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At least 307 records · Page 17Linked to original sources
PubMed2004

Protein chip array profiling analysis in patients with severe acute respiratory syndrome identified serum amyloid a protein as a biomarker potentially useful in monitoring the extent of pneumonia.

BACKGROUND: A new strain of coronavirus (CoV) has caused an outbreak of severe acute respiratory syndrome (SARS), with 8098 individuals being infected and 774 deaths worldwide. We carried out protein chip array profiling analysis in an attempt to identify biomarkers that might be useful in monitoring the clinical course of SARS patients. METHODS: We performed surface-enhanced laser desorption ionization time-of-flight mass spectrometry on 89 sera collected from 28 SARS patients, 72 sera from 51 control patients with various viral or bacterial infections, and 10 sera from apparently healthy individuals. RESULTS: Nine significantly increased and three significantly decreased serum biomarkers were discovered in the SARS patients compared with the controls. Among these biomarkers, one (11,695 Da) was identified to be serum amyloid A (SAA) protein by peptide mapping and tandem mass spectrometric analysis. When we monitored the SAA concentrations longitudinally in 45 sera from four SARS patients, we found a good correlation of SAA concentration with the extent of pneumonia as assessed by a serial chest x-ray opacity score. Increased SAA occurred in three of four patients at the time of extensive pneumonia as indicated by high x-ray scores. Over the course of gradual recovery in two patients, as assessed clinically and radiologically, SAA concentrations gradually decreased. In the third patient, the concentrations were initially increased, but were further increased with superimposed multiple bacterial infections. SAA was not markedly increased in the fourth patient, who had low x-ray scores and whose clinical course was relatively mild. CONCLUSIONS: Protein chip array profiling analysis could be potentially useful in monitoring the severity of disease in SARS patients.

Biomarkers↗
PubMed2025

Identification and analysis of metabolic reprogramming-related genes in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is notorious for its rapid progression, tendency to metastasize, high recurrence rates, dismal outcomes, and limited treatment options, underscoring the urgent need to uncover new biomarkers and molecular pathways to enhance diagnosis, prognosis, and therapeutic strategies. Metabolic reprogramming continues to play a role throughout the life cycle of cancer, evolving and adapting. In this study, we aimed to identify specific genes associated with metabolic reprogramming in TNBC, which can potentially become unique biomarkers of this cancer. TNBC datasets retrieved from the Gene Expression Omnibus were employed to pinpoint genes exhibiting altered expression linked to tumor metabolic reprogramming. Key genes were accurately screened through machine learning algorithms, and then externally verified using the TBNC dataset based on the Cancer Genome Atlas database. Finally, immunohistochemical methods were used to clinically confirm the differential expression and trends of these key genes. Our analysis accurately identified four genes-CLEC7A, IRS1, RSPO3, and ALB-that are closely correlated with the metabolic reprogramming characteristics of cancer, and could be regarded as innovative biomarkers for TNBC. This opens a new avenue for further investigation into the mechanisms of metabolic reprogramming in TNBC and new treatment strategies.

Humans↗
PubMed2005

Practical proteomic biomarker discovery: taking a step back to leap forward.

There is a pressing need for radically improved proteomic screening methods that allow for earlier diagnosis of disease, for systematic monitoring of physiological responses and for uncovering the fundamental mechanisms of drug action. Recent developments in proteomic technology offer tremendous, yet untapped, potential to yield novel biomarkers that are translatable to routine clinical use. Despite the significant conceptual promise of comparative proteomic profiling as a research platform for biomarker discovery, however, major hurdles remain for practical and clinical implementation. In particular, there is growing recognition that rigorous experimental design principles are urgently required to validate conclusively the unproven methodologies currently being touted. Debate and confusion persist about where the burden of proof lies: statistically, biologically or clinically? Moreover, there is no consensus about what constitutes a meaningful benchmark. An important question is how to achieve a scientifically rigorous, and therefore convincing, proof-of-concept that can be accepted by the field. Key analytical challenges related to these issues that must be addressed by the burgeoning biomarker community are discussed here.

Animals↗
PubMed2006

Differential gene expression profiles between tumor biopsies and short-term primary cultures of ovarian serous carcinomas: identification of novel molecular biomarkers for early diagnosis and therapy.

