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Results for “Biomarker Profiling”

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At least 847 records · Page 47Linked to original sources
PubMed2006

Protective effects of EUK4010 on beta-amyloid(1-42) induced degeneration of neuronal cells.

EUK4010 has been identified to exhibit an inhibitory effect on beta-amyloid (Abeta)(1-42)-induced loss of neuronal cell viability. Further studies demonstrated that EUK4010 attenuated the Abeta(1-42)-induced degeneration in both cultured rat hippocampal neurons and human neuroblastoma cells, as demonstrated by typical morphological changes, cell viability and the chip-based flow cytometric assay. Gene expression analysis using DNA microarray showed that the senescence marker calcium-binding protein, regucalcin (Rgn), GABA-A receptor pi subunit (Gabrp), the huntingtin binding protein, optineurin (Optn) and a semaphorin family plexin A3 similar protein (Plex-similar) changed their expression levels significantly in cultured neurons after Abeta(1-42) treatment. In this report, we have undertaken a chemical genetic approach to study the molecular basis of Abeta(1-42) effects on the neuronal degeneration. Our results demonstrate that EUK4010 completely blocked the Abeta(1-42)-induced up-regulation of GABA-A receptor pi subunit and the semaphorin family plexin A3 similar protein, and partially attenuated the down-regulation of senescence marker calcium-binding protein, regucalcin. These observations suggest that EUK4010 may prevent or reduce the Abeta toxicity by regulating the expression of genes involved in the Abeta induced neuronal degeneration. These genes may represent a promising target for the therapeutic drug development for Alzheimer's disease (AD) and other neurological disorders. Furthermore, EUK4010 and its analogues could potentially be developed as neuronal protective agents for the treatment of these diseases.

Amyloid beta-Peptides↗
PubMedDate not supplied

Expression of the c-Met in advanced epithelial ovarian cancer and its prognostic significance.

The purpose of this study was to evaluate the prognostic significance of c-Met expression in advanced cases of epithelial ovarian carcinoma. Paraffin-embedded tissues from 41 stage IIIC primary ovarian adenocarcinoma were stained immunohistochemically for c-Met expression. The expression of c-Met was correlated with conventional clinicopathologic parameters and with overall survival of the patients. c-Met expression was found in 60.9% of cases. This clinicopathologic study showed that epithelial ovarian carcinomas with c-Met expression had higher histologic tumor grade and were more frequently associated with para-aortic lymph node metastasis (P < 0.05). In multivariate analysis, c-Met expression remained as a statistically significant predictor for survival with histologic grade. The patients with stage IIIC epithelial ovarian cancers whose tumors expressed c-Met were more likely to have high-grade tumors, have more para-aortic lymph node involvement, and have a significantly worse overall survival than those whose tumors were c-Met negative. In conclusion, c-Met expression might be a potential prognostic marker for patients with advanced-stage epithelial ovarian cancers.

Adenocarcinoma↗
PubMed2004

Arraying the orchestration of allograft pathology.

Microarrays, or gene chips, are exciting investigative tools for analyzing expression changes across thousands of genes in concert in tissues and cells of interest. Despite the relatively recent application of microarrays to transplant research, they hold great promise for unraveling the staging of rejection, stratifying patients towards more individualized treatment regimes, and discovering noninvasive biomarkers for monitoring of intragraft events. Bioinformatics tools are being developed to sift through the large data sets generated as "genomic fingerprints" of the underlying biologic pathways. Gene clustering and class prediction tools allow discovery of diagnostic and prognostic molecular signatures of health and disease. Oligonucleotide-based microarrays also have utility in genotyping polymorphic markers. This report reviews the current literature of microarray use in transplantation research, compares currently available array platforms, and discusses future application of this technology to clinical organ transplantation.

Biomarkers↗
PubMed2004

Candidate-based proteomics in the search for biomarkers of cardiovascular disease.

