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At least 595 records · Page 33Linked to original sources
PubMed2004

S100A11 gene identified by in-house cDNA microarray as an accurate predictor of lymph node metastases of gastric cancer.

Gastric cancer is one of the most common malignancies in the world, and in Asian countries its incidence and mortality rates are very high. Worldwide, Japan ranks first in the incidence of this type of cancer for both sexes. To shed light on the mechanisms underlying the development and/or progression of gastric cancer, we compared the expression profiles in gastric cancer cells obtained from surgical dissection of 20 gastric adenocarcinoma specimens with those in the corresponding non-cancerous mucosa, by cDNA microarray analysis. In total, 8,000 cDNA clones were randomly picked up and their 5'-end nucleotide sequences were determined. On the basis of sequence information, 4,608 independent clones were selected and used to produce the cDNA microarray. We identified 26 genes that were commonly up-regulated and 44 genes that were commonly down-regulated in cancerous tissues. To validate the cDNA microarray analysis, real-time PCR was performed. We found that gene S100A11 expression was associated with the development of lymph node metastases. S100A11 gene expression was clearly up-regulated in specimens from patients with lymph node metastases relative to those from patients without lymph node metastases. S100A11 gene expression status was useful to distinguish gastric cancers with lymph node metastases from those without lymph node metastasis. This genome-wide information contributes to an improved understanding of molecular changes during the development of gastric cancers. It may also help clinicians predict the development of lymph node metastases and assist researchers in identifying novel therapeutic targets for patients with gastric cancer.

Adenocarcinoma↗
PubMed2006

Immune cell transcriptome datasets reveal novel leukocyte subset-specific genes and genes associated with allergic processes.

BACKGROUND: The precise function of various resting and activated leukocyte subsets remains unclear. For instance, mast cells, basophils, and eosinophils play important roles in allergic inflammation but also participate in other immunologic responses. One strategy to understand leukocyte subset function is to define the expression and function of subset-restricted molecules. OBJECTIVE: To use a microarray dataset and bioinformatics strategies to identify novel leukocyte markers as well as genes associated with allergic or innate responses. METHODS: By using Affymetrix microarrays, we generated an immune transcriptome dataset composed of gene profiles from all of the major leukocyte subsets, including rare enigmatic subsets such as mast cells, basophils, and plasma cells. We also assessed whether analysis of genes expressed commonly by certain groups of leukocytes, such as allergic leukocytes, might identify genes associated with particular responses. RESULTS: Transcripts highly restricted to a single leukocyte subset were readily identified (>2000 subset-specific transcripts), many of which have not been associated previously with leukocyte functions. Transcripts expressed exclusively by allergy-related leukocytes revealed well known as well as novel molecules, many of which presumably contribute to allergic responses. Likewise, Nearest Neighbor Analysis of genes coexpressed with Toll-like receptors identified genes of potential relevance for innate immunity. CONCLUSION: Gene profiles from all of the major human leukocyte subsets provide a powerful means to identify genes associated with single leukocyte subsets, or different types of immune response. CLINICAL IMPLICATIONS: A comprehensive dataset of gene expression profiles of human leukocytes should provide new targets or biomarkers for human inflammatory diseases.

Gene Expression Profiling↗
PubMed2024

Decoding the genetic landscape of allergic rhinitis: a comprehensive network analysis revealing key genes and potential therapeutic targets.

BACKGROUND: Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe. METHODS: We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes. RESULTS: Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR. CONCLUSION: By fuzing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.

Humans↗
PubMed2006

Comprehensive profiling and localisation of the matrix metalloproteinases in urothelial carcinoma.