OBJECTIVE: To identify novel molecular biomarkers useful for the early diagnosis and therapy of ovarian cancer by gene expression profiling. To compare the genetic fingerprints of flash-frozen ovarian serous carcinomas to those of matched highly purified primary tumor cell cultures. METHODS: Gene expression profiles of 19 flash-frozen ovarian serous papillary carcinoma (OSPC) were analyzed and compared to 15 controls (highly purified human ovarian surface epithelium short-term cultures, HOSE) using oligonucleotide microarrays complementary to >14,500 human genes. In addition, gene expression profiling of 5 highly purified primary OSPC cultured in vitro for less than 2 weeks was compared to flash-frozen ovarian carcinoma biopsies obtained from matched samples. Quantitative RT-PCR and IHC staining techniques were used to validate microarray data at RNA and protein levels for some of the differentially expressed genes. RESULTS: Unsupervised analysis of gene expression data readily distinguished normal tissue from flash-frozen OSPC and identified 901 and 557 genes that exhibited >3-fold up-regulation or down-regulation, respectively, in OSPC when compared to HOSE. Mammaglobin 2, an ovarian secreted protein, was identified as the top differentially expressed gene in OSPC (19 out 19 OSPC versus 0 out of 15 HOSE) with over 827-fold up-regulation relative to HOSE. The claudin and kallikrein family of proteins including the clostridium perfringens enterotoxin receptors claudin 3 and 4, kallikreins 6, 7, 8, 10, 11 and the immunomodulatory molecule B7-H4 were found among the most highly overexpressed genes in OSPC when compared to HOSE. Genetic fingerprints of flash-frozen OSPC were found to have high correlation with those of purified primary OSPC short-term in vitro cultures with only 31 out of 8,637 genes (0.35%) differentially expressed between the two groups. CONCLUSIONS: Short-term in vitro culture of primary ovarian carcinomas may greatly increase the purity of ovarian tumor RNA available for gene expression profiling without causing major alteration in OSPC fingerprints. Mammaglobin 2, kallikreins 6, 7, 8, 10, 11, claudin 3 and 4 and B7-H4 gene expression products represent candidate biomarkers endowed with great potential for early screening and therapy of OSPC patients.

Adult↗
PubMed2006

Systematical evaluation of the effects of sample collection procedures on low-molecular-weight serum/plasma proteome profiling.

Blood is an ideal source for biomarker discovery. However, little has been done to address the effects of sampling, handling and storage procedures on serum/plasma proteomes. We used magnetic bead-based MALDI-TOF MS to systematically evaluate the influence of each procedure on low-molecular-weight serum/plasma proteome profiling on the basis of the whole spectra. We found that sampling procedures, including the selection of blood collection tubes and anticoagulants, variations in clotting time or time lag before centrifugation, and hemolysis, displayed significant effects on the proteomes. Moreover, serum and plasma were mutually incompatible for proteome comparison. By contrast, overnight fasting, handling procedures, including centrifugation speeds (1500 x g vs. 3000 x g) or time (15 min vs. 30 min), and storage conditions, such as at 4 degrees C or 25 degrees C for up to 24 h or at -80 degrees C for up to 3 months, and repeated freeze/thaw of up to ten cycles, had relatively minor effects on the proteomes based upon our analysis of about 100 peaks. We concluded that low-molecular-weight serum/plasma proteomes were diversely affected by sampling, handling and storage with most change from variations of sampling procedures. We therefore suggest the necessity of standardizing sampling procedure for proteome comparison and biomarker discovery.

Adult↗
PubMed2007

The use of fatty acid methyl esters as biomarkers to determine aerobic, facultatively aerobic and anaerobic communities in wastewater treatment systems.

The use of fatty acid methyl esters (FAME) as biomarkers to identify groups of microorganisms was studied. A database was constructed using previously published results that identify FAME biomarkers for aerobic, anaerobic and facultatively aerobic bacteria. FAME profiles obtained from pure cultures were utilized to confirm the predicted presence of biomarkers. Principal component analysis demonstrated that the FAME profiles can be used to determine the incidence of these bacterial groups. The presence of aerobic, anaerobic and facultatively aerobic bacteria in the communities, in four bioreactors being used to treat different wastewaters, was investigated by applying FAME biomarkers.

