Immunoglobulin and inflammatory markers profiles in mycetoma.
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Adolescent↗
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Results for “Biomarker Profiling”
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Analysis of cell-cycle profiles in transfected cells using a membrane-targeted GFP.
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Biomarkers↗
Biomarkers as tools in human health risk assessment.
Evaluation of occupational or environmental risk due to exposure to chemicals requires sufficient information on the toxic profiles, mechanisms of action, toxicokinetics, dose-response relation, exposure, and the target dose. Usually exposure is estimated by measuring concentrations of the agent in air, food, water, soil, dust, or other media with which a population or an individual is in contact. However, this external exposure is only a rough estimate for the internal exposure (agent dose or its metabolite at the critical target in the organism). Factors of influence are bioavailability of the chemicals, variations in concentrations and routes of exposure, physical activity, and individual variation in rates of metabolism, distribution, and excretion. All these affect the concentration of the toxic agent at the critical target, which is the most precise information for risk assessment. Thus, internal exposure is best measured by determining the concentration of the toxicant or its ultimate metabolite at the critical site in the target organ or by determining adducts with cellular macromolecules such as proteins, amino acids, DNA, or its bases. The latter are easily available in experimental toxicology from animal experiments but only occasionally from humans. For health surveillance such data usually are not available, because they require invasive procedures such as biopsies. Therefore, more accessible body fluids or tissue are used, such as blood, urine, or adipose tissue, or adducts with macromolecules such as albumin or hemoglobin in the blood, DNA adducts in peripheral lymphocytes, or altered DNA bases in urine such as 8-hydroxyguanine. All of these are indicators for exposure, whereas risk can only be estimated if the correlation between their deviations from normal and the dose-response at the critical target is known.
Animals↗
Predicting biomarkers for ovarian cancer using gene-expression microarrays.
Ovarian cancer has the highest mortality rate of gynaecological cancers. This is partly due to the lack of effective screening markers. Here, we used oligonucleotide microarrays complementary to approximately 12 000 genes to establish a gene-expression microarray (GEM) profile for normal ovarian tissue, as compared to stage III ovarian serous adenocarcinoma and omental metastases from the same individuals. We found that the GEM profiles of the primary and secondary tumours from the same individuals were essentially alike, reflecting the fact that these tumours had already metastasised and acquired the metastatic phenotype. We have identified a novel biomarker, mammaglobin-2 (MGB2), which is highly expressed specific to ovarian cancer. MGB2, in combination with other putative markers identified here, could have the potential for screening.
Biomarkers, Tumor↗
GEPIS--quantitative gene expression profiling in normal and cancer tissues.
MOTIVATION: Expression profiling in diverse tissues is fundamental to understanding gene function as well as therapeutic target identification. The vast collection of expressed sequence tags (ESTs) and the associated tissue source information provides an attractive opportunity for studying gene expression. RESULTS: To facilitate EST-based expression analysis, we developed GEPIS (gene expression profiling in silico), a tool that integrates EST and tissue source information to compute gene expression patterns in a large panel of normal and tumor samples. We found EST-based expression patterns to be consistent with published papers as well as our own experimental results. We also built a GEPIS Regional Atlas that depicts expression characteristics of all genes in a selected genomic region. This program can be adapted for large-scale screening for genes with desirable expression patterns, as illustrated by our large-scale mining for tissue- and tumor-specific genes. AVAILABILITY: The email server version of the GEPIS application is freely available at http://share.gene.com/share/gepis. An interactive version of GEPIS will soon be freely available at http://www.cgl.ucsf.edu/Research/genentech/gepis/. The source code, modules, data and gene lists can be downloaded at http://share.gene.com/share/gepis.
Algorithms↗
Development and gene expression profiling of a metastatic variant of the human breast cancer MDA-MB-435 cells.