The key concept of proteomics (looking at many proteins at once) opens new avenues in the search for clinically useful biomarkers of disease, treatment response and ageing. As the number of proteins that can be detected in plasma or serum (the primary clinical diagnostic samples) increases towards 1000, a paradoxical decline has occurred in the number of new protein markers approved for diagnostic use in clinical laboratories. This review explores the limitations of current proteomics protein discovery platforms, and proposes an alternative approach, applicable to a range of biological/physiological problems, in which quantitative mass spectrometric methods developed for analytical chemistry are employed to measure limited sets of candidate markers in large sets of clinical samples. A set of 177 candidate biomarker proteins with reported associations to cardiovascular disease and stroke are presented as a starting point for such a 'directed proteomics' approach.

Animals↗
PubMed2026

Metagenomic polymorphic toxin effector and immunity profiling predicts microbiome development and disease-related dysbiosis.

Bacteria use antagonistic interbacterial weapons, such as polymorphic toxin secretion systems (TSS), to compete for niches in the human gut microbiome. We hypothesized that TSS influence gut microbiome development and disease-related dysbiosis. We developed a bioinformatic marker gene approach (PolyProf) to quantify TSS including ~200 effector and immunity genes and applied it to ~15,000 publicly available human metagenomes. PolyProf alpha and beta diversity readily distinguished 12 different human disease states and enabled the construction of highly accurate linear regression classifier machine learning models. Elastic net machine learning models integrating bacterial taxonomy with PolyProf had strong predictive value for 12 disease states, outperforming models utilizing taxonomy alone. During microbiome development in the first year of life, PolyProf alpha diversity increases, and beta diversity becomes increasingly like the maternal microbiome, influenced by vertical transfer, delivery mode, and breastfeeding. PolyProf is related to strain sharing among adults through social interactions. In summary, TSS genes strongly correlate with microbiome development and interpersonal strain sharing, suggesting roles for interbacterial antagonism. Since PolyProf distinguishes diverse adult disease statuses, these dynamics may contribute to non-genetic inheritance.IMPORTANCEPrevious research has demonstrated that bacteria compete within the gut microbiome using toxin secretion systems (TSS). How TSS contribute to human microbiome development and the microbiome alterations observed in human diseases is not known. This study develops a new bioinformatic tool for profiling TSS-related genes in metagenomic data. Application of this approach to large-scale human fecal metagenomic data demonstrates the dynamic association of TSS during microbiome development, including the exchange of strains among social contacts. TSS gene abundance patterns are highly predictive of 12 disease states. This study advances the field by enabling TSS profiling in metagenomes and by identifying disease and microbiome development biomarkers that provide hypotheses for future mechanistic studies and may be useful for disease diagnosis.

Dysbiosis↗
PubMedDate not supplied

Differential expression of a human kallikrein 5 (KLK5) splice variant in ovarian and prostate cancer.

The presence of more than one mRNA form is common among kallikrein genes. We identified an mRNA transcript of the human kallikrein gene 5 (KLK5), denoted KLK5 splice variant 1 (KLK5-SV1). This variant has a different 5'-splice site, but encodes the same protein as the classical KLK5 transcript. RT-PCR analysis of this variant transcript expression in 29 human tissues indicated highest expression in the cervix, salivary gland, kidney, mammary gland, and skin. Comparative analysis of the expression levels of KLK5-SV1, another splice variant named KLK5 splice variant 2 (KLK5-SV2), and the classical KLK5 form showed that out of all three mRNA transcripts, the classical form is predominantly expressed (found in more tissues and at higher expression levels) followed by KLK5-SV1. KLK5-SV1 is expressed at high levels in ovarian, pancreatic, breast and prostate cancer cell lines. KLK5-SV1 was also found to be expressed in 9/10 ovarian cancer tissues, but it was not found in one normal ovarian tissue tested. Hormonal regulation experiments suggest that KLK5-SV1 is regulated by steroid hormones in the BT-474 breast cancer cell line. Furthermore, this variant had significantly higher expression in normal prostate tissues compared to their matched cancer tissue counterparts. KLK5-SV1 may have clinical utility in various malignancies and should be further explored as a potential new biomarker for prostate and ovarian cancer.

