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At least 631 records · Page 35Linked to original sources
PubMed2005

Relationship between hepatic gene expression profiles and hepatotoxicity in five typical hepatotoxicant-administered rats.

In the field of gene expression analysis, DNA microarray technology has a major impact on many different areas including toxicogenomics, such as in predicting the adverse effects of new drug candidates and improving the process of risk assessment and safety evaluation. In this study, we investigated whether there is relationship between the hepatotoxic phenotypes and gene expression profiles of hepatotoxic chemicals measured by DNA microarray analyses. Sprague-Dawley rats (6 weeks old) were administered five hepatotoxicants: acetaminophen (APAP), bromobenzene, carbon tetrachloride, dimethylnitrosamine, and thioacetamide. Serum biochemical markers for liver toxicity were measured to estimate the maximal toxic time of each chemical. Hepatic mRNA was isolated, and the gene expression profiles were analyzed by DNA microarray containing 1,097 drug response genes, such as cytochrome P450s, other phase I and phase II enzymes, nuclear receptors, signal transducers, and transporters. All the chemicals tested generated specific gene expression patterns. APAP was sorted to a different cluster from the other four chemicals. From the gene expression profiles and maximal toxic time estimated by serum biochemical markers, we identified 10 up-regulated genes and 10 down-regulated genes as potential markers of hepatotoxicity. By Quality-Threshold (QT) clustering analysis, we identified major up- and down-regulated expression patterns in each group. Interestingly, the average gene expression patterns from the QT clustering were correlated with the mean value profiles from the biochemical markers. Furthermore, this correlation was observed at any extent of hepatotoxicity. In this study, we identified 17 potential toxicity markers, and those expression profiles could estimate the maximal toxic time independently of the hepatotoxicity levels. This expression profile analysis could be one of the useful tools for evaluating a potential hepatotoxicant in the drug development process.

Acetaminophen↗
PubMedDate not supplied

Gene expression profiling and breast cancer care: what are the potential benefits and policy implications?

PURPOSE: Gene expression profiling has been proposed as an alternative to clinical guidelines to identify high-risk patients for adjuvant chemotherapy. However, the outcomes associated with gene expression profiling are not clear, and guidelines for the appropriate use of genomic technologies have not been established. METHODS: We developed a decision analytic model to evaluate the incremental cost and quality-adjusted life years of gene expression profiling versus NIH clinical guidelines in a hypothetical cohort of premenopausal early stage breast cancer patients 44 years of age. We conducted empirical analyses and identified literature-based data to inform the model, and performed probabilistic sensitivity analyses to evaluate uncertainty in the results. We interpreted the implications of our findings for treatment guidelines and policies. RESULTS: Use of gene expression profiling resulted in an absolute 5% decrease in the proportion of cases of distant recurrence prevented, 0.21 fewer quality-adjusted life years, and a cost savings of USD 2882. The chosen test cutoff value to identify a tumor as poor prognosis and the cost of adjuvant chemotherapy were the most influential parameters in the analysis, but our findings did not change substantially in sensitivity analyses. Regardless of the test cutoff used to identify a poor prognosis tumor, the gene expression profiling assay studied in our analysis, at its current level of performance, did not attain the threshold sensitivity (95%) necessary to produce equal or greater quality-adjusted life years than NIH guidelines. CONCLUSION: Although the use of gene expression profiling in breast cancer care holds great promise, our analysis suggests additional refinement and validation are needed before use in clinical practice.

Adult↗
PubMed2004

Predicting prostate cancer behavior using transcript profiles.