Bacteria↗
PubMed2025

Development and validation of blood-based diagnostic biomarkers for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) using EpiSwitch® 3-dimensional genomic regulatory immuno-genetic profiling.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a debilitating, multifactorial disorder characterised by profound fatigue, post-exertional malaise, cognitive impairments, and autonomic dysfunction. Despite its significant impact on quality of life, ME/CFS lacks definitive diagnostic biomarkers, complicating diagnosis and management. Recent evidence highlights potential blood tests for ME/CFS biomarkers in immunological, genetic, metabolic, and bioenergetic domains. Chromosome conformations (CCs) are potent epigenetic regulators of gene expression and cross-tissue exosome signalling. We have previously developed an epigenetic assay, EpiSwitch®, that employs an algorithm-based CCs analysis. Using EpiSwitch® technology, we have shown the presence of disease-specific CCs in peripheral blood mononuclear cells (PBMCs) of patients with amyotrophic lateral sclerosis (ALS), rheumatoid arthritis (RA), prostate and colorectal cancers, diffuse Large B-cell lymphoma and severe COVID-19. In a recent paper, we have identified a profile of systemic chromosome conformations in cancer patients reflective of the predisposition to respond to immune checkpoint inhibitors, PD-1/PD-L1 antagonists, with 85% accuracy. In this Retrospective case/control study (EPI-ME, Epigenetic Profiling Investigation in Myalgic Encephalomyelitis), we used whole blood samples retrospectively collected from n = 47 patients with severe ME/CFS and n = 61 age-matched healthy control patients to perform whole-genome 3D DNA screening for CCs correlating to ME/CFS diagnosis. We identified a 200-marker model for ME/CFS diagnosis (Episwitch®CFS test). First testing on the retrospective independent validation cohort demonstrated a strong systemic ME/CFS signal with a sensitivity of 92% and a specificity of 98%.Pathways analysis revealed several likely contributors to the pathology of ME/CFS, including interleukins, TNFα, neuroinflammatory pathways, toll-like receptor signalling and JAK/STAT. Comparison with pathways involved in the action of Rituximab and glatiramer acetate (Copaxone) (therapies with potential in ME/CFS treatment) identified IL2 as a shared pathway with clear patient clustering, indicating a possibility of a potential responder group for targeted treatment.

Humans↗
PubMed2004

Isotope-differentiated binding energy shift tags (IDBEST) for improved targeted biomarker discovery and validation.

Mass spectrometry has proved to be an important tool for protein biomarker discovery, identification and characterization. However, global proteomic profiling strategies often fail to identify known low-abundance biomarkers as a result of the limited dynamic range of mass spectrometry (two to three orders of magnitude) compared with the large dynamic range of protein concentrations in biologic fluids (11 to 12 orders of magnitude for serum). In addition, the number of peptides generated in such methods vastly overwhelms the resolution capacity of mass spectrometers, requiring extensive sample clean-up (e.g., affinity tag, retentate chromatography and/or high-performance liquid chromatography) before mass spectrometry analysis. Baiting and affinity pre-enrichment strategies, which overcome the dynamic range and sample complexity issues of global proteomic strategies, are very difficult to couple to mass spectrometry. This is due to the fact that it is nearly impossible to sort target peptides from those of the bait since there will be many cases of isobaric peptides. IDBEST (Target Discovery, Inc.) is a new tagging strategy that enables such pre-enrichment of specific proteins or protein classes as the resulting tagged peptides are distinguishable from those of the bait by a mass defect shift of approximately 0.1 atomic mass units. The special characteristics of these tags allow: resolution of tagged peptides from untagged peptides through incorporation of a mass defect element; high-precision quantitation of up- and downregulation by using stable isotope versions of the same tag; and potential analysis of protein isoforms through more complete peptide coverage from the proteins of interest.

Biomarkers↗
PubMed2006

A systems biology strategy reveals biological pathways and plasma biomarker candidates for potentially toxic statin-induced changes in muscle.

