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PubMed2005

Deterministic projection by growing cell structure networks for visualization of high-dimensionality datasets.

Recent advances in clinical proteomics data acquisition have led to the generation of datasets of high complexity and dimensionality. We present here a visualization method for high-dimensionality datasets that makes use of neuronal vectors of a trained growing cell structure (GCS) network for the projection of data points onto two dimensions. The use of a GCS network enables the generation of the projection matrix deterministically rather than randomly as in random projection. Three datasets were used to benchmark the performance and to demonstrate the use of this deterministic projection approach in real-life scientific applications. Comparisons are made to an existing self-organizing map projection method and random projection. The results suggest that deterministic projection outperforms existing methods and is suitable for the visualization of datasets of very high dimensionality.

Algorithms↗
PubMed2005

High-affinity capture of proteins by diamond nanoparticles for mass spectrometric analysis.

Carboxylated/oxidized diamond nanoparticles (nominal size 100 nm) exhibit exceptionally high affinity for proteins through both hydrophilic and hydrophobic forces. The affinity is so high that proteins in dilute solution can be easily captured by diamonds, simply separated by centrifugation, and directly analyzed by matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry (MS). No preseparation of the adsorbed molecules from diamonds is required for the mass spectrometric analysis. Compared to conventional MALDI-TOF-MS, an enhancement in detection sensitivity by more than 2 orders of magnitude is achieved for dilute solution containing cytochrome c, myoglobin, and albumin because of preconcentration of the probed molecules. The lowest concentration detectable is 100 pM for a 1-mL solution. Aside from the enhanced sensitivity, the overall performance of this technique does not show any sign of deterioration for highly contaminated protein solutions, and furthermore, no significant peak broadening and band shift were observed in the mass spectra. The promise of this new method for clinical proteomics research is demonstrated with an application to human blood serum.

Adsorption↗
PubMedDate not supplied

The dragon on the gold: myths and realities for data mining in biomedicine and biotechnology using digital and molecular libraries.

To develop bioscience and personalized medicine in the post-genomic era, the biggest problem may be how to extract knowledge from the rich libraries of biomedical data. A particular dragon protects the gold therein: the dragon is the "curse of dimensionality" and its formidable fire weapon, which is burning researchers, is the "combinatorial explosion". This arises because many genomic, proteomic, clinical, and lifestyle factors may interact that cannot necessarily be considered on a simple pairwise or additive basis. A suggested theoretical solution--or at least "road map" that ameliorates management of these problems--borrows from several disciplines. It is undertaken also in the hope might also lead to research with broader impact on several unresolved issues in biotechnology: conversely, mathematical understanding of processes involving molecular libraries, such as cDNA libraries and DNA in the living cell itself, may open the opportunities to use biotechnology to construct nanotechnological storage and query systems.

Biomedical Research↗
PubMed2006

Label-free semiquantitative peptide feature profiling of human breast cancer and breast disease sera via two-dimensional liquid chromatography-mass spectrometry.

A label-free semiquantitative peptide feature profiling method was developed in response to challenges associated with analysis of two-dimensional liquid chromatography-tandem mass spectrometry data. One hundred twenty human sera (49 from invasive breast carcinoma patients, 26 from non-invasive breast carcinoma patients, 35 from benign breast disease patients, and 10 from normal controls) were repeatedly analyzed using a standardized two-dimensional liquid chromatography-mass spectrometry method. Data were extracted using the novel semiquantitative peptide feature profiling method, which is based on comparisons of normalized relative ion intensities. Hierarchical cluster analyses and principle component analyses were used to evaluate the predicative capability of the extracted data, and results were promising. Extracted data were also randomly assigned to either a training group (65%) or to a test group (35%) for artificial neural network modeling. Models best identified invasive breast carcinomas (212 predictions, 94% accurate) and benign non-neoplastic breast disease (96 predictions, 81.3% accurate). These results suggest that, after further development, the novel method may be useful for large scale clinical proteomic profiling.

Breast Diseases↗
PubMed2026

Deep learning-based multimodal pathogenomics integration for precision cancer prognosis.