PURPOSE: Prostate cancer represents a disease with diverse clinical outcomes. Treatment strategies that optimize benefit and minimize morbidities depend on accurate estimates of disease status and likelihood of progression. Emerging technologies capable of qualitatively and quantitatively profiling genes expressed by neoplastic tissues may provide insights into tumor behavior. This review discusses the use of microarray based transcript expression profiling to stratify human cancers into risk categories. MATERIALS AND METHODS: MEDLINE was used to perform a comprehensive literature review of reports describing the assessment of gene expression profiles in malignant diseases. Particular emphasis was placed on studies developing models using individual genes or gene cohorts as predictors of prostate cancer outcome. RESULTS: Alterations in the expression of individual genes identified by microarray analyses have been used in studies of outcome in cancers of the prostate and other tissue types. Profiles of expressed genes have been used to develop prediction models that stratify cancers into prognostic categories based on relapse rates or responses to therapy. CONCLUSIONS: Gene expression profiles offer an opportunity for acquiring molecular determinants correlating with clinical outcome. With rare exceptions these profiles have yet to be validated or used in prospective studies. Future research will benefit from assessments of intratumor heterogeneity and host factors such as the immune response and hormonal milieu. The prospective validation of predictive models will serve to prove usefulness in the clinical arena.

Gene Expression Profiling↗
PubMed2004

Discrimination of benign and malignant thyroid nodules by molecular profiling.

BACKGROUND: The evaluation of thyroid nodules by fine-needle aspiration has been the standard for almost 30 years, despite significant shortcomings in sensitivity and specificity. Recent data from our laboratory have suggested that molecular profiling permits the discrimination of specific types of thyroid nodules. These studies were undertaken to determine whether molecular profiling can discriminate between benign and malignant thyroid nodules with the necessary sensitivity and specificity required of a screening test. METHODS: Molecular profiles of 11 papillary thyroid carcinomas, 13 follicular variant of papillary thyroid carcinomas, 9 follicular thyroid carcinomas, and 26 benign tumors (follicular adenomas and hyperplastic nodules) were analyzed by oligonucleotide microarray analysis. A gene list was created based on 45 samples. Seventeen samples were then added to the analysis as unknowns. A hierarchical clustering analysis was performed on all 62 samples to examine the groups for potential differences and the ability of the gene list to distinguish tumor types. RESULTS: Cluster analysis of all 62 samples produced 2 distinct groups, 1 containing the carcinomas and 1 containing the benign lesions. The sensitivity for a diagnosis of cancer was 91.7% with a specificity of 96.2% (3 follicular variant of papillary thyroid carcinomas clustered with the benign lesions). The cancer gene profiles contained both known cancer-associated genes (MET, galectin-3) and previously unidentified genes. CONCLUSIONS: Molecular profiling readily distinguishes between benign and malignant thyroid tumors with excellent sensitivity and specificity. Elucidated genes may provide insight into the molecular pathogenesis of thyroid cancer. Gene profiling may significantly enhance the evaluation of thyroid nodules in the future.

Adult↗
PubMedDate not supplied

Semisupervised learning for molecular profiling.

Class prediction and feature selection are two learning tasks that are strictly paired in the search of molecular profiles from microarray data. Researchers have become aware how easy it is to incur a selection bias effect, and complex validation setups are required to avoid overly optimistic estimates of the predictive accuracy of the models and incorrect gene selections. This paper describes a semisupervised pattern discovery approach that uses the by-products of complete validation studies on experimental setups for gene profiling. In particular, we introduce the study of the patterns of single sample responses (sample-tracking profiles) to the gene selection process induced by typical supervised learning tasks in microarray studies. We originate sample-tracking profiles as the aggregated off-training evaluation of SVM models of increasing gene panel sizes. Genes are ranked by E-RFE, an entropy-based variant of the recursive feature elimination for support vector machines (RFE-SVM). A Dynamic Time Warping (DTW) algorithm is then applied to define a metric between sample-tracking profiles. An unsupervised clustering based on the DTW metric allows automating the discovery of outliers and of subtypes of different molecular profiles. Applications are described on synthetic data and in two gene expression studies.

Algorithms↗
PubMed2006

Essential role for gene profiling analysis in the authentication of human cell lines.