We have developed a model system of late stage metastatic progression by isolating a highly malignant variant of human breast cancer cells from the parental MDA-MB-435 cell line. These cells, isolated from early lung metastasis, displayed increased anchorage independent growth in vitro and when transplanted ortho-topically into nude mice showed accelerated tumor growth rate and early lung spontaneous metastasis when compared to its parental counterpart. These cells, designated as MDA-MB-435-F-L, also showed intense wide spread early skeletal metastasis in vertebrae, mandible, femur, tibia and skull as detected by fluorescence imaging in an experimental bone metastasis model. Gene expression profiles from cDNA microarray showed up or downregulation of the expression of several significant genes regulating angiogenesis, apoptosis, ECM remodeling and metastasis in the MDA-MB-435-F-L cells in comparison to the parental cells. Among the up or downregulated genes, some have also been implicated in the survival of breast cancer patients. As such, the candidate genes selected in this breast cancer progression model system may serve as biomarkers of metastatic progression and also as potential tumor targets for breast cancer therapy.
Animals↗
Successful prediction of prostate cancer recurrence by gene profiling in combination with clinical data: a 5-year follow-up study.
We show here that gene expression profiling, performed with conventional techniques and focused on a selected group of genes, when used in combination with standard clinical information, provides reliable prognostic prediction of prostate cancer (CaP). We showed previously that changes in the expression of metabolically related genes are involved in CaP progression. We then proceeded to search further for correlations between patients' gene profiling and recurrence with a 5-year follow-up study conducted on the same cohort of patients in which the molecular data were obtained. CaP prognosis was first assessed on the basis of gene expression profiling alone; then the result was compared with the prediction obtained using clinical and pathological information (Gleason score, Tumor-Node-Metastasis staging, prostate volume, or prostate-specific antigen levels at the time of diagnosis). The best result was obtained with a selected combination of gene profiling and clinical/pathological parameters, which resulted in prediction of recurrence in 95.7% of patients.
Aged↗
A lipid-immune network signature defines susceptibility to asparaginase-associated pancreatitis.
BACKGROUNDAsparaginase is essential for curing acute lymphoblastic leukemia (ALL), but its use is limited by asparaginase-associated pancreatitis (AAP), a severe and unpredictable toxicity lacking validated prospective biomarkers. We sought to define early systemic molecular features of susceptibility to AAP.METHODSWe performed longitudinal lipidomic and proteomic profiling in two independent pediatric ALL cohorts (n = 161; 79 AAP cases, 82 controls) using paired blood samples collected before asparaginase exposure and at the end of induction therapy (including a single dose of asparaginase), thereby capturing pre-injury biology rather than consequences of pancreatitis. We applied differential abundance and network-based analyses and integrated lipid-cytokine associations using proteomics.RESULTSAcross cohorts, we identified a reproducible lysophosphatidylcholine-centered (LPC-centered) signature characterized by attenuated induction therapy-associated LPC responses and disruption of LPC coregulation at the network level. Proteomic profiling revealed enrichment of cytokine signaling pathways, and integrative analyses demonstrated altered lipid-cytokine coupling, including a flip in association direction for LPC species and IL-18 between cases and controls. Although IL-18/LPC ratios did not differ globally, elevated postinduction IL-18/LPC ratios identified AAP risk within a protocol-defined very high-risk ALL subgroup (AUC = 0.81).CONCLUSIONThese findings support a systems-level model in which failure of coordinated lipid-immune responses under therapeutic stress confers vulnerability to AAP, providing a framework for validation and mitigation strategies.TRIAL REGISTRATIONNCT00400946; NCT01574274; NCT03020030 (parent trials).FUNDINGServier Pharmaceuticals (IIT-95014-027-USA); SDRC (P30DK116074); Stanford SPARK; Fonds de Recherche du Québec - Santé; Fondation Charles-Bruneau; Leukemia & Lymphoma Society of Canada.
Adolescent↗
Identification of serum amyloid A as a biomarker to distinguish prostate cancer patients with bone lesions.
BACKGROUND: Prostate cancer has a propensity to metastasize to the bone. Currently, there are no curative treatments for this stage of the disease. Sensitive biomarkers that can be monitored in the blood to indicate the presence or development of bone metastases and/or response to therapies are lacking. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) is an affinity-based approach that allows sensitive and high-throughput protein profiling and screening of biological samples. METHODS: We used SELDI-TOF MS for protein profiling of sera from prostate cancer patients (n = 38) with and without bone metastases in our effort to identify individual or multiple serum markers that may be of added benefit to those in current use. Serum was applied to ProteinChip surfaces (H4 and IMAC) to quickly screen samples and detect peaks predominating in the samples obtained from patients with bone metastases. Unique proteins in the bone metastasis cohort observed by SELDI-TOF MS were identified by two-dimensional gel electrophoresis, in-gel trypsin digestion, and tandem MS. The identities of the proteins were confirmed by ELISA and immunodepletion assays. RESULTS: The cluster of unique proteins in the sera of patients with bone metastases was identified as isoforms of serum amyloid A. Machine-learning algorithms were also used to identify patients with bone metastases with a sensitivity and specificity of 89.5%. CONCLUSIONS: SELDI-TOF MS protein profiling in combination with other proteomic approaches may provide diagnostic tools with potential clinical applications and serve as tools to aid in the discovery of biomarkers associated with various diseases.
