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PubMed2004

Simultaneous measurement of multiple radiation-induced protein expression profiles using the Luminex(TM) system.

Space flight results in the exposure of astronauts to a mixed field of radiation composed of energetic particles of varying energies, and biological indicators of space radiation exposure provides a better understanding of the associated long-term health risks. Current methods of biodosimetry have employed the use of cytogenetic analysis for biodosimetry, and more recently the advent of technological progression has led to advanced research in the use of genomic and proteomic expression profiling to simultaneously assess biomarkers of radiation exposure. We describe here the technical advantages of the Luminex(TM) 100 system relative to traditional methods and its potential as a tool to simultaneously profile multiple proteins induced by ionizing radiation. The development of such a bioassay would provide more relevant post-translational dynamics of stress response and will impart important implications in the advancement of space and other radiation contact monitoring.

Antibodies↗
PubMed1996

Bladder squamous cell carcinomas express psoriasin and externalize it to the urine.

PURPOSE: To report a single biomarker, psoriasin (Mr 11.0 kd, pI 6.2), a calcium binding protein which is expressed largely by stratified squamous epithelia and is externalized to the urine of bladder squamous cell carcinoma (SCC) bearing patients. MATERIALS AND METHODS: Protein expression profiles of SCCs obtained immediately after surgery were analyzed by two-dimensional gel electrophoresis and Coomassie blue staining. Protein identity was determined by microsequencing and immunoblotting. Protein expression in cryosections was studied by immunofluorescence. RESULTS: Four patients with SCC were identified from 100 samples of patients with suspected transitional cell carcinoma (TCC). The protein profiles of the 4 SCCs (56-1, grade III, T4; 181-1, grade I, T3; 219-1, grade III, T3 and 239-1, grade not determined, T2-4) resembled that of keratinocytes, suggesting that these cells express an early developmental pattern of gene expression. Besides expressing markers characteristic of keratinizing stratified squamous epithelia, the SCCs exhibited psoriasin, a protein externalized to the medium by keratinocytes. Immunohistochemistry of 3 of the SCCs with psoriasin antibodies showed that the positive cells were confined chiefly to the "squamous pearls." The presence of psoriasin in the urine of the 4 SCC patients was demonstrated by two-dimensional gel immunoblotting. Similar analysis of 43 urines from patients with bladder tumors other than SCC revealed 7 positives, some of which may reflect squamous differentiation. Analysis of the urine of 13 control individuals (12 males matched by age and a 42-year-old female) revealed 2 positives. Immunoblotting of the SCC patients' serum proteins with psoriasin antibodies failed to reveal the protein. CONCLUSION: The results point towards psoriasin, alone or as part of a biomarker profile, as a potential marker for the noninvasive follow-up of patients with SCC.

Adult↗
PubMed2006

Surface-enhanced laser desorption/ionization time of flight mass spectrometry protein profiling identifies ubiquitin and ferritin light chain as prognostic biomarkers in node-negative breast cancer tumors.

Novel prognostic biomarkers are imperatively needed to help direct treatment decisions by typing subgroups of node-negative breast cancer patients. The current study has used a proteomic approach of SELDI-TOF-MS screening to identify differentially cytosolic expressed proteins with a prognostic impact in 30 node-negative breast cancer patients with no relapse versus 30 patients with metastatic relapse. The data analysis took into account 73 peaks, among which 2 proved, by means of univariate Cox regression, to have a good cumulative prognostic-informative power. Repeated random sampling (n = 500) was performed to ensure the reliability of the peaks. Optimized thresholds were then computed to use both peaks as risk factors and, adding them to the St. Gallen ones, improve the prognostic classification of node-negative breast cancer patients. Identification of ubiquitin and ferritin light chain (FLC), corresponding to the two peaks of interest, was obtained using ProteinChip LDI-Qq-TOF-MS. Differential expression of the two proteins was further confirmed by Western blotting analyses and immunohistochemistry. SELDI-TOF-MS protein profiling clearly showed that a high level of cytosolic ubiquitin and/or a low level of FLC were associated with a good prognosis in breast cancer.