Cross-contamination between cultured cell lines can result in the generation of erroneous scientific data. Hence, it is very important to eliminate cell lines that are of an origin different from that being claimed. Inter-species contamination can be detected by various established methods, such as karyotype and isozyme analyses. However, it has been impossible to detect intraspecies cross-contamination prior to the development of technology to detect differences between cell lines at the molecular level. Recently, profiling of short tandem repeat (STR) polymorphisms has been established as a method for the analyses of gene polymorphism. Gene profiling by STR polymorphism (STR profiling) is a simple and reliable method to identify individual cell lines. Each human cell line currently provided by the Cell Engineering Division of the RIKEN BioResource Center was analyzed by STR profiling to authenticate its identity. We found that more than 10 human cell lines out of approximately 400 were in fact identical to a different cell line deposited in the collection, and therefore had been misidentified. We conclude that STR profiling is a useful and powerful method for eliminating cell lines that have been misidentified by cross-contamination or by other causes. Hence, STR profiling of human cell lines used in published research will likely be a prerequisite for publication in the future, so that the problem of misidentification of cell lines can be eliminated.

Cell Line↗
PubMed2002

Response of rat muscle to acute resistance exercise defined by transcriptional and translational profiling.

To further understand molecular mechanisms underlying skeletal muscle hypertrophy, expression profiles of translationally and transcriptionally regulated genes were characterized following an acute bout of maximally activated eccentric contractions. Experiments demonstrated that translational mechanisms contribute to acute gene expression changes following high resistance contractions with two candidate mRNAs, basic fibroblast growth factor (bFGF) and elongation factor-1 alpha (EF1alpha), targeted to the heavier polysomal fractions after a bout of contractions. Gene profiling was performed using Affymetrix Rat U34A GeneChips with either total RNA or polysomal RNA at one and six hours following contractions. There were 18 genes that changed expression at one hour and 70 genes that were different (60 genes increased:10 genes decreased)at six hours after contractions. The model from this profiling suggests that following high resistance contractions skeletal muscle shares a common growth profile with proliferating cells exposed to serum. This cluster of genes can be classified as "growth" genes and is commonly associated with progression of the cell cycle. However, a unique aspect was that there was induction of a cluster of tumour suppressor or antigrowth genes. We propose that this cluster of "antigrowth" genes is induced by the stress of contractile activity and may act to maintain skeletal muscle in the differentiated state. From the profiling results, further experiments determined that p53 levels increased in skeletal muscle at 6 h following contractions. This novel finding of p53 induction following exercise also demonstrates the power of expression profiling for identification of novel pathways involved in the response to muscle contraction.

Animals↗
PubMed2004

Robust classification of renal cell carcinoma based on gene expression data and predicted cytogenetic profiles.

Renal cell carcinoma (RCC) is a heterogeneous disease that includes several histologically distinct subtypes. The most common RCC subtypes are clear cell, papillary, and chromophobe, and recent gene expression profiling studies suggest that classification of RCC based on transcriptional signatures could be beneficial. Traditionally, however, patterns of chromosomal alterations have been used to assist in the molecular classification of RCC. The purpose of this study was to determine whether it was possible to develop a classification model for the three major RCC subtypes that utilizes gene expression profiles as the bases for both molecular genetic and cytogenetic classification. Gene expression profiles were first used to build an expression-based RCC classifier. The RCC gene expression profiles were then examined for the presence of regional gene expression biases. Regional expression biases are genetic intervals that contain a disproportionate number of genes that are coordinately up- or down-regulated. The presence of a regional gene expression bias often indicates the presence of a chromosomal abnormality. In this study, we demonstrate an expression-based classifier can distinguish between the three most common RCC subtypes in 99% of cases (n = 73). We also demonstrate that detection of regional expression biases accurately identifies cytogenetic features common to RCC. Additionally, the in silico-derived cytogenetic profiles could be used to classify 81% of cases. Taken together, these data demonstrate that it is possible to construct a robust classification model for RCC using both transcriptional and cytogenetic features derived from a gene expression profile.

Carcinoma, Renal Cell↗
PubMed2026

Using Mapping-Profiles to Refine Strain-Level Metagenomic Classification.