BACKGROUND: Recent studies have revealed valuable prognostic insights in haematoxylin and eosin (H&E)-stained histological sections and transcriptomic profiles, suggesting potential applications in machine learning. However, existing methods lack sufficient intra- and inter-modal interactions, and face challenges in clinical validation due to incomplete multimodal data. METHODS: We proposed PathoGems (PathoGenomics-based integrative survival prediction), a weakly-supervised, interpretable multimodal learning framework that integrates histology and genomic profiles for precise cancer prognosis prediction. To evaluate the robustness of PathoGems, we initially curated a dataset of 1965 cases across four cohorts from The Cancer Genome Atlas (TCGA), including breast, colorectal, glioblastoma, and esophageal cancers. For external validation, PathoGems was further evaluated on four independent cohorts, consisting of 76 breast cancer and 41 esophageal squamous cell carcinoma cases from Zhejiang Cancer Hospital, as well as 102 colorectal cancer and 58 glioblastoma cases from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). RESULTS: PathoGems effectively stratified patients into favorable and unfavorable risk groups, revealing significant differences in histological patterns, genomic features, and overall survival (log-rank test, p&#x2009;<&#x2009;0.05). Moreover, the model&#x2019;s predictions are further supported by visualization and transcriptomic analysis, enhancing interpretability and reliability. CONCLUSIONS: By fusing histological and clinicogenomic multimodal models, PathoGems will provide a solid foundation for developing an innovative tool that aids clinicians in making informed decisions and selection personalized treatment strategies for cancer patients.

Humans↗
PubMed2026

NPLOC4 Constructs Tumor Immunosuppressive Microenvironment in Pan-cancer and Hepatocellular Carcinoma.

INTRODUCTION: NPLOC4 (nuclear protein localization 4 homolog) is mainly involved in DNA damage, cell cycle, and ubiquitination promotion. Nonetheless, the role of NPLOC4 in the tumor immune microenvironment (TIME) and its potential as a promising tumor therapeutic target remains unclear. METHODS: Therefore, analyses of NPLOC4 mRNA and protein expression, RNA subcellular localization, and patient prognosis associated with NPLOC4 expression were conducted across multiple tumor types. Additionally, the correlations between NPLOC4 and immune cells, non-immune cells, and immune molecules within the tumor immune microenvironment (TIME) were investigated. These analyses utilized data from various public resources, including the Genotype-Tissue Expression (GTEx) project, The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), The Human Protein Atlas (HPA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), TIMER2.0, KM-Plotter, The University of Alabama at Birmingham Cancer Data Analysis Portal (UALCAN), and Tumor Immune Single-cell Hub 2 (TISCH2). Subsequently, we utilized hepatocellular carcinoma (HCC) patients' cancer and adjacent tissues plus tumor cell lines to verify the differential RNA and protein expression of NPLOC4 via qRT-PCR and immunohistochemistry (IHC). Then, the relationship of NPLOC4 expression level with immune infiltration score, infiltration of effector immune cells, suppressive immune cells, and several vital immune checkpoints was analyzed in HCC immune microenvironment. Furthermore, the distribution of expression of NPLOC4 in various cells in the HCC microenvironment was determined through single-cell sequencing analysis. RESULTS: We discovered that NPLOC4 was up-regulated in a variety of tumors and was correlated with poor prognosis. NPLOC4 not only had the potential as a tumor prognostic marker and therapeutic target but also was strongly linked to immune cells, immune checkpoints, and immune-related molecules and pathways in HCC immune microenvironment. CONCLUSION: In summary, NPLOC4 may serve as a promising target for immunotherapy.

Humans↗
PubMed2004

Evaluation of a novel, integrated approach using functionalized magnetic beads, bench-top MALDI-TOF-MS with prestructured sample supports, and pattern recognition software for profiling potential biomarkers in human plasma.

The advantage of using proteins and peptides as biomarkers is that they can be found readily in blood, urine, and other biological fluids. Such sample types are easily obtained and represent a potentially rich palette of biologically informative molecules. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) represents a key tool for rapidly interrogating such sample types. The goal of clinical proteomics is to harness the power of this tool for identifying novel, condition-specific protein fingerprints that may, in turn, lead to the elucidation and use of diseasespecific biomarkers that may be used to diagnose disease as well as to evaluate disease severity, disease progression, and intervention efficacy. Here we have evaluated a simple, affordable bench-top MALDI-TOF mass spectrometer to generate protein profiles from human plasma samples of asthma patients and healthy individuals. We achieve this profiling by using C8-functionalized magnetic beads that enrich a specific subset of plasma proteins based on their absorption by this resin. This step is followed by elution, transfer onto a prestructured sample support (AnchorChip technology), and analysis in a bench-top MALDI-TOF mass spectrometer (OmniFLEX) with AutoXecute acquisition control which enables automated operation with reproducible results. Resulting spectra are compiled and analyzed through the pattern recognition component of ClinProTools software. This approach in combination with ClinProTools software permits the investigator to rapidly scan for potential biomarker peptides/proteins in human plasma. The reproducibility of plasma profiles within and between days has been evaluated. The results show that the novel and facile approach with manual magnetic-bead sample preparation and a low-cost bench-top MALDI-TOF mass spectrometer is suitable for preliminary biomarker discovery studies.