Aged↗
RPL38, FOSL1, and UPP1 are predominantly expressed in the pancreatic ductal epithelium.
OBJECTIVES: Establishing more effective treatment of pancreatic cancer requires an understanding of the molecular events leading to the onset and progression of this disease. The biology of tumorigenesis may be better understood if cell type-specific genes in the pancreas are more recognized. This recognition may be as important as discovering a disease-responsible gene. Identification of a ductal epithelium-specific gene can contribute not only to our knowledge of pancreatic tumorigenesis, tumor marker discovery, and effective drug targeting but also is crucial for making a reliable animal model. METHODS: We used the x-Profiler engine online to compare the SAGE (Serial Analysis of Gene Expression) libraries derived from 2 short-term cultures of normal human ductal epithelial cells from the pancreas against 34 other SAGE libraries generated from other normal human tissues to identify the best candidate gene specific for the ductal epithelium of the pancreas. RESULTS: We identified 3 genes, ribosomal protein L38 (RPL38), uridine phosphorylase (UPP1), and FOS-like antigen-1 (FOSL1), predominantly expressed in the pancreatic ductal epithelium. The expression patterns of these 3 genes were confirmed by virtual Northern analysis, semi-quantitative RT-PCR, and in situ hybridization. CONCLUSION: Although the expressions of these 3 genes are not completely restricted to the ductal epithelium of the pancreas, we showed that they have more specific expression patterns than CK19 and MUC1. We also demonstrated that all 3 genes are highly expressed in a panel of pancreatic cancer cell lines and can potentially be useful in tumor targeting or as tumor markers.
Biomarkers↗
Effect of ret/PTC 1 rearrangement on transcription and post-transcriptional regulation in a papillary thyroid carcinoma model.
BACKGROUND: microRNAs (miRNAs) are a group of non-coding single stranded RNAs measuring approximately 22 nt in length that have been found to control cell growth, differentiation and apoptosis. miRNAs negatively regulate their target genes and recently have been implicated in tumourigenesis. Furthermore, miRNA expression profiling correlates with various cancers, with these genes thought to act as both tumour suppressors and oncogenes. ret/PTC 1 is an oncogene with constitutive kinase activity implicated in the development of papillary thyroid carcinoma (PTC). This rearrangement leads to aberrant MAPK activation that is implicated in PTC tumourigenesis. AIM: The aim of this study was to identify the effect that ret/PTC 1 has on transcription and post-transcriptional regulation in PTC by using DNA microarray and microRNA analysis. RESULTS: DNA microarray analysis revealed a group of genes differentially expressed between normal thyroid cell lines and those harbouring a ret/PTC 1 rearrangement.Furthermore, a unique miRNA expression signature differentiated between PTC cell lines with ret/PTC 1 and a normal thyroid cell line. 21 miRNAs showed significant overexpression and 14 miRNAs showed underexpression in these cell lines when compared to normal thyroid. Several of these up/down regulated miRNAs may be involved in PTC pathogenesis.
Biomarkers, Tumor↗
Porphyrins as early biomarkers for arsenic exposure in animals and humans.