Metagenomic classification at the strain level remains challenging due to high sequence similarity among closely related genomes, which leads to ambiguous read mappings and frequent false-positive strain detections. Reducing such errors improves the reliability of strain-level analyses, which is critical for applications such as pathogen detection. We introduce StrainRefine, a post-mapping refinement method that analyzes read-reference mapping profiles to resolve ambiguous assignments among highly similar genomes. The method represents candidate reference genomes using binary profiles that capture read-support patterns and measures similarity between references based on profile overlap. The method clusters references based on similar mapping profiles, filters weakly supported genomes, and reassigns reads to representative references, reducing redundant reporting of near-identical strains. StrainRefine substantially reduces false-positive strain detections while preserving recall and improving agreement between predicted and true abundance profiles. On large-scale metagenomic datasets, it achieves a substantially improved precision-recall balance compared with existing mapping-based approaches, with the standalone method obtaining the highest read-level classification accuracy on the most complex evaluated dataset. Unlike many strain-level tools designed for individual species, StrainRefine operates without prior assumptions about sample composition or curated species-specific reference collections, while still achieving comparable performance in single-species settings on species-specific reference databases. These results highlight mapping-profile similarity as an effective signal for improving strain-level metagenomic classification.

false-positive reduction↗
PubMed2002

Sources of variability and effect of experimental approach on expression profiling data interpretation.

BACKGROUND: We provide a systematic study of the sources of variability in expression profiling data using 56 RNAs isolated from human muscle biopsies (34 Affymetrix MuscleChip arrays), and 36 murine cell culture and tissue RNAs (42 Affymetrix U74Av2 arrays). RESULTS: We studied muscle biopsies from 28 human subjects as well as murine myogenic cell cultures, muscle, and spleens. Human MuscleChip arrays (4,601 probe sets) and murine U74Av2 Affymetrix microarrays were used for expression profiling. RNAs were profiled both singly, and as mixed groups. Variables studied included tissue heterogeneity, cRNA probe production, patient diagnosis, and GeneChip hybridizations. We found that the greatest source of variability was often different regions of the same patient muscle biopsy, reflecting variation in cell type content even in a relatively homogeneous tissue such as muscle. Inter-patient variation was also very high (SNP noise). Experimental variation (RNA, cDNA, cRNA, or GeneChip) was minor. Pre-profile mixing of patient cRNA samples effectively normalized both intra- and inter-patient sources of variation, while retaining a high degree of specificity of the individual profiles (86% of statistically significant differences detected by absolute analysis; and 85% by a 4-pairwise comparison survival method). CONCLUSIONS: Using unsupervised cluster analysis and correlation coefficients of 92 RNA samples on 76 oligonucleotide microarrays, we found that experimental error was not a significant source of unwanted variability in expression profiling experiments. Major sources of variability were from use of small tissue biopsies, particularly in humans where there is substantial inter-patient variability (SNP noise).

Animals↗
PubMed2005

Towards precise classification of cancers based on robust gene functional expression profiles.

BACKGROUND: Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level. RESULTS: Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles. CONCLUSION: This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.

Algorithms↗
PubMed2006

Predicting a local recurrence after breast-conserving therapy by gene expression profiling.

INTRODUCTION: To tailor local treatment in breast cancer patients there is a need for predicting ipsilateral recurrences after breast-conserving therapy. After adequate treatment (excision with free margins and radiotherapy), young age and incompletely excised extensive intraductal component are predictors for local recurrence, but many local recurrences can still not be predicted. Here we have used gene expression profiling by microarray analysis to identify gene expression profiles that can help to predict local recurrence in individual patients. METHODS: By using previously established gene expression profiles with proven value in predicting metastasis-free and overall survival (wound-response signature, 70-gene prognosis profile and hypoxia-induced profile) and training towards an optimal prediction of local recurrences in a training series, we establish a classifier for local recurrence after breast-conserving therapy. RESULTS: Validation of the different gene lists shows that the wound-response signature is able to separate patients with a high (29%) or low (5%) risk of a local recurrence at 10 years (sensitivity 87.5%, specificity 75%). In multivariable analysis the classifier is an independent predictor for local recurrence. CONCLUSION: Our findings indicate that gene expression profiling can identify subgroups of patients at increased risk of developing a local recurrence after breast-conserving therapy.