Algorithms↗
PubMed2005

The clinical application of proteomics.

BACKGROUND: Proteomics is defined as a scientific approach used to elucidate all protein species within a cell or tissue, and many researchers are taking advantage of proteomic technology to elucidate protein changes between healthy and diseased states. METHODS: The application of proteomic techniques and strategies to the field of medicine is slowly transforming the way biomarker discovery is conducted. However, the complexity of serum is the source of both its promise to clinical applications and its challenge to proteomic analysis. Like any new technology when it is first introduced, proteomics has been touted with much hope and promise. RESULTS AND CONCLUSIONS: We provide a review of the clinical application of proteomics with the emphasis on current practical issues and challenges facing proteomic research.

Humans↗
PubMed2026

Harnessing metabolomics and proteomics in a clinical trial for pulmonary arterial hypertension: insights from post-hoc analysis of the REHAB-PH trial.

BACKGROUND: The significant clinical and molecular heterogeneity of pulmonary arterial hypertension (PAH) poses challenges in identifying effective therapies. Advanced multidimensional profiling offers an opportunity to capture molecular responses and assess biomarker stability, yet its application in randomised trials remains limited. METHODS: We evaluated the multi-omic profiles of participants with PAH in a randomised, placebo-controlled trial of famotidine. Plasma metabolomic and proteomic profiling was performed at enrolment and 24 weeks. Baseline profiles were compared between treatment arms to assess randomisation balance. Intraclass correlation coefficients quantified within-subject stability over time. Linear regression models adjusting for age, sex, body mass index and PAH aetiology evaluated famotidine's molecular effects. False discovery rate was controlled for multiple comparisons. FINDINGS: For the 79 participants, baseline multi-omic profiles were similar between groups. At 24 weeks, 34 and 37 participants remained in the famotidine and placebo groups respectively. The placebo group showed high molecular stability, while greater variability was observed in the famotidine group. Famotidine treatment was associated with significant changes across 191 proteomic pathways (q-value <0.05), but no metabolomic changes remained significant after multiple-testing correction. INTERPRETATION: Integrating multi-omics into a prospective clinical trial is feasible and yields stable longitudinal profiles in the absence of intervention. While famotidine did not yield clinical benefit, associated proteomic changes illustrate how molecular profiling can reveal treatment-related biology and inform future trial design. These findings highlight the broader utility of multi-omics for evaluating drug responses and identifying molecular endotypes in PAH and beyond. FUNDING: US National Institutes of Health.

Humans↗
PubMed2025

Clinical and Multiorgan Proteomics Characteristics of the Diverse Fatal Phase in Super Elderly Patients With SARS-CoV-2 Infection: A Descriptive Study.

This study aims to identify the risk factors associated with clinical outcomes and the proteomic changes in organs related to fatal SARS-CoV-2 infection within the super-elderly population. This retrospective analysis included all elderly individuals with COVID-19 admitted to the Second Medical Center of PLA General Hospital from December 2022 to January 2023. The follow-up period ended on March 30, 2023. During this time, epidemiological, demographic, laboratory, and outcome data were analyzed descriptively. Proteomic sequencing was performed on super-elderly patients who died from COVID-19 at different stages of the disease. A total of 352 elderly COVID-19 patients, with a mean age of 89.84&#x2009;&#xb1;&#x2009;8.54 years, were included in this study. During a median follow-up period of 98 days, 79 patients died. Deceased patients were older and more likely to have cardiovascular and cerebrovascular diseases, with a lower prevalence of lipid-lowering therapy. The number of deaths in the acute and post-acute phases were 34 and 45, respectively. Proteomics data suggest that the immune systems of patients who died in the acute phase underwent a more rapid and severe onslaught. Patients in the post-acute phase showed higher levels of viral genome replication and a more robust immune response. However, the over-activation of the immune system led to systemic organ dysfunction. Effective management of comorbidities may improve the prognosis of COVID-19 in super-elderly patients. The continuous replication of the SARS-CoV-2 virus and its subsequent impact on the immune system are critical determinants of survival time in this demographic.

