Multi-omic analysis identifies metabolic biomarkers for the early detection of breast cancer and therapeutic response prediction.

Reliable blood-based tests for identifying early-stage breast cancer remain elusive. Employing single-cell transcriptomic sequencing analysis, we illustrate a close correlation between nucleotide metabolism in the breast cancer and activation of regulatory T cells (Tregs) in the tumor microenvironment, which shows distinctions between subtypes of patients with triple-negative breast cancer (TNBC) and non-TNBC, and is likely to impact cancer prognosis through the A2AR-Treg pathway. Combining machine learning with absolute quantitative metabolomics, we have established an effective approach to the early detection of breast cancer, utilizing a four-metabolite panel including inosine and uridine.

Evaluation of Small Molecules in Blank EDTA Plasma Tubes and Optimization of Metabolomic Workflow for Biomarker Studies Using Plasma Samples.

As the first step of metabolomic analysis in biomarker identification studies, various types of blood collection tubes are used in clinical practice. However, little attention is paid to potential contamination caused by the blank tube itself. Here, we evaluated small molecules in blank EDTA plasma tubes through LC-MS-based untargeted metabolomic analysis and identified small molecules with markedly varied levels among different production batches or specifications.

Proteomics and metabolomics profiling reveal panels of circulating diagnostic biomarkers and molecular subtypes in stable COPD.

Background: Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease with high morbidity and mortality, especially in advanced patients. We aimed to develop multi-omics panels of biomarkers for the diagnosis and explore its molecular subtypes.

Methods: A total of 40 stable patients with advanced COPD and 40 controls were enrolled in the study.

Applications of liquid chromatography-mass spectrometry based metabolomics in predictive and personalized medicine.

Metabolomics is a fast-developing technique used in biomedical researches focusing on pathological mechanism illustration or novel biomarker development for diseases. The ability of simultaneously quantifying thousands of metabolites in samples makes metabolomics a promising technique in predictive or personalized medicine-oriented researches and applications. Liquid chromatography-mass spectrometry is the most widely employed analytical strategy for metabolomics.

Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis.

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway.

Comprehensive plasma metabolomic and lipidomic analyses reveal potential biomarkers for heart failure.

Heart failure is a syndrome with symptoms or signs caused by cardiac dysfunction. In clinic, four stages (A, B, C, and D) were used to describe heart failure progression. This study was aimed to explore plasma metabolomic and lipidomic profiles in different HF stages to identify potential biomarkers.

Identification of diagnostic markers and lipid dysregulation in oesophageal squamous cell carcinoma through lipidomic analysis and machine learning.

Background: Oesophageal cancer (EC) ranks high in both morbidity and mortality. A non-invasive and high-sensitivity diagnostic approach is necessary to improve the prognosis of EC patients.

Methods: A total of 525 serum samples were subjected to lipidomic analysis.

Proteomic Analyses Identify Differentially Expressed Proteins and Pathways Between Low-Risk and High-Risk Subtypes of Early-Stage Lung Adenocarcinoma and Their Prognostic Impacts.

The histopathological subtype of lung adenocarcinoma (LUAD) is closely associated with prognosis. Micropapillary or solid predominant LUAD tends to relapse after surgery at an early stage, whereas lepidic pattern shows a favorable outcome. However, the molecular mechanism underlying this phenomenon remains unknown.

Integrated proteomics and phosphoproteomics reveal perturbed regulative pathways in pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) has a dismal prognosis largely owing to its inefficient diagnosis, rapid progress, and tenacious drug resistance. Here, we aimed to analyze the expressive patterns of proteins and phosphorylation in PDAC tissue samples and compare them to normal pancreatic tissue to investigate the underlying mechanisms and to reveal potential protein targets for diagnosis and drug development. Liquid chromatography coupled to mass spectrometry (LC-MS) based proteomics and phosphoproteomics analyses were performed using 20 pairs of patient-derived PDAC tissue and normal pancreatic tissue samples.

Proteomic analysis reveals distinctive protein expression patterns of thrombus in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a type of malignant tumor of the urinary system. The renal vein or vena cava thrombus can be found in a subset of ccRCC patients in whom it leads to worse prognosis. However, the protein expression profile and molecular features of ccRCC thrombus remain largely unclear.

Plasma Metabolomics and Lipidomics Reveal Perturbed Metabolites in Different Disease Stages of Chronic Obstructive Pulmonary Disease.

Background: Chronic obstructive pulmonary disease (COPD) is a common disease characterized by persistent respiratory symptoms and airflow restriction. It is usually manifested as airway and/or alveolar abnormalities caused by significant exposure to harmful particulates or gases.

Objective: We aim to explore plasma metabolomic changes in the acute exacerbation stage of COPD (AECOPD) and stable stage of COPD (Stable COPD) to identify potential biomarkers for diagnosis or prognosis in clinical practice.

Proteomic Analysis of Preoperative CSF Reveals Risk Biomarkers of Postoperative Delirium.

To analyze the proteome of preoperative cerebrospinal fluid (CSF) in older orthopedic patients with or without postoperative delirium (POD) using untargeted proteomics. A prospective cohort study was conducted. Eighty hip fracture patients aged ≥65 years were recruited.

PTENα regulates endocytosis and modulates olfactory function.

Phosphatase and tensin homolog deleted on chromosome 10 (PTEN) α is the first identified isoform of the well-known tumor suppressor PTEN. PTENα has an evolutionarily conserved 173-aa N terminus compared with canonical PTEN. Recently, PTENα has been shown to play roles in multiple biologic processes including learning and memory, cardiac homeostasis, and antiviral immunity.