Apoferritins↗
PubMed2005

Clear cell renal cell carcinoma: gene expression analyses identify a potential signature for tumor aggressiveness.

PURPOSE: The objective of this study was to use gene expression profiling to identify novel biomarkers that are predictive of aggressive behavior in clear cell renal cell carcinoma (CCRCC). EXPERIMENTAL DESIGN: Candidate genes were discovered using Human Genome U133 Plus 2 Arrays and validated on independent samples by quantitative reverse transcription-PCR (RT-PCR). Both the discovery and the validation cohorts included nonaggressive primary CCRCC, aggressive primary CCRCC, metastatic CCRCC, and nonneoplastic kidney adjacent to tumor. RESULTS: Aggressive primary and metastatic CCRCC displayed no significant differences in gene expression. In contrast, we identified significant differences in gene expression between nonaggressive and aggressive CCRCC (including metastatic CCRCC). Thirty-four of the 35 transcripts that displayed the most significant differential expression by microarray analysis also displayed significant differential expression in independent validation studies using quantitative RT-PCR (P < 0.001 for 31 candidates and P < 0.005 for the remaining three candidates). Hierarchical clustering of the quantitative RT-PCR data using our candidate markers accurately grouped 88% (23 of 26) of aggressive and metastatic CCRCC samples, 100% (14 of 14) of nonaggressive CCRCC samples, and 100% (15 of 15) of nonneoplastic samples into separate clusters. Finally, we evaluated the ability of protein expression levels of one of our candidate markers (survivin) to predict survival among a cohort of 183 CCRCC patients treated surgically at Mayo Clinic from 1990 to 1992. In multivariate analysis, expression of survivin (BIRC5) was inversely associated with cancer-specific survival (P = 0.017). CONCLUSION: We used a combination of genomic profiling and validation by quantitative PCR to identify a panel of candidate biomarkers for determining CCRCC aggressiveness. Our data also indicate that the gene expression alterations that result in aggressive behavior and metastatic potential can be identified in the primary tumor.

Biomarkers, Tumor↗
PubMed1993

Porphyrin profiles in the nestling European starling (Sturnus vulgaris): a potential biomarker of field contaminant exposure.

Porphyrin patterns in avian and mammalian tissues and/or excreta have been proposed as qualitative and quantitative biomarkers of exposure to polyhalogenated hydrocarbons, heavy metals, and other environmental contaminants. However, no widely distributed terrestrial species has been characterized as a suitable model in which to assess porphyrin profiles in the evaluation of environmental contaminant exposure. The European starling, whose nests can be readily established and manipulated on contaminated sites, has many qualities that accommodate controlled field research and that suggest its suitability for such assessments. In the present studies, we measured the total and individual porphyrin concentrations in liver, kidney, and fecal-urate excreta of nestling starlings from a noncontaminated field site from day of hatch through d 17 of the nestling period. Total as well as individual 8-, 7-, 6-, 5-, 4-, and 2-carboxyl porphyrin concentrations in liver, kidney, and fecal-urate excreta were readily detectable by high-performance liquid chromatography (HPLC) spectrofluorometric techniques and displayed tissue-specific patterns throughout the developmental period. Liver and fecal-urate porphyrin patterns were established by d 4 subsequent to hatch and remained constant through d 17 of development, whereas renal porphyrin profiles were constant throughout the entire developmental period. In controlled field studies, nestling starlings were treated with either HgCl2 or hexachlorobenzene (HCB), and tissue and excreta porphyrins were extracted and evaluated. The findings suggest that the nestling starling may serve as a suitable model species in which to monitor the effects of field contaminant exposure to wildlife based on chemical-induced changes in tissue or excreta porphyrin levels.

Animals↗
PubMed2006

A comparison of transcriptomic and metabonomic technologies for identifying biomarkers predictive of two-year rodent cancer bioassays.