The matrix metalloproteinases (MMPs) are endopeptidases which break down the extracellular matrix and regulate cytokine and growth factor activity. Several MMPs have been implicated in the promotion of invasion and metastasis in a broad range of tumours including urothelial carcinoma. In this study, RNA from 132 normal bladder and urothelial carcinoma specimens was profiled for each of the 24 human MMPs, the four endogenous tissue inhibitors of MMPs (TIMPs) and several key growth factors and their receptors using quantitative real time RT-PCR. Laser capture microdissection (LCM) of RNA from 22 tumour and 11 normal frozen sections was performed allowing accurate RNA extraction from either stromal or epithelial compartments. This study confirms the over expression in bladder tumour tissue of well-documented MMPs and highlights a range of MMPs which have not previously been implicated in the development of urothelial cancer. In summary, MMP-2, MT1-MMP and the previously unreported MMP-28 were very highly expressed in tumour samples while MMPs 1, 7, 9, 11, 15, 19 and 23 were highly expressed. There was a significant positive correlation between transcript expression and tumour grade for MMPs 1, 2, 8, 10, 11, 12, 13, 14, 15 and 28 (P < 0.001). At the same confidence interval, TIMP-1 and TIMP-3 also correlated with increasing tumour grade. LCM revealed that most highly expressed MMPs are located primarily within the stromal compartment except MMP-13 which localised to the epithelial compartment. This work forms the basis for further functional studies, which will help to confirm the MMPs as potential diagnostic and therapeutic targets in early bladder cancer.

Biomarkers, Tumor↗
PubMed2004

caGEDA: a web application for the integrated analysis of global gene expression patterns in cancer.

The explosion of microarray data from pilot studies, basic research and large-scale clinical trials requires the development of integrative computational tools that can not only analyse gene expression patterns but that can also evaluate the methods of analysis adopted and then provide a boost to post-analysis translational interpretation of those patterns. We have developed a web application called caGEDA (cancer gene expression data analyzer) that can: (1) upload gene expression profiles from cDNA or oligonucleotide microarrays; (2) conduct a diverse range of serial linear normalisations; (3) identify differentially expressed genes using a variety of tests - either threshold or permutation tests; (4) produce tables of literature references to papers reporting that specific genes (identified by accession numbers) are up- or down-regulated in specific cancers; (5) estimate the error of sample class prediction using the significant gene set for features; (6) perform low-bias and accurate validated learning using three computational validation techniques (leave-one out validation, k-fold validation, random re-sampling validation); and (7) validate a classifier with a randomly selected or user-defined validation set. Significant genes are reported in a table of links to entries in the following databases: Locus Link, Genome View, UCSC, Ensembl, UniGene, dbSNP, AmiGO and OMIM. caGEDA is seamlessly integrated via embedded forms with UCSD's (University of California at San Diego) 2HAPI server (for medical subject heading (MeSH) term exploration) and EZ-Retrieve (to identify common transcription factors located upstream of sets of genes that exhibit similar modes of differential expression). caGEDA offers a variety of previously described and novel tests for differentially expressed genes, most notably the permutation percentile separability test, which is most appropriate for identifying genes that are significantly differentially expressed in a subset of patients. caGEDA, which is open source and free to academic users, will soon be greatly enhanced by operating with the components of the National Cancer Institute's new cancer bioinformatics grid (caBIG).

Biomarkers, Tumor↗
PubMed2007

Proteomic profiling of the amniotic fluid to detect inflammation, infection, and neonatal sepsis.

