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PubMed2005

The molecular make-up of a tumour: proteomics in cancer research.

The enormous progress in proteomics, enabled by recent advances in MS (mass spectrometry), has brought protein analysis back into the limelight of cancer research, reviving old areas as well as opening new fields of study. In this review, we discuss the basic features of proteomic technologies, including the basics of MS, and we consider the main current applications and challenges of proteomics in cancer research, including (i) protein expression profiling of tumours, tumour fluids and tumour cells; (ii) protein microarrays; (iii) mapping of cancer signalling pathways; (iv) pharmacoproteomics; (v) biomarkers for diagnosis, staging and monitoring of the disease and therapeutic response; and (vi) the immune response to cancer. All these applications continue to benefit from further technological advances, such as the development of quantitative proteomics methods, high-resolution, high-speed and high-sensitivity MS, functional protein assays, and advanced bioinformatics for data handling and interpretation. A major challenge will be the integration of proteomics with genomics and metabolomics data and their functional interpretation in conjunction with clinical results and epidemiology.

Biomarkers, Tumor↗
PubMed2002

Modeling stem cell development by retrospective analysis of gene expression profiles in single progenitor-derived colonies.

The process of development of various cell types is often based on a linear or deterministic paradigm. This is true, for example, for osteoblast development, a process that occurs through the differentiation of a subset of primitive fibroblast progenitors called colony-forming unit-osteoblasts (CFU-Os). CFU-O differentiation has been subdivided into three stages: proliferation, extracellular matrix development and maturation, and mineralization, with characteristic changes in gene expression at each stage. Few analyses have asked whether CFU-O differentiation, or indeed stem cell differentiation in general, may follow more complex and nondeterministic paths, a possibility that may underlie the substantial number of discrepancies in published reports of progenitor cell developmental sequences. We analyzed 99 single colonies of osteoblast stem/primitive progenitor cells cultured under identical conditions. The colonies were analyzed by global amplification poly(A) polymerase chain reaction to determine which of nine genes had been expressed. We used the expression profiles to develop a statistically rigorous map of the cell fate decisions that occur during osteoprogenitor differentiation and show that different developmental routes can be taken to achieve the same end point phenotype. These routes appear to involve both developmental "dead ends" (leading to the expression of genes not correlated with osteoblast-associated genes or the mature osteoblast phenotype) and developmental flexibility (the existence of multiple gene expression routes to the same developmental end point). Our results provide new insight into the biology of primitive progenitor cell differentiation and introduce a powerful new quantitative method for stem cell lineage analysis that should be applicable to a wide variety of stem cell systems.

Animals↗
PubMed2005

Metabolomics in practice: emerging knowledge to guide future dietetic advice toward individualized health.

The profession of dietetics can take an increasingly prominent role in managing health and patient care as clinicians gain access to three new resources: detailed information about the metabolic status of healthy individual clients, metabolic knowledge about the relationships between metabolite abundances and health, and bioinformatics tools that link clients' metabolism to their present and future health status. The current use of single biomarkers as indicators of disease will be replaced by comprehensive profiling of individual metabolites linked to an understanding of health and human metabolism--the emerging science now known as metabolomics. Industrial and academic initiatives are currently developing the analytical and bioinformatic technologies needed to assemble the quantitative reference databases of metabolites as the metabolic analog of the human genome. With these in place, dietetics professionals will be able to assess both the current health status of individuals and predict their health trajectories. Another important role for dietetics professionals will be to assist in the development of the tools and their application in predicting how an individual's specific metabolic pattern can be changed by diet, drugs, and lifestyle, with the goal of improving health and preventing the development of chronic diseases.

Biomarkers↗
PubMed2005

Genotoxic risk assessment in white blood cells of occupationally exposed workers before and after alteration of the polycyclic aromatic hydrocarbon (PAH) profile in the production material: comparison with PAH air and urinary metabolite levels.

