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Results for “Clinical proteomics”

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PubMed2006

Identification of over-expressed proteins in oral squamous cell carcinoma (OSCC) patients by clinical proteomic analysis.

BACKGROUND: Oral cancer is a worldwide problem. It is a universal aggressive disease in the population of smoking and drinking. The oral cancer mortality has been ranked 5th place in Taiwan in male cancer patients. A number of protein markers for oral cancer are still not applicable in large populations. Proteomic technologies provide excellent tools for rapid screening of a large number of potential biomarkers in malignant cells. METHOD: Proteomics and real-time quantitative RT-PCR were used to analyze over-expressed proteins in 10 OSCC patients. RESULT: Forty-one proteins were identified as commonly over-expressed in OSCC tissues. In OSCC tissues, alphaB-crystallin, tropomyosin 2, myosin light chain 1, heat shock protein 27 (HSP27), stratifin, thioredoxin-dependent peroxide reductase, flavin reductase, vimentin, rho GDP-dissociation inhibitor 2 (rho GDI-2), glutathione S-transferase Pi (GST-pi) and superoxide dismutase [Mn] (MnSOD) were significantly over-expressed (an average of 7.2, 6.0, 5.7, 4.3, 3.6, 3.4, 3.0, 3.0, 2.6, 2.5, 2.1-fold, respectively). In real-time quantitative RT-PCR analysis, the gene expressions of alphaB-crystallin, HSP27 and MnSOD were also increased in the cancer tissues, consistent with proteomic results. CONCLUSION: The identified proteins in this experiment may be used in future studies of carcinogenesis or as diagnostic markers and therapeutic targets for OSCC.

Adult↗
PubMed2004

Clinical proteomics in lung diseases.

Proteomics is a relatively new approach for understanding the pathology and pathogenesis of various diseases. It has also been used for characterizing the modifications in protein expression during the development of interstitial lung diseases, in lung tumors, or following exposure to exogenous stress signals. We compared the protein composition of primary human lung fibroblasts derived from patients with lung fibrosis and control fibroblasts of unaffected lung tissues. We found a predominant modulation in proteins related to the cytoskeleton, including decreased expression of vimentin and lamin A/C, and increased expression of moesin. Furthermore, we observed lower levels of components of the antioxidative system, such as omega class glutathione S-transferase and an up-regulation of an intracellular chloride channel. In fibroblasts obtained from fibrotic lungs, the expression of a major histocompatibility complex class I C was decreased, and so was the expression of tripeptidyl-peptidase-I-precursor, a collagen-degrading exopeptidase. Our results and the studies reviewed in this paper represent just the beginning of detailed studies that should unravel the relevance and the functional consequences of differential protein expressions in the diseased lung.

Animals↗
PubMed2006

Clinical proteomics: searching for better tumour markers with SELDI-TOF mass spectrometry.

Recently, the focus of cancer research has expanded from genetic information in the human genome to protein expression analyses. Because this 'proteome' reflects the state of a cell, tissue or organism more accurately, much is expected from proteomics to yield better tumour markers for disease diagnosis and therapy monitoring. Some current proteomic technologies are particularly suitable for protein profiling in the search for new biomarkers. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry has been used frequently, highlighting many new proteins as biomarkers (e.g. for ovarian, breast, prostate and colorectal cancer). However, it is becoming increasingly recognized that reproducibility and validation of these biomarkers should be addressed carefully, as should their origin and identity. If these efforts are made, protein profiling can contribute to the better diagnosis of patients and the optimization of their treatment.

Biomarkers, Tumor↗
PubMed2006

2D gel blood serum biomarkers reveal differential clinical proteomics of the neurodegenerative diseases.

This review addresses the challenges of neuroproteomics and recent progress in biomarkers and tests for neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. The review will discuss how the application of quantitative 2D gel electrophoresis, combined with appropriate single-variable and multivariate biostatistics, allows for selection of disease-specific serum biomarkers. It will also address how the use of large cohorts of specifically targeted patient blood serum samples and complimentary age-matched controls, in parallel with the use of selected panels of these biomarkers, are being applied to the development of blood tests to specifically address unmet pressing needs in the differential diagnosis of these diseases, and to provide potential avenues for mechanism-based drug targeting and treatment monitoring. While exploring recent findings in this area, the review discusses differences in critical pathways of immune/inflammation and amyloid formation between Parkinson's disease and amyotrophic lateral sclerosis, as well as discernable synergistic relationships between these pathways that are revealed by this approach. The potential for pathway measurement in blood tests for differential diagnosis, disease burden and therapeutic monitoring is also outlined.

Biomarkers↗
PubMed2004

[Discovery of disease biomarkers by ProteinChip system; clinical proteomics as noninvasive diagnostic tool].