BACKGROUND: Proteomic analysis of amniotic fluid shows the presence of biomarkers characteristic of intrauterine inflammation. We sought to validate prospectively the clinical utility of one such proteomic profile, the Mass Restricted (MR) score. METHODS AND FINDINGS: We enrolled 169 consecutive women with singleton pregnancies admitted with preterm labor or preterm premature rupture of membranes. All women had a clinically indicated amniocentesis to rule out intra-amniotic infection. A proteomic fingerprint (MR score) was generated from fresh samples of amniotic fluid using surface-enhanced laser desorption ionization (SELDI) mass spectrometry. Presence or absence of the biomarkers of the MR score was interpreted in relationship to the amniocentesis-to-delivery interval, placental inflammation, and early-onset neonatal sepsis for all neonates admitted to the Newborn Special Care Unit (n = 104). Women with "severe" amniotic fluid inflammation (MR score of 3 or 4) had shorter amniocentesis-to-delivery intervals than women with "no" (MR score of 0) inflammation or even "minimal" (MR score of 1 or 2) inflammation (median [range] MR 3-4: 0.4 d [0.0-49.6 d] versus MR 1-2: 3.8 d [0.0-151.2 d] versus MR 0: 17.0 d [0.1-94.3 d], p < 0.001). Nonetheless, a "minimal" degree of inflammation was also associated with preterm birth regardless of membrane status. There was a significant association between the MR score and severity of histological chorioamnionitis (r = 0.599, p < 0.001). Furthermore, neonatal hematological indices and early-onset sepsis significantly correlated with the MR score even after adjusting for gestational age at birth (OR for MR 3-4: 3.3 [95% CI, 1.1 to 9.2], p = 0.03). When compared with other laboratory tests routinely used to diagnose amniotic fluid inflammation and infection, the MR score had the highest accuracy to detect inflammation (white blood cell count > 100 cells/mm3), whereas the combination of Gram stain and MR score was best for rapid prediction of intra-amniotic infection (positive amniotic fluid culture). CONCLUSIONS: High MR scores are associated with preterm delivery, histological chorioamnionitis, and early-onset neonatal sepsis. In this study, proteomic analysis of amniotic fluid was shown to be the most accurate test for diagnosis of intra-amniotic inflammation, whereas addition of the MR score to the Gram stain provides the best combination of tests to rapidly predict infection.

Adolescent↗
PubMed2005

Identification of hemoglobin-alpha and -beta subunits as potential serum biomarkers for the diagnosis and prognosis of ovarian cancer.

The development of new biomarkers for ovarian cancer is clearly necessary for the improved detection and monitoring of the disease. Surface enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) can be employed in the identification of differentially expressed proteins in cancer cells. The objective of this study was, then, to identify potential diagnostic serological biomarkers for ovarian cancer. We performed protein expression difference analyses of 45 serum samples using SELDI protein chip array. Forty-five sera obtained from ovarian cancer patients (n=35) and normal healthy females (n=10), were profiled on the surface of SELDI protein chip. The candidate biomarkers were purified by CM-Sepharose, and their N-terminal amino sequence was determined. The amounts of hemoglobin (Hb) in cancer patient's sera versus that of normal sera were measured by ELISA. Nine sera proteins that were found to be significantly differentially expressed (P<0.05) between the sera of ovarian cancer patients and that of normal healthy females were selected using the WCX2 array. The most distinctive polypeptide peaks detected in the ovarian cancer samples were at 15.1 and 15.8 kDa and these two peaks were identified as the hemoglobin-alpha (Hb-alpha) and -beta (Hb-beta) chain, respectively. ELISA indicated that the sensitivity for intact Hb level was 77% in sera obtained from ovarian cancer patients, as compared with normal healthy female sera. In conclusion, two ovarian cancer biomarker proteins were discovered and identified as Hb-alpha and Hb-beta. Hb levels were significantly different in ovarian cancer serum samples and those obtained from normal healthy females, as determined by ELISA. Additional studies are required to further validate Hb-alpha and Hb-beta biomarkers.

Adult↗
PubMed2026

Personalised Nutraceutical Treatment Guided by MTHFR Genotype in Mental Health: A Retrospective Cohort Study.