OBJECTIVE: Workers in various industries can be exposed to polycyclic aromatic hydrocarbons (PAHs). The relationship between biomarkers of genotoxic risk, PAH compounds in air (ambient monitoring) and PAH metabolites in urine (internal exposure) were studied in 17 workers exposed to PAHs in a fireproof-material producing plant before and 3 months after the PAH profile was altered in the binding pitch. METHODS: Two biomarkers of exposure, specific DNA adducts of (+/-)-r-7,t-8-dihydroxy-t-9,10-oxy-7,8,9,10-tetrahydrobenzo[a]pyrene (anti-BPDE) and non-specific DNA adduct of 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodGuo) were determined in white blood cells (WBCs). In addition, DNA strand breaks were analysed in lymphocytes by single-cell gel electrophoresis in a genotoxic risk assessment. Sixteen PAH compounds in air were determined by personal air sampling, and hydroxylated metabolites of phenanthrene, pyrene and naphthalene were determined in urine. RESULTS: After substitution of the binding pitch the concentrations of benzo[a]pyrene in air decreased (P<0.01). No changes could be observed for pyrene, while levels of phenanthrene (P=0.0013) and naphthalene (P=0.0346) in air increased. Consequently, median DNA adduct rates of anti-BPDE decreased after alteration of the production material (from 0.9 to <0.5 adducts/10(8) nucleotides). No changes in the excretion of 1-hydroxypyrene in urine could be determined, whereas increased levels of 1-, 2+9-, 3- and 4-hydroxyphenanthrene (P<0.0001) and 1-naphthol and 2-naphthol (P=0.0072) were found in urine. In addition, a statistically significant increase in DNA strand break frequencies (P<0.01) and elevated 8-oxodGuo adduct levels (P=0.7819, not statistically significant) were found in the WBCs of exposed workers 3 months after the PAH profile in the binding pitch had been altered. CONCLUSION: The results presented here show that the increased concentration of naphthalene and/or phenanthrene in the air at the work place could induce the formation of DNA strand breaks and alkali-labile sites in WBCs of exposed workers.

Adult↗
PubMed2004

Molecular signatures in biopsy specimens of lung cancer.

Gene expression profiles of resected tumors may predict treatment response and outcome. We hypothesized that profiles derived from lung tumor biopsies would discriminate tumor-specific gene signatures and provide predictive information about outcome. Lung carcinoma specimens were obtained from 23 patients undergoing computed tomography-guided transthoracic biopsy or endobronchial brushing for undiagnosed nodules. Excess tissue was processed for gene profiling. We built class prediction models for lung cancer histology and for cancer outcome. The histology model used an F test to identify 99 genes that were differentially expressed among lung cancer subtypes. The histology validation set class prediction accuracy rate was 86%. The outcome model used the maximum difference subset algorithm to identify 42 genes associated with high risk for cancer death. The outcome training set class prediction accuracy rate was 87%. In conclusion, gene expression profiles of biopsy specimens of lung cancers identify unique tumoral signatures that provide information about tissue morphology and prognosis. The use of specimens acquired from lung biopsy procedures to identify biomarkers of clinical outcome may have application in the management of patients with lung cancer. The procedures are safe and feasible; the efficacy and utility of this strategy will ultimately be determined by prospective clinical trials.

Adult↗
PubMed2006

Gene expression profiling as a window into idiopathic pulmonary fibrosis pathogenesis: can we identify the right target genes?

Expression microarrays that provide genome-level, transcriptional, high-resolution profiles have been applied successfully to multiple diseases. Although microarrays provide information regarding thousands of genes, many investigators prefer to focus on a single gene and validate its role, an approach often supported by grant and journal reviewers. Only a minority of investigators focus on global changes in gene expression. Here, we describe and contrast two general approaches to the use of microarray data: the reductionist "cherry picking" approach and the more global, quantitative "systems" approach. We describe microarray analysis experiments relevant to idiopathic pulmonary fibrosis (IPF) in the context of these two approaches. Although it seems that the cherry-picking approaches have been successful in identifying new relevant genes in IPF, we suggest that to fulfill the discovery potential of microarrays in IPF and to create a working model of IPF, unbiased integrative systems approaches are required.

Biomarkers↗
PubMed2006

Altered gene expression of transcriptional regulatory factors in tumor marker-positive cells during chemically induced hepatocarcinogenesis.