We studied the effect of arsenic exposure on the haem biosynthetic pathway in the rat and humans. Significant increases in protoporphyrin IX, coproporphyrin III, coproporphyrin I were observed in the blood, liver and kidney, and in the urine of rats after a single dose of arsenic. The level of increase was dependent on the arsenic species present. Most of porphyrin concentrations in the tissues increased within 24 hr and urinary excretion elevated within 48 hr. In the human study, we collected urine samples from 113 people who live in Xing Ren of Guizhou Province, a coal-borne arsenicosis endemic area in southwest of PR China and from 30 people who live in Xing Yi (about 80 km southwest of Xing Ren) where arsenicosis is not prevalent. We analyzed the urinary porphyrins using HPLC. Results indicate that all urinary porphyrins were higher in the arsenic exposed group than those in the control group. Women, children and older age people spend much of their time indoors, they had greater increases of urinary arsenic and porphyrins. They were the higher risk groups among the study subjects. A positive correlation between the urinary arsenic levels and porphyrin concentrations demonstrated the effect of arsenic on haem biosynthesis. Significant alteration in the porphyrin excretion profiles of the younger age (<20 y) arsenic exposed group suggested that porphyrins could be used as early warning biomarkers for chronic exposure to arsenic.
Age Factors↗
Distinguishing lung tumours from normal lung based on a small set of genes.
Identifying specific molecular markers and developing sensitive detection methods are two of the fundamental requirements for detection and differential diagnosis of cancer. Toward this goal, we first performed cDNA array analysis using 65 non-small cell lung cancer and non-involved normal lung tissues. We then used several complementary statistical and analytical methods to examine gene expression profiles generated by us and others from four independent sets of normal and neoplastic lung tissues. We report here that several sets of roughly 20 genes were sufficient to provide a robust distinction between normal and neoplastic tissues of the lung. Next we assessed the predictive ability of these gene sets by using Flow-Thru Chips (FTC) (MetriGenix, Baltimore, MD) containing 20 genes to screen 48 primary lung tumours and normal lung tissues. Gene expression changes detected by FTC distinguished lung cancers from the normal lung tissues using an RNA amount equivalent to that present in as few as 300 cells. We also used an independent set of 24 genes and showed that their expression profile was equally effective when measured by quantitative polymerase chain reaction (Q-PCR). Our results demonstrate that lung cancers can be identified based on the expression patterns of just 20 genes and that this approach is applicable for cancer diagnosis, prognosis, and monitoring using small amount of tumour or biopsy samples.
Biomarkers, Tumor↗
Gene overexpression in pancreatic adenocarcinoma: diagnostic and therapeutic implications.
Pancreatic cancer is the deadliest of cancers, and effective diagnostic and therapeutic strategies are lacking. Global gene expression profiling holds promise for improved diagnosis and treatment. Knowledge of the location and timing of gene overexpression and the function of these genes, including their effects on signaling pathways, is important. These data may be used to develop histologic and serum biomarkers as well as to develop immunotherapeutic, molecular targeting, and gene therapy strategies. We have compiled a list of overexpressed genes in pancreatic cancer for which overexpression was confirmed by reverse transcriptase polymerase chain reaction, immunohistochemistry, and/or in situ hybridization following initial identification by global gene expression profiling. The techniques used in the determination of overexpression, problems encountered in global gene expression profiling, and the diagnostic and therapeutic implications of overexpression are discussed. The S100 gene family, mesothelin, prostate stem cell antigen, and 14-3-3 sigma, may have important clinical implications in pancreatic cancer diagnosis and treatment.
14-3-3 Proteins↗
Comparison of mRNA abundance quantified by gene expression profiling and percentage of positive cells using immunophenotyping for diagnostic antigens in acute and chronic leukemias.
BACKGROUND: Microarray analysis is considered a future diagnostic tool in leukemias. Whereas data accumulate on specific gene expression patterns in biologically defined leukemia entities, data on the correlation between flow cytometrically determined protein expression, which are essential in the diagnostic setting today, and microarray results are limited. METHODS: The results obtained by microarray analysis were compared using the Affymetrix GeneChip HG-U133 system in parallel with flow cytometric findings of 36 relevant targets in 814 patients with newly diagnosed acute and chronic leukemias as well as in normal bone marrow samples. RESULTS: In a total of 21,581 individual comparisons between signal intensities obtained by microarray analysis and percentages of positive cell as determined by flow cytometry, coefficients of correlation in the range of 0.171 to 0.807 were obtained. In particular, the degree of correlation was high in the following genes critical in the diagnostic setting: CD4, CD8, CD13 (ANPEP), CD33, CD23 (FCER2), CD64 (FCGR1A), CD117 (KIT), CD34, MPO, CD20 (MS4A1), CD7 (range of r, 0.589-0.807). CONCLUSIONS: The present data prove the high degree of correlation between findings obtained by microarray analysis and flow cytometry. They are in favor of a future application of the microarray technology as a robust diagnostic tool in leukemias.