Two-year rodent bioassays play a central role in evaluating the carcinogenic potential of both commercial products and environmental contaminants. The bioassays are expensive and time consuming, requiring years to complete and costing $2-4 million. In this study, we compare transcriptomic and metabonomic technologies for discovering biomarkers that can efficiently and economically identify chemical carcinogens without performing a standard two-year rodent bioassay. Animals were exposed subchronically to two chemicals (one genotoxic and one nongenotoxic) that were positive for lung and liver tumors in a standard two-year bioassay, two chemicals that were negative, and two control groups. Microarray analysis performed on liver and lung tissues identified multiple biomarkers in each tissue that could discriminate between carcinogenic and noncarcinogenic treatments. The discriminating biomarkers shared a common expression profile among carcinogenic treatments despite different genotoxicity categories and potential modes of action, suggesting that they reflect underlying cellular changes in the transition toward neoplasia. Statistical classification analysis exhibited 100% accuracy in both tissues when the number of genes was less than 5000. Additional genes reduced the predictive accuracy of the model. Serum samples were analyzed by 1H nuclear magnetic resonance (NMR) spectroscopy, and chemical-specific metabolites were removed from the spectra. The statistical classification analysis of the endogenous serum metabolites showed relatively low predictive accuracy with few metabolites in the model, but the accuracy increased to a maximum of 94% when all metabolites were added. These results suggest that individual endogenous metabolites are relatively poor biomarkers, but the metabolite profile as a whole is altered following carcinogen treatment.

Animals↗
PubMed2005

Molecular prognostic markers in pancreatic cancer: a systematic review.

Pancreatic cancer is one of the most lethal tumours of the gastrointestinal tract. The ability to predict which patients would benefit most from surgical intervention and/or chemotherapy would be a great clinical asset. Considerable research has focused on identifying molecular events in pancreatic carcinogenesis, and their correlation with clinicopathological variables of pancreatic tumours and survival. This systematic review examined evidence from published manuscripts looking at molecular markers in pancreatic cancer and their correlation with tumour stage and grade, response to chemotherapy and long-term survival. A literature search was undertaken using PubMed and MEDLINE search engines, using the keywords p53, p21, p16, p27, SMAD4, K-ras, cyclin D1, Bax, Bcl-2, EGFR, EGF, c-erbB2, HB-EGF, TGFbeta, FGF, MMP, uPA, cathepsin, heparanase, E-cadherin, laminins, integrins, TMSF, CD44, cytokines, angiogenesis, VEGF, IL-8, beta-catenin, DNA microarray, and gene profiling. A bewildering number of biomarkers are currently under evaluation. For the most part, the evidence regarding their application as prognostic indicators is conflicting. The advent of gene microarray and mass spectrometric protein profiling offers the potential to examine many different biomarkers simultaneously. This 'protein/gene signature' could revolutionise work in this field and allow researchers to develop accurate and reproducible predictions of survival based on protein or gene profiles.

Apoptosis↗
PubMed2000

Profiles of a healthful diet and its relationship to biomarkers in a population sample from Mediterranean southern France.

OBJECTIVES: The failure of single-nutrient supplementation to prevent disease in intervention studies underlines the necessity to develop a holistic view of food intake. The objectives of this study were to devise a diet quality index (DQI) and identify biomarkers of multidimensional dietary behavior. DESIGN: A nutrition survey was conducted in Mediterranean southern France by means of a food frequency questionnaire. The DQI was based on current dietary recommendations for prevention of diet-related diseases such as cardiovascular disease and some cancers. A second DQI included tobacco use. STATISTICAL ANALYSES: performed Spearman rank correlations, cross-classifications and intraclass correlations were computed between the DQI and biomarkers. RESULTS: Of the 146 subjects, 10 had a healthful diet and 18 had a poor diet. Erythrocyte omega-3 fatty acids-eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)-beta carotene, and vitamin E concentrations were lower and cholesterol concentrations were higher in the poor diet; the difference was significant for EPA and DHA and borderline significant for vitamin E. Significant correlation was found between the DQI and vitamin E (-0.12), EPA (-0.30), and DHA (-0.28), and beta carotene (-0.17) when tobacco use was considered, but not between the DQI and cholesterol. The correlation coefficient reached 0.58 (P0.01) for a composite index based on all biomarkers except cholesterol. CONCLUSIONS: Subjects with a beta carotene levels greater thanl micromol/L, vitamin E greater than 30 micromol/L and EPA greater than 0.65% and DHA greater than 4% of fatty acids in erythrocytes were likely to have a healthful diet. Each biomarker indicated the quality of diet, but correlation was higher with a composite index.