Glutathione-S-transferase placental form (GST-P) is markedly and specifically inducible in rat chemical hepatocarcinogenesis and is a reliable marker protein for pre-neoplasia. To gain insights into the molecular mechanisms at the early stage of hepatocarcinogenesis and hepatotoxicity, we investigated the gene expression profile by DNA microarray analysis. We prepared RNA from GST-P-positive foci in three individual rats and compared with normal liver sections from three individual rats, and labeled RNA was individually hybridized onto Affymetrix GeneChip Rat Expression Array 230A. DNA microarray analysis showed distinctly different profiles of dysregulated gene expression and supported the previous finding that some enzymes involved in metabolism and detoxification are overexpressed and suppressed. Here we discovered that several DNA-binding transcription factors and cofactors, including sterol-regulatory-element binding protein 1 (SREBP1) and Wilms' tumour 1 (WT1)-interacting protein, and their target genes were dysregulated in GST-P-positive foci. Moreover, genes involved in chromatin components, histone modification enzymes, and centrosome duplication were highly expressed. These genes were not previously known to be up-regulated during chemically induced hepatocarcinogenesis. DNA microarray analysis using RNA prepared from tumor marker-positive foci and control tissues provided a candidate gene link to the early stage of carcinogenesis and hepatotoxicity.

Animals↗
PubMed2026

Proteomic Analysis of Extracellular Vesicles Reveals Vitronectin and Laminin Subunit Alpha-3 as Candidate Biomarkers for Gastric Cancer.

BACKGROUND/AIMS: Clinically useful noninvasive biomarkers for gastric cancer remain limited. Extracellular vesicles (EVs) carry a molecular cargo reflective of their cells of origin and have emerged as promising candidates for blood-based cancer biomarkers. We aimed to identify EV-associated protein biomarkers for gastric cancer via a proteomic approach. METHODS: Proteomic profiling of EVs was performed using one normal gastric cell line (Hs738st/int) and two gastric cancer cell lines (AGS and NCI-N87). Selected proteins were validated in blood-derived EVs isolated from plasma samples of 10 healthy controls and 36 patients with gastric cancer. RESULTS: Proteomic analysis identified 224 differentially expressed proteins whose expression was consistently altered in gastric cancer cell line-derived EVs. Among these, vitronectin (VTN) and laminin subunit alpha-3 (LAMA3) were selected based on their consistent upregulation. EV-associated LAMA3 levels were significantly higher in patients with gastric cancer than in healthy controls (p=0.003), with significant elevations observed from stage II onward (p=0.041, p=0.017, and p=0.004 for stages II, III, and IV, respectively). EV-associated VTN levels were not significantly different overall (p=0.089); however, stage-specific analysis demonstrated significant increases in VTN levels in patients with stage III (p=0.036) and stage IV (p=0.005) gastric cancer. Both EV-associated VTN and LAMA3 levels showed significant positive correlations with the cancer stage (&#x3c1;=0.564 and &#x3c1;=0.611, respectively; both p<0.001). CONCLUSIONS: The levels of EV-associated VTN and LAMA3 appear to be more closely associated with disease progression than with early-stage detection of gastric cancer. These findings suggest that EV-based proteomic biomarkers may have clinical utility for monitoring tumor progression in patients with clinically advanced gastric cancer.

Humans↗
PubMed2005

Transcriptional profiles in peripheral blood mononuclear cells prognostic of clinical outcomes in patients with advanced renal cell carcinoma.

PURPOSE: Given their accessibility, surrogate tissues, such as peripheral blood mononuclear cells (PBMC), may provide potential predictive biomarkers in clinical pharmacogenomic studies. In leukemias and lymphomas, the prognostic value of peripheral blast expression profiles is clear; however, it is unclear whether circulating mononuclear cells of patients with solid tumors might yield profiles with similar prognostic associations. EXPERIMENTAL DESIGN: In this study, we evaluated the association of expression profiles in PBMCs with clinical outcomes in patients with advanced renal cell cancer. Transcriptional patterns in PBMCs of 45 renal cell cancer patients were compared with clinical outcome data at the conclusion of a phase II study of the mTOR kinase inhibitor CCI-779 to determine whether pretreatment transcriptional patterns in PBMCs were correlated with eventual patient outcomes. RESULTS: Unsupervised hierarchical clustering of the PBMC profiles using all expressed genes identified clusters of patients with significant differences in survival. Cox proportional hazards modeling showed that the expression levels of many PBMC transcripts were predictors for the patient outcomes of time to progression and overall survival (time to death). Supervised class prediction approaches identified multivariate expression patterns in PBMCs capable of assigning favorable outcomes of time to death and time to progression in a test set of renal cancer patients, with overall performance accuracies of 72% and 85%, respectively. CONCLUSIONS: The present study provides the first example of gene expression profiling in peripheral blood, a clinically accessible surrogate tissue, for identifying patterns of gene expression associated with higher likelihoods of positive outcome in patients with a solid tumor.