Adult↗
PubMed2002

Gene expression profiles derived from fine needle aspiration correlate with response to systemic chemotherapy in breast cancer.

BACKGROUND: Drug resistance in breast cancer is a major obstacle to successful chemotherapy. In this study we used cDNA microarray technology to examine gene expression profiles obtained from fine needle aspiration (FNA) of primary breast tumors before and after systemic chemotherapy. Our goal was to determine the feasibility of obtaining representative expression array profiles from limited amounts of tissue and to identify those expression profiles that correlate with treatment response. METHODS: Repeat presurgical FNA samples were taken from six patients who were to undergo primary surgical treatment. Additionally, a group of 10 patients who were to receive neoadjuvant chemotherapy underwent two FNAs before chemotherapy (adriamycin 60 mg/m2 and cyclophosphamide 600 mg/m2) followed by another FNA on day 21 after the first cycle. Total RNA was amplified with T7 Eberwine's procedure and labeled cDNA was hybridized onto a 7600-feature glass cDNA microarray. RESULTS: We identified candidate gene expression profiles that might distinguish tumors with complete response to chemotherapy from tumors that do not respond, and found that the number of genes that change after one cycle of chemotherapy was 10 times greater in the responding group than in the non-responding group. CONCLUSION: This study supports the suitability of FNA-derived cDNA microarray expression profiling of breast cancers as a comprehensive genomic approach for studying the mechanisms of drug resistance. Our findings also demonstrate the potential of monitoring post-chemotherapy changes in expression profiles as a measure of pharmacodynamic effect and suggests that these approaches might yield useful results when validated by larger studies.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed2006

Use of immuno-LCM to identify the in situ expression profile of cellular constituents of the tumor microenvironment.

Expression profiling using microarrays has become an essential tool for interrogating tumor biology. However, profiling of whole tumor RNA reflects both tumor and host cells, making it difficult to dissect molecular events within specific cellular compartments in the tumor microenvironment. We developed and optimized a simple, rapid technique combining immunohistochemistry and laser-capture microdissection (immuno-LCM) to purify specific cell populations from the tumor microenvironment followed by RNA isolation and amplification for microarray analysis. Using this methodology, we were able to elucidate the in situ expression profile of pure tumor cells and tumor endothelial cells from ovarian tumors with brisk immune infiltrates. This technique not only increased the specificity of profiling isolated cell populations, eliminating genes expressed by surrounding cells, but also increased the sensitivity of analysis, allowing for the detection of low expression genes that were not detected in whole tumor arrays. Pathway analysis of tumor cells in situ identified distinct activation of signaling pathways converging on NF-kappaB, as compared to pathways identified in cultured tumor cell lines, which were primarily metabolic. Profiling of tumor vascular cells revealed most known panendothelial and tumor endothelial-specific markers, and unveiled genes specific to the myeloid-monocytic lineage. We propose that immuno-LCM coupled with transcriptional profiling is a convenient tool for dissecting molecular and cellular events in complex biological systems such as the tumor microenvironment.

Computational Biology↗
PubMed2003

Feasibility of using low-volume tissue samples for gene expression profiling of advanced non-small cell lung cancers.

PURPOSE: The majority of patients with non-small cell lung cancer (NSCLC) present at an advanced clinical stage, when surgery is not a recommended therapeutic option. In such cases, tissues for molecular research are usually limited to the low-volume samples obtained at the time of diagnosis, usually via fine-needle aspiration (FNA). We tested the feasibility of performing gene expression profiling of advanced NSCLCs using amplified RNA from lung FNAs. EXPERIMENTAL DESIGN AND RESULTS: A total of 46 FNAs was tested, of which 18 yielded RNA of sufficient quality for microarray analysis. Expression profiles of these 18 samples were compared with profiles of 17 pairs of tumor and normal lung tissues that had been surgically obtained. Using a variety of unsupervised and supervised analytical approaches, we found that the FNA profiles were highly distinct from the normal samples and similar to the tumor profiles. CONCLUSIONS: We conclude that when RNA amplification is successful, gene expression profiles from NSCLC FNAs can determine malignancy and suggest that with additional refinement and standardization of sample collection and RNA amplification protocols, it will be possible to conduct additional and more detailed molecular analysis of advanced NSCLC using lung FNAs.