BACKGROUND: Aggressive lipid lowering with high doses of statins increases the risk of statin-induced myopathy. However, the cellular mechanisms leading to muscle damage are not known and sensitive biomarkers are needed to identify patients at risk of developing statin-induced serious side effects. METHODOLOGY: We performed bioinformatics analysis of whole genome expression profiling of muscle specimens and UPLC/MS based lipidomics analyses of plasma samples obtained in an earlier randomized trial from patients either on high dose simvastatin (80 mg), atorvastatin (40 mg), or placebo. PRINCIPAL FINDINGS: High dose simvastatin treatment resulted in 111 differentially expressed genes (1.5-fold change and p-value<0.05), while expression of only one and five genes was altered in the placebo and atorvastatin groups, respectively. The Gene Set Enrichment Analysis identified several affected pathways (23 gene lists with False Discovery Rate q-value<0.1) in muscle following high dose simvastatin, including eicosanoid synthesis and Phospholipase C pathways. Using lipidomic analysis we identified previously uncharacterized drug-specific changes in the plasma lipid profile despite similar statin-induced changes in plasma LDL-cholesterol. We also found that the plasma lipidomic changes following simvastatin treatment correlate with the muscle expression of the arachidonate 5-lipoxygenase-activating protein. CONCLUSIONS: High dose simvastatin affects multiple metabolic and signaling pathways in skeletal muscle, including the pro-inflammatory pathways. Thus, our results demonstrate that clinically used high statin dosages may lead to unexpected metabolic effects in non-hepatic tissues. The lipidomic profiles may serve as highly sensitive biomarkers of statin-induced metabolic alterations in muscle and may thus allow us to identify patients who should be treated with a lower dose to prevent a possible toxicity.

Atorvastatin↗
PubMedDate not supplied

Application of a novel protein biochip technology for detection and identification of rheumatoid arthritis biomarkers in synovial fluid.

We compared protein profiles of the synovial fluid of patients with rheumatoid arthritis and osteoarthritis by using surface-enhanced laser desorption/ionization mass spectrometry technology. With this approach, we identified a protein expressed specifically in the synovial fluid of the patients with rheumatoid arthritis. During the investigation, we found several reproducible and discriminatory biomarker candidates for distinction between rheumatoid arthritis and osteoarthritis. Among these candidates, a 10 850 Da protein peak was the clearest example of a single signal found specifically in the rheumatoid arthritis samples. This candidate was purified using a size-exclusion spin column followed by gel electrophoresis and subsequently identified by peptide mapping and post-source decay (PSD) analysis. The results clearly indicate that the protein is myeloid-related protein 8, which was verified by the enzyme immunoassay. It is known that the myeloid-related protein 8 level in serum and synovial fluid is related to disease activity in juvenile rheumatoid arthritis. The results suggest that the ProteinChip platform is useful to detect and identify protein biomarkers expressed specifically in diseases or in some stage of diseases.

Arthritis, Rheumatoid↗
PubMed2006

Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum.

BACKGROUND: We investigated the potential of proteomic fingerprinting with mass spectrometric serum profiling, coupled with pattern recognition methods, to identify biomarkers that could improve diagnosis of tuberculosis. METHODS: We obtained serum proteomic profiles from patients with active tuberculosis and controls by surface-enhanced laser desorption ionisation time of flight mass spectrometry. A supervised machine-learning approach based on the support vector machine (SVM) was used to obtain a classifier that distinguished between the groups in two independent test sets. We used k-fold cross validation and random sampling of the SVM classifier to assess the classifier further. Relevant mass peaks were selected by correlational analysis and assessed with SVM. We tested the diagnostic potential of candidate biomarkers, identified by peptide mass fingerprinting, by conventional immunoassays and SVM classifiers trained on these data. FINDINGS: Our SVM classifier discriminated the proteomic profile of patients with active tuberculosis from that of controls with overlapping clinical features. Diagnostic accuracy was 94% (sensitivity 93.5%, specificity 94.9%) for patients with tuberculosis and was unaffected by HIV status. A classifier trained on the 20 most informative peaks achieved diagnostic accuracy of 90%. From these peaks, two peptides (serum amyloid A protein and transthyretin) were identified and quantitated by immunoassay. Because these peptides reflect inflammatory states, we also quantitated neopterin and C reactive protein. Application of an SVM classifier using combinations of these values gave diagnostic accuracies of up to 84% for tuberculosis. Validation on a second, prospectively collected testing set gave similar accuracies using the whole proteomic signature and the 20 selected peaks. Using combinations of the four biomarkers, we achieved diagnostic accuracies of up to 78%. INTERPRETATION: The potential biomarkers for tuberculosis that we identified through proteomic fingerprinting and pattern recognition have a plausible biological connection with the disease and could be used to develop new diagnostic tests.