Adolescent↗
PubMed2006

Clinical proteomics and mass spectrometry profiling for cancer detection.

A key challenge in the clinical proteomics of cancer is the identification of biomarkers that would enable early detection, diagnosis and monitoring of disease progression to improve long-term survival of patients. Recent advances in proteomic instrumentation and computational methodologies offer a unique chance to rapidly identify these new candidate markers or pattern of markers. The combination of retentate affinity chromatography and mass spectrometry is one of the most interesting new approaches for cancer diagnostics using proteomic profiling. This review presents two technologies in this field, surface-enhanced laser desorption/ionization time-of-flight and Clinprot, and aims to summarize the results of studies obtained with the first of them for the early diagnosis of human cancer. Despite promising results, the use of the proteomic profiling as a diagnostic tool brought some controversies and technical problems, and still requires some efforts to be standardized and validated.

Biomarkers, Tumor↗
PubMed2003

Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons.

Despite having a relatively low incidence, renal cell carcinoma (RCC) is one of the most lethal urologic cancers. For successful treatment including surgery, early detection is essential. Currently there is no screening method such as biomarker assays for early diagnosis of RCC. Surface-enhanced laser desorption/ionization-time of flight mass spectrometry (SELDI-TOF) is a recent technical advance that can be used to identify biomarkers for cancers. In this study, we investigated whether SELDI protein profiling and artificial intelligence analysis of serum could distinguish RCC from healthy persons and other urologic diseases (nonRCC). The SELDI-TOF data was acquired from a total of 36 serum samples with weak cation exchange-2 protein chip arrays and filtered using ProteinChip software. We used a decision tree algorithm c4.5 to classify the three groups of sera. Five proteins were identified with masses of 3900, 4107, 4153, 5352, and 5987 Da. These biomarkers can correctly separate RCC from healthy and nonRCC samples.

Biomarkers, Tumor↗
PubMed2026

Polygenic Profiles Are Associated with Multidomain Biochemical Adaptations Across a Competitive Season in Professional Football Players: A Longitudinal Observational Study.

Background/Objectives: The physiological adaptations required to sustain elite football performance are influenced by both genetic background and dynamic biochemical responses, although their interaction across a full competitive season remains insufficiently characterized. This study aimed to examine the association between polygenic profiles and longitudinal biochemical adaptations in professional football players. Methods: Forty male professional football players competing in the Spanish league were monitored across two consecutive seasons. Blood samples were collected at six time points representing different phases of the competitive cycle. Biomarkers related to muscle metabolism, iron status, and hepatic function were analyzed. Polygenic profiles were calculated using Total Genotype Scores (TGS) for muscle performance, hepatic resilience, and metabolic efficiency. Associations were initially explored using Pearson correlations and subsequently evaluated using linear mixed-effects models accounting for repeated measurements within subjects. Results: Exploratory correlation analyses identified several associations between polygenic profiles and biochemical markers. Muscle performance TGS was inversely associated with serum iron (r = -0.36, p = 0.017) and positively associated with CK (r = 0.32, p = 0.041), Hb (r = 0.29, p = 0.046), and Hct (r = 0.33, p = 0.024). Hepatic resilience TGS showed inverse associations with ALT (r = -0.39, p = 0.012), urea (r = -0.51, p = 0.011), and BUN (r = -0.51, p = 0.011). Metabolic efficiency TGS was negatively associated with AST (r = -0.43, p = 0.044), ALT (r = -0.33, p = 0.025), and GGT across multiple time points (p = 0.001-0.013). However, although several nominal associations emerged in linear mixed-effects models accounting for repeated measurements, none remained statistically significant after false discovery rate correction. These findings should therefore be interpreted as exploratory and hypothesis-generating. Conclusions: Polygenic profiles may be associated with inter-individual variability in biochemical adaptations throughout a competitive season. These findings suggest the integration of genomic and biochemical data in precision athlete monitoring, while highlighting causal relationships and predictive applications require further investigation.

Humans↗
PubMed2004

Use of mass spectrometry to identify protein biomarkers of disease severity in the synovial fluid and serum of patients with rheumatoid arthritis.