Base Sequence↗
PubMed1995

Life-time exposure to dichlorvos affects behaviour of mature rats.

Wistar rats were exposed to dichlorvos [CAS number 62737]. Doses were 1/25; 1/50; and 1/100 LD50, that is 3.88; 1.94; 1.46; and 0.97 mg kg-1 DDVP, respectively, throughout gestation (GD1-birth) and during suckling via oral dosing to the dams, and then via the same doses by gavage for the rest of their lives. The offspring were tested in an open field (OF), a multiple T-maze, and in a 'novelty-induced-grooming' test to assess behavioural competence as adults. Dose-related increases in running time and incorrect choices in the maze were observed during the first 2 weeks of a 3-week study at 9-11 weeks of age. Horizontal activity was increased, vertical activity decreased, and defecation decreased in the OF at 9-12 weeks of age; these changes were again dose-related. Sleep was also affected at 12 weeks of age. AChE activity in the brain and in blood at sacrifice was roughly 40% to 65% of control, again reflecting the doses administered. The significantly changed behavioural profile of DDVP treated rats may serve as a useful biomarker to judge functional damage of CNS properties.

Acetylcholinesterase↗
PubMed2004

Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes.

BACKGROUND: Due to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using microarray GeneChip, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively. RESULTS: In addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16. CONCLUSION: This study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy.

Adenocarcinoma↗
PubMed2006

Recursive SVM feature selection and sample classification for mass-spectrometry and microarray data.

BACKGROUND: Like microarray-based investigations, high-throughput proteomics techniques require machine learning algorithms to identify biomarkers that are informative for biological classification problems. Feature selection and classification algorithms need to be robust to noise and outliers in the data. RESULTS: We developed a recursive support vector machine (R-SVM) algorithm to select important genes/biomarkers for the classification of noisy data. We compared its performance to a similar, state-of-the-art method (SVM recursive feature elimination or SVM-RFE), paying special attention to the ability of recovering the true informative genes/biomarkers and the robustness to outliers in the data. Simulation experiments show that a 5%- approximately 20% improvement over SVM-RFE can be achieved regard to these properties. The SVM-based methods are also compared with a conventional univariate method and their respective strengths and weaknesses are discussed. R-SVM was applied to two sets of SELDI-TOF-MS proteomics data, one from a human breast cancer study and the other from a study on rat liver cirrhosis. Important biomarkers found by the algorithm were validated by follow-up biological experiments. CONCLUSION: The proposed R-SVM method is suitable for analyzing noisy high-throughput proteomics and microarray data and it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features. The multivariate SVM-based method outperforms the univariate method in the classification performance, but univariate methods can reveal more of the differentially expressed features especially when there are correlations between the features.

Algorithms↗
PubMed2005

Bioinformatics approaches in clinical proteomics.

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.

Computational Biology↗
PubMed2006

New analytical tools for studying autoimmune diseases.

Protein microarrays with immobilised proteins on their surface are new analytical tools to overcome the current limits with respect to sample volume and throughput. They have a great potential as well with respect to multiplexing of complex samples, as a research tool and in diagnostics. Based on recent advances in this technology, new applications for protein microarrays in studying autoimmune diseases were described. Required tools for bioinformatical analysis of protein microarrays concerning normalisation, clustering and classification methods are discussed. The huge potential of this technology as well as future requirements such as protein microarray based diagnostics are presented.

Animals↗
PubMed1995

Mucosal cell proliferation in patients with hyperplastic colorectal polyps.

