*Number of genes that were differentially expressed in any one of the disease tissues at the mRNA level (fpf≤0.05, RankProd R package) with detectable protein abundance in the biofluid proteome database (see Methods).*Number of known diagnostic protein biomarkers in clinical and preclinical studies from the GVK BIO Online Biomarker Database (GOBIOM).**Number of correctly predicted diagnostic protein biomarkers.$P values were calculated to evaluate whether known protein biomarkers were significantly enriched in our predicted genes using Fisher's exact test.

THIS RESOURCE IS NO LONGER IN SERVICE, documented July 22, 2016. A database of biomarkers for diagnosis, detection, protection and characterization of infectious diseases. It provides information on pathogens and biomarkers, such as nucleic acids, proteins, carbohydrates, and immune epitopes. The site links to global news and life science journal search engines as well as provides links to tool functions for gene sequence and protein structure analyses.

The promises of data-independent acquisition (DIA) strategies are a comprehensive and reproducible digital qualitative and quantitative record of the proteins present in a sample. We developed a fast and robust DIA method for comprehensive mapping of the urinary proteome that enables large scale urine proteomics studies. Compared to a data-dependent acquisition (DDA) experiments, our DIA assay doubled the number of identified peptides and proteins per sample at half the coefficients of variation observed for DDA data (DIA = ∼8%; DDA = ∼16%). We also tested different spectral libraries and their effects on overall protein and peptide identifications and their reproducibilities, which provided clear evidence that sample type-specific spectral libraries are preferred for reliable data analysis. To show applicability for biomarker discovery experiments, we analyzed a sample set of 87 urine samples from children seen in the emergency department with abdominal pain. The whole set was analyzed with high proteome coverage (∼1300 proteins/sample) in less than 4 days. The data set revealed excellent biomarker candidates for ovarian cyst and urinary tract infection. The improved throughput and quantitative performance of our optimized DIA workflow allow for the efficient simultaneous discovery and verification of biomarker candidates without the requirement for an early bias toward selected proteins.

List of the proteomics datasets used in this study.

An improved data analysis method is described for rapid identification of intact microorganisms from MALDI-TOF-MS data. The method makes no use of mass spectral fingerprints. Instead, a microorganism database is automatically generated that contains biomarker masses derived from ribosomal protein sequences and a model of N-terminal Met loss. We quantitatively validate the method via a blind study that seeks to identify microorganisms with known ribosomal protein sequences. We also include in the database microorganisms with incompletely known sets of ribosomal proteins to test the specificity of the method. With an optimal MALDI protocol, and at the 95% confidence level, microorganisms represented in the database with 20 or more biomarkers (i.e., those with complete or nearly completely sequenced genomes) are correctly identified from their spectra 100% of the time, with no incorrect identifications. Microorganisms with seven or less biomarkers (i.e., incompletely sequenced genomes) are either not identified or misidentified. Robustness with respect to variations in sample preparation protocol and mass analysis protocol is demonstrated by collecting data with two different matrixes and under two different ion-mode configurations. Statistical analysis suggests that, even without further improvement, the method described here would successfully scale up to microorganism databases with roughly 1000 microorganisms. The results demonstrate that microorganism identification based on proteome data and modeling can perform as well as methods based on mass spectral fingerprinting.

Database containing several body fluid proteomes, including plasma, urine, and cerebrospinal fluid. Cell lines have been mapped to a depth of several thousand proteins and the red blood cell proteome has also been analyzed in depth. The liver proteome is represented with 3200 proteins. By employing high resolution MS and stringent validation criteria, false positive identification rates in MAPU are lower than 1:1000. Thus MAPU datasets can serve as reference proteomes in biomarker discovery. MAPU contains the peptides identifying each protein, measured masses, scores and intensities using a clickable interface of cell or body parts. Proteome data can be queried across proteomes by protein name, accession number, sequence similarity, peptide sequence and annotation information. More than 4500 mouse and 2500 human proteins have already been identified in at least one proteome. Basic annotation information and links to other public databases are provided in MAPU and we plan to add further analysis tools.