Biopsy-associated complications could not be eliminated in spite of recent refinement, although needle biopsy has been the standard test for diagnosis of renal diseases. The development of noninvasive diagnostic test that provides insights into the mechanisms of diseases would be expected in the field of laboratory medicine. Biological fluids potentially have important histological information whose determination could serve to detect early disease state. Recently the advent of SELDI-TOF-MS (surface-enhanced laser desorption/ionization time-of-flight mass spectrometry) has extended the application of mass spectrometry to the study of proteins in biological fluids. We applied a new protein-chip technology based on SELDI in a discovery of renal disease biomarkers. Proteomic patterns in serum by means of protein-chip were exemplified by elucidating a biomarker candidate for acute renal allograft rejection. SELDI protein-chip technology will be applied more frequently to a number of medical and basic research problems because of high resolution, high reproducibility, ease of use, and fentomole sensitivity.

Biomarkers↗
PubMed2006

Present status of clinical proteomic analysis for the early detection and determination of therapeutic strategy in lung cancer.

Although human DNA sequences have already been successfully decoded, there is still much we do not understand about the pathogenesis of human diseases. Proteomic analysis is comprehensive analysis of proteins, and there is a high probability that recent advances in this field will enable its application to individualized therapy based on thorough understanding of the pathogenesis of human diseases. Two-dimensional polyacrylamide gel electrophoresis (2-DE) and mass spectrometry (MS) are indispensable tools for proteomic analysis, and these technologies enable discovery of tiny changes of protein expression associated with pathogenesis. In this review we introduce the present status of proteomic analysis for cancer-related proteins, and especially lung cancer-related proteins in the attempt to discover new biomarkers for early detection and determine novel target-molecules for treatment. We believe that proteomic analysis will provide crucial information for diagnosis and treatment.

Adenocarcinoma↗
PubMed2006

Proteomics in clinical trials and practice: present uses and future promise.

The study of clinical proteomics is a promising new field that has the potential to have many applications, including the identification of biomarkers and monitoring of disease, especially in the field of oncology. Expression proteomics evaluates the cellular production of proteins encoded by a particular gene and exploits the differential expression and post-translational modifications of proteins between healthy and diseased states. These biomarkers may be applied towards early diagnosis, prognosis, and prediction of response to therapy. Functional proteomics seeks to decipher protein-protein interactions and biochemical pathways involved in disease biology and targeted by newer molecular therapeutics. Advanced spectrometry technologies and new protein array formats have improved these analyses and are now being applied prospectively in clinical trials. Further advancement of proteomics technology could usher in an era of personalized molecular medicine, where diseases are diagnosed at earlier stages and where therapies are more effective because they are tailored to the protein expression of a patient's malignancy.

Clinical Trials as Topic↗
PubMed2004

Bioinformatics strategies for proteomic profiling.

Clinical proteomics is an emerging field that involves the analysis of protein expression profiles of clinical samples for de novo discovery of disease-associated biomarkers and for gaining insight into the biology of disease processes. Mass spectrometry represents an important set of technologies for protein expression measurement. Among them, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI TOF-MS), because of its high throughput and on-chip sample processing capability, has become a popular tool for clinical proteomics. Bioinformatics plays a critical role in the analysis of SELDI data, and therefore, it is important to understand the issues associated with the analysis of clinical proteomic data. In this review, we discuss such issues and the bioinformatics strategies used for proteomic profiling.

Computational Biology↗
PubMed2026

Proteomics at scale: Bottlenecks and opportunities for early-career researchers in a fast developing field.

The field of proteomics has rapidly evolved over the last five years enabled by rapid advances in instrumentation and computation. At the same time, the proteomics community is also growing. This is reflected by the increasing participation in international conferences such as those organized by the European Proteomics Association and the Human Proteome Organization. These events provide early-career researchers with unique opportunities to exchange ideas, develop collaborations, and build networks that support professional development. One such network is the Young Proteomics Investigators Club, a European initiative supported by European Proteomics Association and led by early-career researchers. In this Community-Driven project, we investigate recent trends in proteomics by screening conference abstracts and evaluating the session attendance at Human Proteome Organization Congresses and European Proteomics Association conferences. Based on these analyses, we identified five areas that, from our perspective, are shaping the current trends in proteomics: clinical proteomics, proteomics of post-translational modifications, single-cell proteomics, systems biology and multi-omics, and computational proteomics. For each area, we highlight both unique challenges and identify a common theme: a shift from exploratory studies with manageable sample numbers towards large screenings and cohorts and the generation of big data, which often comes with the lack of computational support, organizational networks, and infrastructure. In this light, we describe the unique challenges and opportunities faced by early-career researchers. We point to actionable directions for enabling reproducible and transparent proteomics as well as community-driven projects and initiatives, which are often providing training and support. SIGNIFICANCE: In this perspective, the Young Proteomics Investigators Club (YPIC) discusses advances in analytical developments and computational approaches in proteomics research. Based on empirical analysis of recent European Proteomics Association conference and Human Proteome Organization congresses contributions, we identify clinical, single-cell, post-translational and systems-level proteomics as the research areas that have gained most momentum in the last three to five years. What makes this work distinctive is that it is written by and for early-career researchers, thereby uniquely identifying where momentum, challenges, and unmet needs converge for the newest generation of proteomics researchers. Rather than cataloguing advances, we examine the widening gap between what modern proteomics can generate and what individual researchers can realistically process, validate, and interpret. We describe specific structural barriers including access to high performance computing, limited formal training in scalable data analysis, the need for unified benchmarking standards and navigating clinical collaboration frameworks. We then highlight opportunities for the field, such as community-curated benchmarks, interdisciplinary mentorship models, and shared computational infrastructure. By making these challenges explicit from an early-career researchers standpoint, we aim to inform how training, funding, and community initiatives can be shaped to support the next generation of proteomics researchers.