OBJECTIVE: To identify a panel of candidate protein biomarkers of rheumatoid arthritis (RA) that can predict which patients will develop erosive, disabling disease. METHODS: A 2-step proteomic approach was used for biomarker discovery and verification. In the first step, 2-dimensional liquid chromatography-coupled tandem mass spectrometry was used to generate protein profiles of synovial fluid (SF) from patients with either erosive RA (n = 5) or nonerosive RA (n = 5). In the second step, the selected candidate markers were verified using quantitative multiple reaction monitoring mass spectrometry in sera of patients with erosive RA (n = 15) or nonerosive RA (n = 15) and of healthy controls (n = 15). RESULTS: Through differential profiling of proteins in the <40-kd portion of the SF proteome, we selected 33 prospective candidate biomarkers from a total of 418 identified proteins. Among the proteins that were elevated in the SF of patients with erosive RA were C-reactive protein (CRP) and 6 members of the S100 protein family of calcium-binding proteins. Significantly, levels of CRP, S100A8 (calgranulin A), S100A9 (calgranulin B), and S100A12 (calgranulin C) proteins were also elevated in the serum of patients with erosive disease compared with patients with nonerosive RA or healthy individuals. CONCLUSION: Several potential protein marker candidates have been identified for prognosis of the erosive form of RA. This study demonstrates the facility of using protein mass spectrometry in SF and serum for global discovery and verification of clinically relevant sets of disease biomarkers.

Adult↗
PubMedDate not supplied

Plasma proteomic pattern as biomarkers for ovarian cancer.

Early detection of ovarian cancer remains a challenge. Pathologic changes within an organ might be reflected in proteomic patterns in serum or plasma. The objective of this study was to identify new plasma biomarkers in ovarian cancer patients using mass spectrometry (MS) protein profiling and artificial intelligence. The study included 35 women with ovarian cancer and 30 age-matched disease-free controls. For plasma protein signature analysis, the protein chip array surface-enhanced laser desorption/ionization (SELDI) analysis was performed. The strong anion exchange (SAX) and weak cation exchange (WCX) chips were used for analysis. After a training analysis by SAX and WCX protein chips, learning algorithm and clustering analysis was performed to reach a discriminate pattern of protein signature. SELDI mass spectroscopy was highly reproducible in detecting ovarian tumor-specific protein profiles. Four specific protein peaks were identified in plasma of women with ovarian cancer, but not in controls, with relative molecular masses of 6190.48, 5147.06, 11522.6, and 11537.7 d. Two peaks, with Mr 5295.5 and 8780.48 d, were present in plasma of control but not in women with ovarian cancer. A sensitivity of 90-96.3% and specificity of 100% for this studied cases and controls were reached. This study clearly demonstrates that the combined technology of SELDI-MS and artificial intelligence is effective in distinguishing protein expression between normal and ovary cancer plasma. The identified gained and lost protein peaks in plasma may provide as candidate proteins to be used for the detection or monitoring ovarian cancer.

Adolescent↗
PubMed2026

Comparison of Proteomic Analysis of Cerebrospinal Fluid From Neurological Patients With and Without Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterised by progressive muscle weakness in both bulbar and extremity muscles, leading to a diverse clinical phenotype with motor and non-motor symptoms. Approximately 85% of ALS cases are sporadic (sALS), while the remaining 10%-15% are familial (fALS). Biological biomarkers of sporadic ALS remain poorly understood, hindering precise patient screening, delaying diagnosis and negatively affecting prognosis. This study aims to identify potential proteomic biomarkers by comparing the cerebrospinal fluid (CSF) of sALS patients with that of patients suffering from other neurological diseases. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used for proteomic profiling of CSF samples from 24 sALS patients and 26 patients with other neurological diseases. The complete protein expression profiles were compared using a two-tailed Student's t-test, with a p <&#x2009;0.05 considered statistically significant with additional FDR correction at the 0.1 level. Proteomic analysis of CSF samples identified significant quantitative changes in 96 proteins with threshold p&#x2009;<&#x2009;0.05 and 74 proteins with FDR <&#x2009;0.1 between sALS and non-ALS patients, including alterations in proteins associated with neurodegenerative processes, such as amyloid precursor proteins and inflammatory markers. CSF proteomic analysis reveals altered inflammatory and neurodegenerative metabolic pathways, providing valuable insights into the proteomic landscape of sALS. Several dysregulated proteins were consistent with the disease mechanisms highlighted in previous studies. These findings represent a step forward in developing personalised approaches for diagnosing and managing the disease.

Humans↗