Background: Alzheimer's disease (AD) is the major cause of dementia in population aged over 65 years, accounting up to 70% dementia cases. However, validated peripheral biomarkers for AD diagnosis are not available up to present. In this study, we adopted a new strategy of combination of computational prediction and experimental validation to identify blood protein biomarkers for AD.Methods: First, we collected tissue-based gene expression data of AD patients and healthy controls from GEO database. Second, we analyzed these data and identified differentially expressed genes for AD. Third, we applied a blood-secretory protein prediction program on these genes and predicted AD-related proteins in blood. Finally, we collected blood samples of AD patients and healthy controls to validate the potential AD biomarkers by using ELISA experiments and Western blot analyses.Results: A total of 2754 genes were identified to express differentially in brain tissues of AD, among which 296 genes were predicted to encode AD-related blood-secretory proteins. After careful analysis and literature survey on these predicted blood-secretory proteins, ten proteins were considered as potential AD biomarkers, five of which were experimentally verified with significant change in blood samples of AD vs. controls by ELISA, including GSN, BDNF, TIMP1, VLDLR, and APLP2. ROC analyses showed that VLDLR and TIMP1 had excellent performance in distinguishing AD patients from controls (area under the curve, AUC = 0.932 and 0.903, respectively). Further validation of VLDLR and TIMP1 by Western blot analyses has confirmed the results obtained in ELISA experiments.Conclusion: VLDLR and TIMP1 had better discriminative abilities between ADs and controls, and might serve as potential blood biomarkers for AD. To our knowledge, this is the first time to identify blood protein biomarkers for AD through combination of computational prediction and experimental validation. In addition, VLDLR was first reported here as potential blood protein biomarker for AD. Thus, our findings might provide important information for AD diagnosis and therapies.

Single cell genomics enables characterization of disease specific cell states, while improvements in mass spectrometry workflows bring the clinical use of body fluid proteomics within reach. However, the correspondence of peripheral protein signatures to changes in cell state in diseased organs is currently unknown. Here, we leverage single cell RNA-seq and proteomics from large patient cohorts of pulmonary fibrosis to establish that predictive protein signatures in body fluids correspond to specific cellular changes in the lung. We determined transcriptional changes in 45 cell types across three patient cohorts and quantified bronchoalveolar lavage fluid and plasma proteins to discover protein signatures and associated cell state changes that were linked to diagnosis, lung function, smoking and injury status. Altered expression of the novel marker of lung health CRTAC1 in alveolar epithelium is robustly reported in patient plasma. With further improvements of this concept and deeper coverage of plasma proteomes, we envision future longitudinal profiling of body fluid signatures coupled to machine learning for non-invasive prediction and monitoring of pathological cell state changes in patient organs.

Fecal immunochemical tests (FIT) detecting hemoglobin in stool are widely used for non-invasive colorectal cancer (CRC) screening, but their sensitivity leaves room for improvement. Our aim is to identify novel protein biomarkers in stool that outperform or complement hemoglobin in detecting CRCs and advanced adenomas (AAs). Stool samples (one series of 12 CRCs and 10 controls, and a second series of 81 CRCs, 40 AAs, 43 non-advanced adenomas and 129 controls) were analyzed by mass-spectrometry and searched for human proteins. Classification and regression tree and logistic regression analyses were performed to identify protein combinations that differentiated CRCs and/or AAs from controls. Antibody-based assays for four selected proteins were performed on an independent series of FIT samples (14 CRCs, 16 AAs, 18 non-advanced adenomas and 24 controls) Results: In total, 834 human proteins were identified, of which 29 were significantly enriched in CRCs versus controls in both stool sample series. Combinations of four proteins reached sensitivities of 80% and 45% for detecting CRCs and AAs, at 95% specificity, which was higher than hemoglobin alone.

Abstract Background There is an urgent need for new, accurate, rapid, and affordable tuberculosis (TB) diagnostic tests. The aim of the present study was to use mass spectrometry to identify new preliminary candidate TB diagnostic protein biomarkers in saliva obtained from individuals with TB, and patients with other respiratory diseases (ORD). Methods Saliva samples were collected from 22 individuals who self-presented with symptoms suggestive of TB as part of a larger TB biomarker project. Purified salivary proteins were subjected to tryptic digestion peptides were analysed using a QExactive Orbitrap Mass Spectrometer. Identified proteins were subjected to gene ontology and ingenuity pathway analysis for functional enrichment analysis. Results 26 of the 652 identified proteins significantly discriminated individuals with TB from those with ORD after Benjamini Hochberg correction (5% FDR), with five of these proteins diagnosing TB with an AUC ≥ 0.80. A 5-protein biosignature comprising of P01011, Q8NCW5, P28072, A0A2Q2TTZ9, and Q99574 diagnosed TB with an AUC of 1.00 (95% CI, 1.00-1.00), sensitivity of 100% (95% CI, 76.2-100%) and specificity of 90.9% (95% CI, 58.7-99.8%) after leave-one-out cross validation. Conclusions We identified novel candidate salivary protein biomarkers and biosignatures with strong potential as TB diagnostic candidates. Our results are preliminary and require validation in larger studies.