Humans↗
PubMed2002

Clinical potential of proteomics in the diagnosis of ovarian cancer.

The need for specific and sensitive markers of ovarian cancer is critical. Finding a sensitive and specific test for its detection has an important public health impact. Currently, there are no effective screening options available for patients with ovarian cancer. CA-125, the most widely used biomarker for ovarian cancer, does not have a high positive predictive value and it is only effective when used in combination with other diagnostic tests. However, pathologic changes taking place within the ovary may be reflected in biomarker patterns in the serum. Combination of mass spectra generated by new proteomic technologies, such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and artificial-intelligence-based informatic algorithms, have been used to discover a small set of key protein values and discriminate normal from ovarian cancer patients. Serum proteomic pattern analysis might be applied ultimately in medical screening clinics, as a supplement to the diagnostic work-up and evaluation.

Biomarkers, Tumor↗
PubMed2003

Clinical applications of proteomics.

Proteomics, the systematic evaluation of changes in the protein constituency of a cell, is more than just the generation of lists of proteins that increase or decrease in expression as a cause or consequence of disease. The ultimate goal is to characterize the information flow through protein pathways that interconnect the extracellular microenvironment with the control of gene transcription. The nature of this information can be a cause or a consequence of disease processes. Clinical applications of proteomics involve the use of proteomic technologies at the bedside. The analysis of human cancer as a model for how proteomics can have an impact at the bedside is now employing several new proteomic technologies that are being developed for early detection, therapeutic targeting and finally, patient-tailored therapy.

Gene Expression Profiling↗
PubMed2006

Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics.

The goal of this chapter is to introduce some of the available computational methods for expression analysis. Genomic and proteomic experimental techniques are briefly discussed to help the reader understand these methods and results better in context with the biological significance. Furthermore, a case study is presented that will illustrate the use of these analytical methods to extract significant biomarkers from high-throughput microarray data. Genomic and proteomic data analysis is essential for understanding the underlying factors that are involved in human disease. Currently, such experimental data are generally obtained by high-throughput microarray or mass spectrometry technologies among others. The sheer amount of raw data obtained using these methods warrants specialized computational methods for data analysis. Biomarker discovery for neurological diagnosis and prognosis is one such example. By extracting significant genomic and proteomic biomarkers in controlled experiments, we come closer to understanding how biological mechanisms contribute to neural degenerative diseases such as Alzheimers' and how drug treatments interact with the nervous system. In the biomarker discovery process, there are several computational methods that must be carefully considered to accurately analyze genomic or proteomic data. These methods include quality control, clustering, classification, feature ranking, and validation. Data quality control and normalization methods reduce technical variability and ensure that discovered biomarkers are statistically significant. Preprocessing steps must be carefully selected since they may adversely affect the results of the following expression analysis steps, which generally fall into two categories: unsupervised and supervised. Unsupervised or clustering methods can be used to group similar genomic or proteomic profiles and therefore can elucidate relationships within sample groups. These methods can also assign biomarkers to sub-groups based on their expression profiles across patient samples. Although clustering is useful for exploratory analysis, it is limited due to its inability to incorporate expert knowledge. On the other hand, classification and feature ranking are supervised, knowledge-based machine learning methods that estimate the distribution of biological expression data and, in doing so, can extract important information about these experiments. Classification is closely coupled with feature ranking, which is essentially a data reduction method that uses classification error estimation or other statistical tests to score features. Biomarkers can subsequently be extracted by eliminating insignificantly ranked features. These analytical methods may be equally applied to genetic and proteomic data. However, because of both biological differences between the data sources and technical differences between the experimental methods used to obtain these data, it is important to have a firm understanding of the data sources and experimental methods. At the same time, regardless of the data quality, it is inevitable that some discovered biomarkers are false positives. Thus, it is important to validate discovered biomarkers. The validation process may be slow; yet, the overall biomarker discovery process is significantly accelerated due to initial feature ranking and data reduction steps. Information obtained from the validation process may also be used to refine data analysis procedures for future iteration. Biomarker validation may be performed in a number of ways - bench-side in traditional labs, web-based electronic resources such as gene ontology and literature databases, and clinical trials.

Animals↗
PubMed2001

Correlation of clinical data with proteomics profiles in 24 patients with B-cell chronic lymphocytic leukemia.