Adult↗
PubMed2026

Mechanism-Driven Diagnostic Development: A Specimen-Aware Framework Illustrated by Colorectal Cancer and Solid Tumours.

Translational oncology has moved rapidly from histopathology and single-analyte biomarkers toward multi-dimensional molecular profiling. Yet many clinically deployed tests still use reductionist biomarker strategies that under-represent cancer complexity. This review examines whether a mechanistic, multi-layered, and specimen-aware approach can improve cancer detection, classification, prognosis, minimal residual disease (MRD) assessment, and therapeutic selection. Evidence across solid tumours shows that genomic alterations alone incompletely explain tumour state, metastatic behaviour, immune evasion, or therapeutic vulnerability. Integrated genome and transcriptome analyses, proteogenomics, single-cell atlases, fragmentomic, methylation based cell-free DNA assays, metabolomics and microbiome assessments reveal clinically relevant biology that single modality tests cannot determine. Minimally invasive collected specimens can extend access to screening, diagnosis and longitudinal monitoring, but the choice of specimen should be matched to disease biology and analytes that represent mechanisms of oncogenesis. However, translation remains constrained by pre-analytical variability, contamination, differences in tumour shedding behaviour, clonal haematopoiesis, translation of generated models, incomplete external validation and uncertain downstream clinical utility for emerging platforms. This review provides a commentary on the future of cancer diagnostics, the considerations and barriers to clinical translation, the relationship between utility and dimensionality of biomarkers assessed and the emerging rationale towards mechanistically grounded integrated models.

biomarkers↗
PubMed2002

No antioxidant effect of combined HRT on LDL oxidizability and oxidative stress biomarkers in treated post-menopausal women.

OBJECTIVE: To compare oxidative stress and LDL oxidizability in postmenopausal women with and without HRT. METHODS: In a cross sectional study, two groups of women, with or without combined per os HRT (1.5-2 mg estrogen associated with 10 mg dydrogesteron), were age and duration of menopause matched. Women were recruited after medical examination at LBSO (Oxidative Stress Laboratory), Joseph Fourier University, Grenoble, and Department of Gynecology, Grenoble University Hospital, France. Main outcome measures included determination of lipid profile and oxidative stress biomarkers (TBARS, LDL oxidizability, autoantibodies against oxidized-LDL). Measurement of circulating levels of vitamin C, E, beta-carotene, lycopene and total antioxidant plasma capacity. RESULTS: HRT led to decreased plasma total and LDL cholesterol (p < 0.05), but did not affect oxidizability and oxidation of LDL. Circulating levels of antioxidant vitamins (beta-carotene, vitamin C, vitamin E/triglycerides) and total antioxidant capacity of plasma and lipid peroxidation, assessed by plasma TBARs, were not different from controls in postmenopausal women receiving HRT. CONCLUSION: This study suggests that even if combined HRT modifies the blood lipid profile, it does not appear to influence oxidative status.

Antioxidants↗
PubMed2004

Identification of patients with head and neck cancer using serum protein profiles.

BACKGROUND: New and more consistent biomarkers of head and neck squamous cell carcinoma (HNSCC) are needed to improve early detection of disease and to monitor successful patient management. OBJECTIVE: To determine if a new proteomic technology can correctly identify protein expression profiles for cancer in patient serum samples as well as detect the presence of a known tumor marker. DESIGN: Direct proteomic analysis and comparison. METHODS: The surface-enhanced laser desorption/ionization time of flight mass spectrometry (SELDI-TOF) ProteinChip system was used to screen for differentially expressed proteins in serum samples from 99 patients with HNSCC, 25 "healthy" smokers, and 102 healthy (normal) controls. Protein peak clustering and classification analyses of the SELDI spectral data were performed. RESULTS: Several proteins, with masses ranging from 2778 to 20,800 Da, were differentially expressed between patients with HNSCC and the normal controls. The serum protein expression profiles were used to develop a classification tree algorithm, which achieved a sensitivity of 83.3% and a specificity of 90% in discriminating HNSCC from normal and healthy smoker controls. The positive and negative predictive values were 80% and 92%, respectively. A peak with an average mass of 10,068 Da was detected in sera from HNSCC patients and identified as the known biomarker metallopanstimulin-1 (MPS-1), based on mass. Peak relative intensity of the 10,068-Da protein correlated consistently with MPS-1 levels detected by radioimmunoassay in serum samples of HNSCC patients and controls. The 10,068-Da peak was provisionally identified as MPS-1 by SELDI immunoassay. CONCLUSION: We propose that this technique may allow for the development of a reliable screening test for the early detection and diagnosis of HNSCC, as well as the potential identification of tumor biomarkers.