Database that provides access to biological systems and their component genes, proteins, and small molecules, as well as literature describing those biosystems and other related data throughout Entrez. A biosystem, or biological system, is a group of molecules that interact directly or indirectly, where the grouping is relevant to the characterization of living matter. BioSystem records list and categorize components, such as the genes, proteins, and small molecules involved in a biological system. The companion FLink tool, in turn, allows you to input a list of proteins, genes, or small molecules and retrieve a ranked list of biosystems. A number of databases provide diagrams showing the components and products of biological pathways along with corresponding annotations and links to literature. This database was developed as a complementary project to (1) serve as a centralized repository of data; (2) connect the biosystem records with associated literature, molecular, and chemical data throughout the Entrez system; and (3) facilitate computation on biosystems data. The NCBI BioSystems Database currently contains records from several source databases: KEGG, BioCyc (including its Tier 1 EcoCyc and MetaCyc databases, and its Tier 2 databases), Reactome, the National Cancer Institute's Pathway Interaction Database, WikiPathways, and Gene Ontology (GO). It includes several types of records such as pathways, structural complexes, and functional sets, and is desiged to accomodate other record types, such as diseases, as data become available. Through these collaborations, the BioSystems database facilitates access to, and provides the ability to compute on, a wide range of biosystems data. If you are interested in depositing data into the BioSystems database, please contact them.

The global biomarkers market for diagnosing cancer is experiencing robust growth, projected to reach $48.42 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.8% from 2025 to 2033. This significant expansion is driven by several key factors. The increasing prevalence of cancer globally fuels the demand for accurate and early diagnostic tools. Advancements in biotechnology and genomic sequencing technologies are leading to the discovery and development of more sensitive and specific biomarkers, enabling earlier detection and personalized treatment strategies. Furthermore, the growing adoption of liquid biopsies, which offer a minimally invasive approach to biomarker detection, is significantly impacting market growth. The market is segmented by biomarker type (genetic, protein, glyco-biomarkers) and application (diagnostics, drug discovery and development). The diagnostics segment is currently the largest, driven by the need for improved diagnostic accuracy and early disease detection. The drug discovery and development segment is expected to exhibit strong growth, as biomarker data is increasingly used for targeted therapy development and clinical trial optimization. Key players in this competitive market include established pharmaceutical and diagnostic companies such as Bristol-Myers Squibb, QIAGEN, Abbott Laboratories, and Roche Diagnostics, alongside innovative biotechnology firms. These companies are investing heavily in R&D to expand their biomarker portfolios and strengthen their market positions. The geographic distribution of the market reveals a strong presence in North America and Europe, driven by advanced healthcare infrastructure and high healthcare expenditure. However, the Asia-Pacific region is expected to witness substantial growth in the coming years due to rising cancer incidence, increasing healthcare awareness, and expanding diagnostic capabilities. The market's future trajectory is positive, influenced by ongoing research and development, technological advancements, and the increasing adoption of personalized medicine approaches. The continued focus on improving diagnostic accuracy, reducing healthcare costs, and developing novel therapeutic strategies ensures that the biomarker market for cancer diagnosis will remain a dynamic and rapidly evolving space.

Analysis of the blood proteome allows identification of proteins related to changes upon certain physiological conditions. The pathophysiology of necrotic enteritis (NE) has been extensively studied. While intestinal changes have been very well documented, data addressing NE-induced alterations in the blood proteome are scant, although these might have merit in diagnostics. Considering recent technological advancements in proteomics and pressing need for tools to access gut health, the current study employs mass-spectrometry (MS) proteomics to identify biomarkers for gastrointestinal health of broilers chickens. Untargeted proteomics investigation was conducted on chicken blood plasma in animals under NE challenge. Two MS-strategies were used for analysis: DDA (Data Dependent Acquisition) and DIA (Data Independent Acquisition). DIA showed superior completeness and quantification of the acquired data, despite high degree of agreement in identification and quantification between both approaches. Identified differentially expressed proteins shared by DDA and DIA represent responses of animals to infection and may serve as potential biomarkers. Experimental validation through ELISA immunoassays for selected regulated proteins confirmed medium-to-high levels of inter-protein correlation, along with positive correlation between the methods. Functional analysis showed enhanced defense, immune, and acute phase responses, alongside reduced signaling, regulatory, and cell adhesion activities in infected animals.