Proteomics↗
PubMed2025

Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.

Coronary microvascular disease (CMVD) is an underdiagnosed but significant contributor to the burden of ischemic heart disease, characterized by angina and myocardial infarction. The development of risk prediction models such as polygenic risk scores (PRS) for CMVD has been limited by a lack of large-scale genome-wide association studies (GWAS). However, there is significant overlap between CMVD and enrollment criteria for coronary artery disease (CAD) GWAS. In this study, we developed CMVD PRS models by selecting variants identified in a CMVD GWAS and applying weights from an external CAD GWAS, using CMVD-associated loci as proxies for the genetic risk. We integrated plasma proteomics, clinical measures from perfusion PET imaging, and PRS to evaluate their contributions to CMVD risk prediction in comprehensive machine and deep learning models. We then developed a novel unsupervised endotyping framework for CMVD from perfusion PET-derived myocardial blood flow data, revealing distinct patient subgroups beyond traditional case-control definitions. This imaging-based stratification substantially improved classification performance alongside plasma proteomics and PRS, achieving AUROCs between 0.65 and 0.73 per class, significantly outperforming binary classifiers and existing clinical models, highlighting the potential of this stratification approach to enable more precise and personalized diagnosis by capturing the underlying heterogeneity of CMVD. This work represents the first application of imaging-based endotyping and the integration of genetic and proteomic data for CMVD risk prediction, establishing a framework for multimodal modeling in complex diseases.

Cardiovascular Disease↗
PubMed2004

Proteomics applied to the clinical follow-up of patients after allogeneic hematopoietic stem cell transplantation.

A phase 1 diagnostic study was performed to evaluate a novel technology for clinical proteomic research based on capillary electrophoresis and mass spectrometry. Urine from 40 patients after hematopoietic stem cell transplantation (HSCT; 35 allogeneic, 5 autologous) and 5 patients with sepsis was collected for a period of 100 days and analyzed. More than 1000 different polypeptides could be detected in individual samples. Polypeptide patterns excreted in the urine of patients were significantly different from those of healthy volunteers. No significant differences were detected comparing different conditioning regimens. The aim of this study was to identify polypeptide patterns functioning as early indicators of graft-versus-host disease (GVHD). Eighteen patients developed GVHD after allogeneic HSCT. Sixteen differentially excreted polypeptides formed a pattern of early GVHD markers, allowing discrimination of GVHD from patients without complications with 82% specificity and 100% sensitivity, cross-validated. Inclusion of 13 sepsis-specific polypeptides allowed us to distinguish sepsis from GVHD with a specificity of 97% and a sensitivity of 100%. Sequencing 2 prominent GVHD-indicative polypeptides led to the identification of a peptide from leukotriene A4 hydrolase and a peptide from serum albumin. The data reveal that capillary electrophoresis and mass spectrometry allow identification of biomarkers for a variety of diseases or related complications.

Adult↗
PubMed2026

Unlocking the Circulating Proteome: Toward Clinical Translation.

Blood-based proteomics is approaching a translational inflection point. Driven by advances in measurement technologies, rapid expansion of analytical capabilities, and growing adoption across research and medical communities, there is increasing demand for clinically actionable biomarkers. As the field transitions away from purely large-scale discovery-oriented studies toward more informed, targeted, application-driven analyses, the generation of proteomic data is no longer the bottleneck. Instead, the central challenge is to translate these measurements into robust, reproducible, and clinically meaningful insights. In this Review, we assess recent technological and methodological developments, evaluate persistent preanalytical and interpretative limitations, and outline the key steps required for clinical translation. We focus on three deeply interconnected dimensions: the capabilities and constraints of current measurement platforms, the role of computational and machine learning approaches in extracting biological and clinical signals, and the emergence of large-scale population studies that create new opportunities for validation and generalization. Finally, we discuss a forward-looking vision in which proteomics plays a central role in dynamic, multilayered omics frameworks, where integration with genomics, temporal profiling, and imaging can deepen our understanding of health, disease, and therapeutic response.

Humans↗