Biopsy, Needle↗
PubMed2005

[Preliminary biomarker related to nasopharyngeal carcinoma filtered from the whole genome expression profiling involved in microdissection nasopharyngeal tissues].

OBJECTIVE: To filter biomarkers of nasopharyngeal carcinoma (NPC) by constructing the homogenesis tissue gene expression profiling with the whole human genome GeneChip. METHODS: The epithelium cells of the homogenesis NPC and the pure nasopharyngeal normal tissues microdissected from nasopharyngeal biopsy which was preserved in the RNAlater were used to isolate RNA and then to harvest the aRNA through in vitro transcription, and aRNA prober was labled to hybridize to HG-U133. plus 2.0, so the expression profiling of each homogenesis tissue could be constructed. RESULTS: Some candidate biomarker genes related to the tumorigenesis of NPC had been filtered by comparing the expression profiling of NPC samples with the expression profiling of normal nasopharyngeal epithelia samples. Any genes regarding the metastasis of NPC might have been selected by comparing the expression profiling of no-metastasis samples with those of the metastasis samples. CONCLUSION: Using the whole genome GeneChip to construct the expression profiling for the microdissected homogenesis tissue is effective to filter the candidate biomarker genes.

Adult↗
PubMed2004

Evaluation and comparison of blood pressure profiles of Pakistani cohort of children.

OBJECTIVE: To determine the blood pressure profile of children in Metroville, a lower middle class urban community of Karachi, Pakistan, and compare it to Pakistan National Health Survey of children. DESIGN: A research survey. PLACE AND DURATION OF STUDY: The study was conducted by National Institute of Cardiovascular Diseases, Karachi from 1996 2002. SUBJECTS AND METHODS: The blood pressure data was generated at the base line examination of MHS and was used to define the BP profile of urban Metroville children, age 2-17 years. Similar data of Pakistan National Health Survey (PNHS) undertaken by Pakistan Medical Research Council (PMRC), was used for comparison. RESULTS: A comparison of the blood pressure profile of the MHS with that of the PMRC showed that blood pressure was lower in the MHS. Additionally, comparison of the PMRC profile with USA data showed higher diastolic pressure in the PMRC. Comparison of Pakistani profiles with European data also showed higher blood pressure in Pakistani children. CONCLUSION: It is concluded that the MHS represents the BP profile of children in newly emerging lower middle class urban communities in Pakistan. PMRC data represents national average and can be used to define the blood pressure characteristics of urban communities similar to Metroville. The comparison with affluent countries highlighted the urgent need for community based preventive programs to combat hypertensive diseases in Pakistan.

Adolescent↗
PubMedDate not supplied

The ideal nasal profile: rhinoplasty patients vs the general public.

OBJECTIVES: To evaluate whether patients seeking reduction rhinoplasty hold a different concept of the ideal nose than does the general public, and to determine what features characterize the ideal nasal profile. METHODS: Twenty-seven patients seeking reduction rhinoplasty and 15 randomly selected members of the public evaluated a series of computer-manipulated photographic profiles using a pictorial visual analogue scale to rate their preferences for several variables. Center-scale images were created from mesh-warped ("morphed") computer averaging of 12 white women. Differences between the rhinoplasty group and the public group were then compared, as was each group's deviation from the center of the scale. RESULTS: Both groups preferred narrowly distributed differences from the "average" profile to a high degree of significance. No statistically significant difference was found between the ideal nasal profiles selected by the rhinoplasty group and the public group. CONCLUSIONS: Reduction rhinoplasty patients do not appear to have a different concept of the ideal nose than does the public at large. The ideal nose, as it pertains to the ideal white female profile, has characteristics that differ from a mathematically averaged nasal profile.

Adult↗