Adolescent↗
PubMed2006

Genomic approach to biomarker identification and its recent applications.

This paper discusses selected activities, issues, and challenges in recent development of analytical methods and applications in biomarker identification and validation using state-of-the-art genomic approaches. Molecular profiling via genomics, proteomics, and metabonomics has opened new windows to study disease states and biological systems. It has also provided exciting opportunities for novel applications in clinical research as well as in drug discovery and development. In the past several years, we have witnessed enormous progress resulting particularly from gene expression profiling of mRNA or transcriptomics. After a brief review on technology advances in gene expression profiling using microarrays, I mainly discuss recent developments of the genomic approaches to biomarker identification and validation in two major types of applications. The first type involves examples in cancer diagnostics and prognostics based on tumor gene expression profiling, whereas the second type involves biomarker applications in drug discovery and development. The focus will be on analytical methods and algorithms that have been developed in recent years facilitating biomarker discovery and application by leveraging genome-wide expression profiles derived from microarrays. Technical issues in experimental design, data processing, error modeling, quality control, figures of merit for performance evaluation, and meta-analysis related to biomarker discovery and application are also discussed. A case study of disease outcome prognosis for breast cancer patients based on tumor expression pattern is presented before closing remarks.

Biomarkers↗
PubMed2026

Biomarker Analysis from Patients with Metastatic PDAC Treated with TGF&#x3b2; Antibody NIS793 plus Abraxane + Gemcitabine versus Abraxane + Gemcitabine Alone in a Phase II, Open-Label, Randomized Study.

PURPOSE: Transforming growth factor &#x3b2; (TGF&#x3b2;) plays a dual role in cancer, acting as a tumor suppressor early in the disease but promoting progression and immune evasion when dysregulated. In pancreatic ductal adenocarcinoma (PDAC), TGF&#x3b2;-driven desmoplasia fosters chemoresistance and immunosuppression, limiting therapeutic efficacy. NIS793, a fully human mAb targeting TGF&#x3b2;, demonstrated antifibrotic and immunomodulatory activity in preclinical models and early-phase trials. PATIENTS AND METHODS: We conducted a randomized, open-label, phase II study in treatment-na&#xef;ve patients with metastatic PDAC (mPDAC) to evaluate NIS793 &#xb1; spartalizumab (anti-PD-1) combined with nab-paclitaxel (or Abraxane)/gemcitabine (ABRA/GEM) versus ABRA/GEM alone. The primary endpoint was progression-free survival (PFS); secondary endpoints included overall survival (OS), safety, pharmacokinetics, and biomarker analyses. Exploratory assessments included paired tumor RNA sequencing, cell-free DNA profiling, and plasma proteomics. RESULTS: NIS793 demonstrated target engagement and suppression of TGF&#x3b2; signaling, confirmed by transcriptomic and proteomic analyses. Stromal remodeling was evident, with significant downregulation of cancer-associated fibroblast markers (Acta2, Fap) and collagen-related signatures. Despite proof of mechanism, clinical efficacy was not observed: Median PFS and OS were comparable or numerically worse in the NIS793 arm versus control (HR for OS in NIS793 + ABRA/GEM vs. ABRA/GEM: 1.32; 95% confidence interval, 0.84-2.07). The safety profile was manageable, with no unexpected toxicities. Biomarker data revealed increased expression of neutrophil-related genes after treatment, suggesting potential induction of tumor-promoting inflammation. CONCLUSIONS: NIS793 effectively inhibited TGF&#x3b2; signaling and led to stromal remodeling but failed to improve outcomes in mPDAC. These findings highlight the complexity of TGF&#x3b2; biology and caution against its blockade in combination with chemotherapy for PDAC. Future strategies should consider context-dependent effects of TGF&#x3b2; inhibition.