Biomarkers, Tumor↗
PubMed2005

Proteomic profiling in the sera of workers occupationally exposed to arsenic and lead: identification of potential biomarkers.

Arsenic (As) and lead (Pb) are important inorganic toxicants in the environment. Frequently, humans are exposed to the mixtures of As and Pb, but little is known about the expression of biomarkers resulting from such mixed exposures. In this study, we analyzed serum proteomic profiles in a group of smelter workers with the aim of identifying protein biomarkers of mixed As and Pb exposure. Forty-six male workers co-exposed to As and Pb were studied. Forty-five age-matched male office workers were chosen as controls. Urine As and blood Pb concentrations were determined. Serum proteomic profiles were analyzed by Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight (SELDI-TOF) mass spectrometer on the WCX2 ProteinChip. Using Recursive support vector machine (RSVM) algorithm, a panel of five peptides/proteins (2097 Da, 2953 Da, 3941 Da, 5338 Da, and 5639 Da) was selected based on their collective contribution to the optional separation between higher metal mixture exposure and non-exposure controls. Among these five selected markers, the 3941 Da was down-regulated and the four other proteins were up-regulated. Descriptive statistics confirmed that these five proteins differed significantly between metal exposure and non-exposure. Interestingly, the combined use of the five selected biomarkers could achieve higher discriminative power than single marker. These results demonstrated that proteomic technology, in conjunction with bioinformatics tools, could facilitate the discovery of new and better biomarkers of mixed metal exposure.

Adult↗
PubMed2006

Following the differentiation of human pluripotent stem cells by proteomic identification of biomarkers.

Following the differentiation of cultured stem cells is often reliant on the expression of genes and proteins that provide information on the developmental status of the cell or culture system. There are few molecules, however, that show definitive expression exclusively in a specific cell type. Moreover, the reliance on a small number of molecules that are not entirely accurate biomarkers of particular tissues can lead to misinterpretation in the characterization of the direction of cell differentiation. Here we describe the use of technology that examines the mass spectrum of proteins expressed in cultured cells as a means to identify the developmental status of stem cells and their derivatives in vitro. This approach is rapid and reproducible and it examines the expression of several different biomarkers simultaneously, providing a profile of protein expression that more accurately corresponds to a particular type of cell differentiation.

Acetamides↗
PubMed2005

Mass profiling-directed isolation and identification of a stage-specific serologic protein biomarker of advanced prostate cancer.

Carcinoma of the prostate (CaP) is the second leading cause of cancer-related mortality among American men. While high cure rates are associated with localized CaP, no cure exists for advanced recurrent disease. At present there are no known serologic biomarkers specific to this stage of the disease. Several groups have used mass spectrometry (MS) based mass profiling (MP) combined with multivariate analysis to identify diagnostically predictive protein peaks for CaP in serum and tissues. Nevertheless, an appreciable level of skepticism exists for MP attributed primarily to a lack of definitive protein characterization. To address this problem, we have applied an approach that combines MP with a whole-protein based top-down separation strategy for the identification of a stage-specific marker in a group comprising 16 patients with CaP (metastatic and localized disease) and 15 healthy individuals. MP, combined with multivariate analysis, yielded 17 serum proteins specific to metastatic disease. A single protein detected at m/z 7771 was found to be significantly decreased in the sera of all the metastatic CaP patients but not in localized CaP or healthy individuals. This protein was therefore chosen as the primary candidate for further analysis. The complex nature of the serologic proteome necessitated an isolation strategy that included a C18 prefractionation, followed by multidimensional liquid chromatography and, finally, two-dimensional gel electrophoresis. The separation process was monitored by UV-Vis and matrix-assisted laser desorption/ionization-time of flight MS analysis. This strategy was found to greatly facilitate subsequent MS characterization of the unknown protein, which was identified as platelet factor 4, a chemokine with prothrombolytic and antiangiogenic activities. Confirmation was achieved using both Western blot analysis and enzyme-linked immunosorbent assay. With the growing interest in using MP for patient classification and diagnosis, our approach and its variations should be powerful in the separation and characterization of proteins following MP.