Antigens, CD↗
Pharmacologic inhibition of epigenetic modifications, coupled with gene expression profiling, reveals novel targets of aberrant DNA methylation and histone deacetylation in lung cancer.
Lung cancer is the leading cause of cancer-related deaths in the United States due, in large part, to the lack of early detection methods. Lung cancer arises from a complex series of genetic and epigenetic changes leading to uncontrolled cell growth and metastasis. Unlike genetic changes, epigenetic changes, such as DNA methylation and histone acetylation, are reversible with currently available pharmaceuticals and are early events in lung tumorigenesis detectable by non-invasive methods. In order to better understand how epigenetic changes contribute to lung cancer, and to identify new disease biomarkers, we combined pharmacologic inhibition of DNA methylation and histone deacetylation in non-small cell lung cancer (NSCLC) cell lines, with genome-wide expression profiling. Of the more than 200 genes upregulated by these treatments, three of these, neuronatin, metallothionein 3 and cystatin E/M, were frequently hypermethylated and transcriptionally downregulated in NSCLC cell lines and tumors. Interestingly, four other genes, cylindromatosis, CD9, activating transcription factor 3 and oxytocin receptor, were dominantly regulated by histone deacetylation and were also frequently downregulated in lung tumors. The majority of these genes also suppressed NSCLC growth in culture when ectopically expressed. This study therefore reveals new putative NSCLC growth regulatory genes and epigenetic disease biomarkers that may enhance early detection strategies and serve as therapeutic targets.
Acetylation↗
Gene expression profiles predict early relapse in ovarian cancer after platinum-paclitaxel chemotherapy.
PURPOSE: Women with advanced epithelial ovarian cancer are routinely treated with platinum-paclitaxel chemotherapy following cytoreductive surgery, yet only approximately 20% achieve long-term disease-free survival. We hypothesized that differences in gene expression before treatment could distinguish patients with short versus long time to recurrence after administration of platinum-paclitaxel combination chemotherapy. EXPERIMENTAL DESIGN: To test this hypothesis, gene expression profiling of 79 primary surgically resected tumors from women with advanced-stage, high-grade epithelial ovarian cancer was done using cDNA microarrays containing 30,721 genes. Supervised learning algorithms were applied in an effort to develop a binary classifier that could discriminate women at risk for early (< or =21 months) versus late (>21 months) relapse after initial chemotherapy. RESULTS: A 14-gene predictive model was developed using a set of training samples (n = 51) and subsequently tested using an independent set of test samples (n = 28). This model correctly predicted the outcome of 24 of the 28 test samples (86% accuracy) with 95% positive predictive value for early relapse. CONCLUSIONS: Predictive markers for early recurrence can be identified for platinum-paclitaxel combination chemotherapy in primary ovarian carcinoma. The proposed 14-gene model requires further validation.
Adult↗
Mass spectrometry for the detection of differentially expressed proteins: a comparison of surface-enhanced laser desorption/ionization and capillary electrophoresis/mass spectrometry.
The discovery of biomarkers is currently attracting much interest as it harbors great potential for the diagnosis and monitoring of human diseases. Here we have used two advanced mass spectroscopy based technologies, surface enhanced laser desorption ionization (SELDI-MS) and capillary electrophoresis/mass spectrometry (CE/MS), to obtain proteomic patterns of urine samples from patients suffering from membranous glomerulonephritis (MGN) and healthy volunteers. The results indicate that CE/MS analysis is able to display a rich and complex pattern of polypeptides with high resolution and high mass accuracy. In order to analyze these patterns, the MosaiqueVisu software was developed for peak identification, deconvolution and the display of refined maps in a three-dimensional format. The polypeptide profiles obtained with SELDI-MS from the same samples are much sparser and show lower resolution and mass accuracy. The SELDI-MS profiles are further heavily dependent on analyte concentration. SELDI-MS analysis identified three differentially expressed polypeptides, which are potential biomarkers that can distinguish healthy donors from patients with MGN. In contrast, approximately 200 potential biomarkers could be identified by CE/MS. Thus, while SELDI-MS is easy to use and requires very little sample, CE/MS generates much richer data sets that enable an in-depth analysis.
Adult↗