Comprehensive metabolomic and proteomic analyses reveal candidate biomarkers and related metabolic networks in atrial fibrillation.

Introduction: Atrial fibrillation (AF) is an abnormal heart rhythm characterized by an irregular beating of the atria and is associated with an increased risk of heart failure, dementia, and stroke. Currently, the perturbation of plasma content due to AF disease onset is not well known.

Objectives: To investigate dysregulated molecules in blood plasma of untreated AF patients, with the goal of identifying biomarkers for disease screening and pathological studies.

Metabolomics identifies serum and exosomes metabolite markers of pancreatic cancer.

Introduction: Pancreatic cancer (PC) is one of the most aggressive malignancies, and it's difficult to diagnosis PC at an early stage, which leads to the poor prognosis of PC.

Objectives: To identifiy the possible prognosis or dignosis metabolite biomarkers in the serum exosome of PC patients.

Methods: We employed LC-DDA-MS based untargeted lipidomic analysis to search for potential candidate biomarkers in the serum exosome of PC patients.

Discovery of 2,4-diarylaminopyrimidine derivatives bearing dithiocarbamate moiety as novel FAK inhibitors with antitumor and anti-angiogenesis activities.

A series of 2,4-diarylaminopyrimidine derivatives containing dithiocarbamate moiety were designed by molecular hybridization strategy and synthesized for screening as inhibitors of focal adhesion kinase (FAK). Most of these compounds exhibit significant antiproliferative activities on human cancer cell lines expressing high levels of FAK at nanomolar concentrations. The compound 14z was identified as the most potent FAK inhibitor among these candidates.

Unbiased lipidomic profiling reveals metabolomic changes during the onset and antipsychotics treatment of schizophrenia disease.

Introduction: Schizophrenia (SCH) is one of the most common psychiatric disorders, which involves impairments in motivation and cognition. The pathological mechanisms underlying SCH are still unknown, and no effective therapies can prevent or treat perfectly the cognitive impairments and deficit symptoms caused by SCH.

Objectives: We aimed to find the lipid expression change in plasma that underlie SCH onset and antipsychotics treatment.

Use of Liquid Chromatography-Mass Spectrometry-Based Metabolomics to Identify Biomarkers of Tuberculosis.

Liquid chromatography-mass spectrometry (LC-MS) based metabolomics has proven to be a powerful analytical tool for biomarker screening. Here we describe two workflows which employ untargeted metabolomics to study serum biomarkers in tuberculosis patients. Expression profiles for samples of hydrophilic metabolites and hydrophobic metabolites (lipids) may be obtained by this method.

Lipidomic profiling of plasma samples from patients with mitochondrial disease.

Mitochondrial disease (MD) is a rare mitochondrial respiratory chain disorder with a high mortality and extremely challenging to treat. Although genomic, transcriptomic, and proteomic analyses have been performed to investigate the pathogenesis of MD, the role of metabolomics in MD, particularly of lipidomics remains unclear. This study was undertaken to identify potential lipid biomarkers of MD.

Lipidomic analysis of serum samples from migraine patients.

Background: Migraine is a prevalent, disabling type of primary headache disorder associated with a high socioeconomic burden. The clinical management of migraine is challenging. This study was to identify potential serum lipidomic biomarkers of migraine.

Low levels of serum serotonin and amino acids identified in migraine patients.

Migraine is a highly disabling primary headache associated with a high socioeconomic burden and a generally high prevalence. The clinical management of migraine remains a challenge. This study was undertaken to identify potential serum biomarkers of migraine.

Data-Independent Acquisition-Based Quantitative Proteomic Analysis Reveals Potential Biomarkers of Kidney Cancer.

Purpose: Renal cell carcinoma (RCC) is a malignant and metastatic cancer with 95% mortality, and clear cell RCC (ccRCC) is the most observed among the five major subtypes of RCC. Specific biomarkers that can distinguish cancer tissues from adjacent normal tissues should be developed to diagnose this disease in early stages and conduct a reliable prognostic evaluation.

Experimental Design: Data-independent acquisition (DIA) strategy has been widely employed in proteomic analysis because of various advantages, including enhanced protein coverage and reliable data acquisition.

Serum proteomics study reveals candidate biomarkers for systemic lupus erythematosus.

Systemic lupus erythematosus (SLE) is an autoimmune disease which is characterized by the presence of autoantibodies. It will be helpful if specific serum biomarkers can be used for monitoring the disease activity as well as differentiating SLE from other diseases. For this purpose, we used a label free-based two dimensional liquid chromatography mass spectrometry platform to analyze serum samples from SLE patients in active or inactivestage.

Workflow development for targeted lipidomic quantification using parallel reaction monitoring on a quadrupole-time of flight mass spectrometry.

Advances in high-resolution mass spectrometers with faster scanning capabilities and higher sensitivities have expanded these instruments' functionality beyond traditional data-dependent acquisition in targeted metabolomics. Apart from the traditional multiple reaction monitoring strategy, the parallel reaction monitoring (PRM) strategy is also used for targeted metabolomics quantification. The high resolution and mass accuracy of full-scan (MS1) and tandem mass spectrometry (MS/MS) scan result in sufficient selectivity by monitoring all MS/MS fragment ions for each target precursor and simultaneously providing flexibility in assay method construction and post-acquisition data analysis.