Biomarkers, Tumor↗
PubMed2004

Neoadjuvant endocrine therapy as a drug development strategy.

The aromatase inhibitors offer both toxicity and efficacy advantages over tamoxifen, but to date, their overall impact on breast cancer outcomes has been modest. Advanced breast cancer remains incurable, and for early stage disease, an improvement in survival with AI versus tamoxifen has yet to be demonstrated. Resistance to endocrine manipulation is at the core of the problem and must be overcome to make additional progress. A number of signal transduction inhibitors (STIs) are now under development as endocrine resistance modulators, including those targeting cyclooxygenase-2, HER1 and/or 2 kinase, mTOR, and farnesyl transferase. Developing STIs for this indication is a challenge, however, because we still do not have a clear understanding of the molecular basis of resistance. A complete understanding could translate into a series of endocrine therapy/STI combinations that would be tailored according to the biology of the individual tumor to achieve optimal efficacy and safety. The development of this strategy will require the ability to diagnose resistance mechanisms on a tumor-by-tumor basis, and this can only be attained through careful clinical investigation. Neoadjuvant endocrine therapy is an appealing context to conduct research in this area because clinical outcomes can be obtained within a few months of treatment, and repeated tumor sampling for biomarker analysis (pharmacodynamic tumor profiling) can be readily achieved. However, the optimal clinical investigative approaches, analytical techniques, and appropriate surrogate end points have yet to be identified and are the subject of several ongoing or planned clinical studies.

Androstadienes↗
PubMed2026

Machine learning-based clinical prediction model and multi-omics integration for assessing pancreatic cancer risk in new-onset diabetes.

BACKGROUND: Given that pancreatic cancer (PC) is typically diagnosed at an advanced stage but is often preceded by new-onset diabetes mellitus (NODM), providing a window for early detection, we sought to develop and validate an interpretable machine-learning model integrated with multi-omics profiling to identify early biomarkers of NODM-associated PC. METHODS: In a population-based cohort, individuals with NODM-associated PC and NODM without PC were identified and randomly divided (70:30) into training and validation sets after feature selection. Eight machine learning (ML) classifiers were compared using fivefold cross-validation, and model performance was evaluated in terms of discrimination, calibration, and decision curve&#x2013;based clinical utility. We evaluated interpretability using the Shapley additive explanations (SHAP) analyses. Mechanistically, Olink proteomic profiling and metabolomics were analyzed through clinical classifications and model-defined risk strata. RESULTS: Categorical boosting achieved the best performance in the independent validation set (AUROC&#x2009;=&#x2009;0.844). The NODM cohort was stratified into high- (n&#x2009;=&#x2009;2,362) and low-risk (n&#x2009;=&#x2009;5,030) groups, and internal validation together with SHAP analyses demonstrated consistent model performance and identified clinically interpretable predictors. Proteomic and metabolomic analyses under clinical and risk-based grouping identified 39 overlapping differentially expressed proteins and 145 overlapping metabolites with enriched across 11 shared KEGG pathways. Cross-platform validation highlighted PLTP, CRTAC1, and ITGAV as serum biomarkers with a strong potential for early NODM-PC detection. CONCLUSIONS: We developed an interpretable ML framework centered on NODM enables practical risk stratification for early PC detection by multi-omics and provides a pathway of ML-based triage followed by biomarker confirmation for earlier detection and diagnosis.

Humans↗