cancer Subtypes
Project description:Major depressive disorder (MDD) is a complex condition with unclear pathophysiology. Molecular disruptions within the periphery and limbic brain regions contribute to depression symptomatology. Here, we utilized a mouse chronic stress model of MDD and performed metabolomic, lipidomic, and proteomic profiling on serum plus several brain regions (ventral hippocampus, nucleus accumbens, and prefrontal cortex) of susceptible, resilient, and unstressed control mice. Proteomic analysis identified three serum proteins reduced in susceptible animals; lipidomic analysis detected differences in lipid species between resilient and susceptible animals in serum and brain; and metabolomic analysis revealed pathway dysfunctions of purine metabolism, beta oxidation, and antioxidants, which were differentially associated with stress susceptibility vs resilience by brain region. Antidepressant treatment ameliorated MDD-like behaviors and affected key metabolites within outlined networks, most dramatically in the ventral hippocampus. This work presents a resource for chronic stressinduced, tissue-specific changes in proteins, lipids, and metabolites and illuminates how molecular dysfunctions contribute to individual differences in stress sensitivity
Project description:Using an experimental TBI rat model of mild/moderate Controlled Cortical Impact (CCI) injury, we combined large-scale proteomics identification and relative quantification using Spatially-Resolved Microproteomics with MALDI MS Imaging of Lipids. Spatially by studying different regions in the brain post injury in a coronal view, with main focus on the injury site itself. Temporally by studying the acute and subacute phase post injury, including injured rat brains at 1 day, 3 days, 7 days, and 10 days post injury. Direct on-tissue micro-digestion followed by micoextraction from 1 mm2 surface area within the injured cortical tissue were subjected to LC-MS & MS/MS analysis using HR MS. In addition, several identified potential biomarkers within our study were used to stimulate dorsal root ganglion (DRG), astrocyte, and macrophage cell lines to obtain a better understanding of their role and contribution in the injury.
Project description:The aim of this study is to prospectively determine the incidence of brain metastases in metastatic colorectal cancer patients using systematic annual screening by MRI.