The development of human cancer is caused by complex molecular perturbations leading to variable clinical behavior often even in single disease entities. To prove that expression profiling on the protein level can be correlated with clinical data we systematically compared in a pilot study the protein expression patterns obtained by 2-dimensional gel electrophoresis with clinical features in human B-cell chronic lymphocytic leukemia (B-CLL), a disease characterized by broad clinical variability. Statistical methods were devised to analyze the spot pattern from 24 patient samples. This analysis allowed the identification of proteins that clearly discriminated between the patient groups with defined chromosomal characteristics or whose expression levels did correlate with clinical parameters such as patient survival. This report demonstrates that the correlation of large-scale protein expression profiles with clinical data can be used to gain new insights into molecular aspects of a disease. The data described here show that B-CLL patient populations with shorter survival times exhibit changed levels of redox enzymes, heat shock protein 27 and protein disulfide isomerase. These molecules may be potentially involved in drug resistance.

Humans↗
PubMed2003

From proteomic analysis to clinical significance: overexpression of cytokeratin 19 correlates with hepatocellular carcinoma metastasis.

To better understand the mechanism underlying the hepatocellular carcinoma (HCC) metastasis and to search potential markers for HCC prognosis, differential proteomic analysis on two well-established HCC cell strains with high and low metastatic potentials, MHCC97-H and MHCC97-L, was conducted using two-dimensional gel electrophoresis followed by matrix-assisted laser desorption/time-of-flight mass spectrometry. Cytokeratin 19 (CK19) was identified and found to be overexpressed in MHCC97-H as compared with MHCC97-L. This result was further confirmed by two-dimensional Western blot analysis and immunofluorescence assay. Furthermore, one-dimensional Western blot analysis showed consistently increased CK19 expression in progressively more metastatic cells. Immunohistochemical study on 102 human HCC specimens revealed that more patients in the CK19-positive group had overt intrahepatic metastases (satellite nodules, p < 0.05; vascular tumor emboli, p < 0.001; tumor node metastatis staging, p < 0.001). CK19 fragment CYFRA 21-1 levels measured in sera from nude mice model of human HCC metastasis with radioimmunoassay increased in parallel with tumor progression and rose remarkably when pulmonary metastases occurred. The results demonstrated that overexpression of CK19 in HCC cells is related to metastatic behavior. Serum CK19 level might reflect the pathological progression in some HCC and may be a useful marker for predicting tumor metastasis and a therapeutic target for the treatment of HCC patients with metastases.

Animals↗
PubMed2004

Proteomic research: potential opportunities for clinical and physiological investigators.

Proteomics is the comprehensive and systematic study of proteins, which are functional molecules. Although proteins are products of gene expression, there are more proteins than genes due to the posttranslational modifications of proteins, making the study of proteins difficult. Protein expression is tissue specific, and its function is modulated by variety of factors, including other proteins, phosphates, sulfates, carbohydrates, and lipids, as well as other metabolites. Because of the dynamic nature of protein expression and posttranslational modifications, identification and quantification of proteins alone are not sufficient to understand functional changes. Emerging technologies will allow investigators to perform a combination of metabolic labeling and identification as well as quantification and measurement of the synthesis rates of a large number of proteins in a tissue. This offers the opportunity to better understand the regulation of tissue functions. Rapid advances in mass spectrometry, protein purification techniques, isotope labeling of proteins, and bioinformatics are likely to improve our understanding of physiological states and altered functions in diseased states. Such mechanistic information will improve the ability to perform early diagnosis of tumors and other diseases and develop prognostic indexes and novel therapies.

Endocrinology↗
PubMed2006

Urinary proteomics.

Protein measurement in urine has been used for many years for the diagnosis and monitoring of renal disease. The pattern of urinary protein excretion can be used to identify the cause of the disease and to classify proteinuria. In recent years, proteomics has proven to be a powerful tool in investigation and clinical medicine. Proteomics employs a protein separation method and the identification of proteins using mass spectrometry. One of the objectives of clinical proteomics is the identification of biological markers of disease. To accomplish this, it is necessary to have a normal proteome of the medium in question, which in our case is urine. Comparison of the normal urinary proteome with the urinary proteome from patients with a defined disease can detect proteins expressed differentially from one another. The aim of this review is to present the situation of urinary proteomics, putting special emphasis on its application in the diagnosis of glomerular diseases, renal allograft rejection, urological cancers and urolithiasis.

Biomarkers↗