Humans↗
PubMed2005

Molecular signature analysis: using the myocardial transcriptome as a biomarker in cardiovascular disease.

With the emergence of microarray technology, it is now possible to simultaneously assess the expression of tens of thousands of gene transcripts, providing a resolution and precision of phenotypic characterization not previously possible. In the field of cardiomyopathy, microarray studies have largely focused on gene discovery, identifying differentially expressed genes characteristic of diverse disease states, through which novel genetic pathways and potential therapeutic targets may be elucidated. However, gene expression profiling may also be used to identify a pattern of genes (a molecular signature) that serves as a biomarker for clinically relevant parameters. One study thus far does demonstrate that a molecular signature can accurately identify etiology in cardiovascular disease, supporting ongoing efforts to incorporate expression-profiling-based biomarkers in determining prognosis and response to therapy in heart failure. Microarray research in cardiomyopathy is still in its earliest stages. Nevertheless, the ultimate potential application of transcriptome-based molecular signature analysis is individualization of the management of patients with heart failure, whereby a patient with a newly diagnosed cardiomyopathy could, through molecular signature analysis, be offered an accurate assessment of prognosis and how individualized medical therapy could affect his or her outcome.

Biomarkers↗
PubMed2005

Gefitinib-responsive EGFR-positive colorectal cancers have different proteome profiles from non-responsive cell lines.

Biomarkers that predict response to therapy with inhibitors of epidermal growth factor receptor (EGFR) tyrosine kinase remain largely uncharacterized. In order to define proteins involved in potential resistance mechanisms, we examined the effect of gefitinib (ZD1839, Iressa) in the EGFR-positive colon cancer cell lines Caco-2, DiFi, HRT-18 and HT-29. None of them exhibited an activating mutation in exons 19 or 21 of EGFR. Proteome profiling with two-dimensional polyacrylamide gel electrophoresis followed by mass spectrometry revealed 12 proteins differentially expressed in responsive and non-responsive cells. These proteins are involved in metabolic pathways, partially relevant in malignant growth and four of them are known to interact with the EGFR signalling pathway. Ubiquitin carboxyl-terminated hydrolase isozyme L1 (UCH-L1) and galectin-3 are overexpressed in the responsive cell line Caco-2, whereas fatty acid-binding protein (E-FABP) and heat shock protein (hsp) 27 are expressed more in the resistant cell lines HRT-18 and HT-29 suggesting a role in non-responsiveness of cells to gefitinib.

Antineoplastic Agents↗
PubMed1999

Differentiation of microorganisms based on pyrolysis-ion trap mass spectrometry using chemical ionization.

The ability to differentiate microorganisms using pyrolysision trap mass spectrometry was demonstrated for five Gram-negative disease-causing organisms: Brucella melitensis, Brucella suis, Vibrio cholera, Yersinia pestis, and Francisella tularensis. Bacterial profiles were generated for gamma-irradiated bacterial samples using pyrolytic methylation and compared for electron ionization and chemical ionization using several liquid reagents with increasing proton affinities. Electron ionization combined with pyrolysis caused extensive fragmentation, resulting in a high abundance of lower mass ions and diminishing the diagnostic value of the technique for compound identification and bacterial profiling. Chemical ionization reduced the amount of fragmentation due to ionization while enhancing the molecular ion region of the fatty acids. As the proton affinity of the reagent increased, the protonated molecular ions of the fatty acids became the predominant ions observed in the mass spectrum. As a result, chemical ionization was shown to be more effective than electron ionization in bacterial profiling. Whereas the bacteria could be distinguished at the Genera level using electron ionization, further differentiation to the subspecies level was possible using chemical ionization. The greatest separation among the five test organisms, in terms of Euclidean distances, was obtained using ethanol as the chemical ionization reagent and using pooled masses representing specific fatty acid biomarkers rather than total ion profiles.

Fatty Acids↗