Summary statistics from the GWAS on circulating plasma protein levels.

Samples for TMA EdgeSeq, TCGA RNA-seq and CCLE RNA-seq comparison.

Summary statistics from an association study comparing the concentration levels of 425 plasma proteins with the genome-wide genetic variation.

Statistically significant associations from this data has previously been published in:

"Systemic and specific effects of antihypertensive and lipid-lowering medication on plasma protein biomarkers for cardiovascular diseases" by Enroth et al in Scientific Reports 2018 (https://www.nature.com/articles/s41598-018-23860-y, also included here as "s41598-018-23860-y.pdf")

Each library file contains 24 files (chr1-22, X, M) with the associations of genetic markers to the protein given in the name of the library file. A list of matches between protein identifiers can be found in the "proteinInfo.txt" file. All coordinates are given in the hg19 assembly.

Database that attempts to consolidate information on known clinical and selected set of pre-clinical biomarkers into single resource. Database includes five major types of biomarkers (condition specific, protein, chemical, karyotypic and genetic) and six biomarker categories (diagnostic, risk, prognostic, safety, monitoring, and response). Provides information such as: biomarker names and synonyms, associated conditions or pathologies, detailed disease descriptions, detailed biomarker descriptions, biomarker specificity, sensitivity and ROC curves, standard reference values (for protein and chemical markers), variants (for SNP or genetic markers), sequence information (for genetic and protein markers), molecular 2D and 3D structures (for protein and chemical markers), tissue or biofluid sources (for protein and chemical markers), chromosomal location and structure (for genetic and karyotype markers), clinical approval status and relevant literature references. Users can browse the data by conditions, condition categories, biomarker types, biomarker categories or search by sequence similarity through the advanced search function.

Adipose tissue is recognized as a major source of systemic inflammation with age, driving age-related tissue dysfunction and pathogenesis. Macrophages (Mϕ) are central to these changes yet adipose tissue Mφ (ATMs) from aged mice remain poorly characterized. To identify biomarkers underlying changes in aged adipose tissue, we performed an unbiased RNA-seq analysis of ATMs from young (10-week old) and healthy aged (80-week old) mice. One of the genes identified, V-set immunoglobulin-domain-containing 4 (VSIG4/CRIg), encodes a Mφassociated complement receptor and B7 family-related immune checkpoint protein. Here, we demonstrate that Vsig4 expression is highly upregulated with age in perigonadal white adipose tissue (gWAT) in two mouse strains (inbred C57BL/6J and outbred NIH Swiss) independent of gender. The accumulation of VSIG4 was mainly associated with a 4-fold increase in the proportion of VSIG4+ ATMs (13% to 52%). In a longitudinal study, VSIG4 expression in gWAT showed a strong corr...

This dataset contains data for CBC counts and absolute protein abundance measurements from ELISA experiments.

Extracellular vesicles (EVs) operate as chemical messengers that facilitate intercellular communication. Emerging evidence has demonstrated that lung tissue-derived EVs play pivotal roles in pulmonary physiological processes and have potential as biomarkers and therapeutics for lung diseases. Multiple methods have been proposed for the isolation of lung tissue-derived EVs. However, the effects of different tissue pre-treatments on lung EV isolation and subsequent disease biomarker discovery have not yet been comprehensively investigated. In this study, we compared the physical characteristics, recovery yields, and protein compositions of EVs isolated from lung tissues using three methods based on different tissue dissociation principles. Methodologically, the beneficial roles of blood perfusion and gentle meshing were emphasized based on their impact on EV yield and purity. These results demonstrate that different methods enrich distinct subpopulations of EVs that exhibit significant differences in their protein cargo and surface properties. These disparities directly affect the diagnostic detection of marker proteins related to lung diseases, including lung tumors, asthma, and pulmonary fibrosis. Collectively, these findings highlight the variations in EV characteristics resulting from the applied approaches, and offer compelling suggestions for guiding researchers in selecting a suitable isolation method based on downstream functional studies and clinical applications.