BACKGROUND: Morphologic, histochemical, and cytoproliferative characteristics differentiate hyperplastic polyps from adenomas. Even so, these polyps are indicators of populations with a high colorectal cancer risk. Since changes in mucosal cell proliferation are highly predictive biomarkers of the adenomacarcinoma sequence, this study examined the cytoproliferative profiles of mucosa bearing hyperplastic polyps, in a search for the significance of such lesions in bowel carcinogenesis. METHODS: Proliferative activity demonstrated by immunohistochemical detection of the in vitro uptake of bromodeoxyuridine was evaluated in the rectal mucosa of 26 patients with hyperplastic polyps, 35 with adenomas < 1 cm, 60 with adenomas > 1 cm, 10 with adenomas + synchronous hyperplastic polyps, and 400 controls. RESULTS: An upward shift of the major DNA synthesis zone to the intermediate and surface crypt compartments was found in all four patients groups. Significant hyperproliferation, on the other hand, was only observed in patients with large adenomas or hyperplastic polyps, or small adenomas and synchronous hyperplastic polyps. CONCLUSIONS: These findings suggest that hyperplastic polyps are morphologic indicators of tumor initiation in the rectal mucosa and its ensuing hyperproliferation. Abnormalities in the distribution of proliferation in the mucosa do not appear to be specifically associated with the morphogenesis of hyperplastics polyps.

Adenoma↗
PubMed2005

Immunoexpression of HBME-1, high molecular weight cytokeratin, cytokeratin 19, thyroid transcription factor-1, and E-cadherin in thyroid carcinomas.

To examine the immunohistochemical alterations associated with the histological dedifferentiation of thyroid carcinomas, we performed staining for HBME-1, high molecular weight cytokeratin (HCK), CK 19, thyroid transcription factor-1 (TTF-1) and E-cadherin (E-CD) on 125 various types of thyroid carcinomas. The HBME-1 staining was strong and diffuse in follicular carcinoma (FC), papillary carcinoma (PC), and poorly differentiated carcinoma (PDC), while it was rare in undifferentiated carcinoma (UC) as well as in benign lesions. Strong, diffuse staining for CK19 and HCK was predominantly found in PC, and these markers were not much found in other carcinomas. TTF-1 uniformly stained the tumor cells of all cases of PC, FC and Hurthle cell carcinoma (HC) and 42% of the PDC, while there was only focal staining in one case of the UC. Compared to the strong, diffuse reactivity in the benign lesions, E-CD staining was noted in 67% of PC, 80% of FC, 83% of HC, 58% of PDC and none of the UC. These results suggest that HBME-1 may be a marker for well-differentiated carcinomas while CK19 and HCK are phenotypic markers for papillary carcinoma. The loss or reduced expression of TTF-1 and E-CD may be markers for dedifferentiation.

Biomarkers, Tumor↗
PubMed2003

Detection of circulating melanoma cells by a two-marker polymerase chain reaction assay in relation to therapy.

Malignant melanoma is one of the most rapidly increasing cancer types, and patients with metastatic disease have a very poor prognosis. Detection of metastatic melanoma cells in circulation may aid the clinician in assessing tumor progression, metastatic potential, and response to therapy. Tyrosinase is a key enzyme in melanin biosynthesis. The gene is actively expressed in melanocytes and melanoma cells. Melan A is a differentiation antigen that is expressed in melanocytes. The presence of these molecules in blood is considered a marker for circulating melanoma cells. In this study, we analyzed the usefulness of this marker combination in evaluating the response to therapy in the blood of 30 patients with malignant melanoma. Circulating cells were detected by a reverse-transcriptase-polymerase-chain reaction. The tyrosinase expression was observed in 9 (30%) patients and Melan A in 19 (63.3%) patients before therapy. Following treatment, the tyrosinase mRNA was detected in only one patient, while Melan A transcripts were still present in 14 patients. We suggest that this molecular assay can identify circulating melanoma cells that express melanoma-associated antigens and may provide an early indication of therapy effectiveness.

Antigens, Neoplasm↗