BACKGROUND & AIMS: One-carbon metabolism plays a central role in neurotransmitter synthesis, methylation capacity, and neurobiological resilience. Variants in the methylenetetrahydrofolate reductase (MTHFR) gene can reduce enzymatic activity, affecting folate- and methionine-cycle functions and potentially influencing biological pathways relevant to mood and anxiety disorders. Personalised nutraceutical treatment strategies, particularly those addressing methylation capacity through targeted B-vitamin, folate, and adjunctive metabolic interventions are increasingly implemented in integrative clinical practice, yet evidence regarding their clinical outcomes remains limited. METHODS: We conducted a retrospective cohort study of 50 adults attending an integrative general practice clinic for anxiety and/or depression. All received personalised nutraceutical treatment informed by clinical assessment, laboratory testing and, for 37/50 patients, MTHFR genotyping. Psychological distress was measured using the Kessler-10 (K10) scale at baseline and approximately three months later. Secondary analyses evaluated whether outcomes differed by MTHFR genotype, whether specific supplements (e.g., L-methylfolate and SAMe) were associated with greater improvement, whether biomarker changes correlated with symptom change, and the safety/tolerability profile. RESULTS: Across the full cohort, mean K10 scores significantly decreased by four points over the treatment period, with 72% of patients showing clinical improvement. Reductions in psychological distress were seen across all MTHFR genotypes, including individuals with homozygous variant genotypes. Supplement-specific analyses showed improvement among those receiving methylfolate or SAMe, although the differences were not statistically significant. Following nutraceutical treatment, biomarker analyses demonstrated significant increases in serum vitamin B12 and modest reductions in homocysteine, but biomarker shifts did not correlate strongly with K10 change. No serious adverse events or clinically significant abnormalities in liver or renal function were identified. CONCLUSIONS: In this real-world primary care cohort, personalised nutraceutical treatment, grounded in one-carbon metabolism support and applied alongside usual care, was associated with clinically meaningful reductions in psychological distress. Outcomes were comparable across MTHFR genotypes when treatments were appropriately tailored, suggesting that genotype and biomarker-informed nutraceutical strategies may mitigate potential metabolic disadvantages. These findings support further controlled research into precision nutraceutical psychiatry for anxiety and depression. Secondary analyses of genotype subgroup, specific supplements, and biomarker-outcome associations are reported alongside Benjamini-Hochberg FDR-adjusted p-values and should be interpreted as hypothesis-generating.

Humans↗
PubMed2004

Molecular classification and molecular genetics of human lung cancers.

Recent advances in the molecular classification of lung carcinomas and the identification of causative genetic alterations will likely lead to improvements in the diagnosis and treatment of patients with lung cancer. It is now possible to identify gene expression profiles that associate with patient outcome in lung carcinomas, in particular adenocarcinoma. Furthermore, patient survival has been shown to correlate with lung cancer oligonucleotide microarray expression profiles. Large-scale microarray technology may allow for the identification of useful biomarkers for early cancer detection. Oligonucleotide microarray data can be optimized by relating them to protein expression levels in tissue microarrays, by annotation with mutational data, and with results of testing for post-translational modification of cellular proteins. These data may be useful in tailoring chemotherapeutic protocols to individual tumors and identifying new targets for therapeutic intervention.

Adenocarcinoma↗
PubMed2005

Hepatic angiomyolipoma and hepatic stellate cells share a similar gene expression profile.

BACKGROUND AND AIMS: Angiomyolipomas (AMLs) of the liver are rare neoplasms composed of large epithelioid cells with intermixed fat and blood vessels. Hepatic AMLs have no clear normal-cell counterpart in the liver. However, AMLs and stellate cells both are positive for neural crest-derived markers including HMB-45 antigen. METHODS: To further explore the similarities between hepatic AMLs and stellate cells, gene expression of a hepatic AML was studied by cDNA microarray. Real-time polymerase chain reaction was used to confirm gene expression. Hepatic stellate cells can be quiescent, activated, or have a myofibroblastic phenotype depending on their state of activation. Expression of known markers of activated stellate cells was compared between the AML, activated primary mouse stellate cells, and stellate cell lines with activated and myofibroblastic phenotypes. Next, 5 novel genes from the AML were selected because they were not previously known to be markers of stellate cells and mRNA expression measured in the activated mouse stellate cells and in myofibroblastic stellate cell lines. Finally, expression levels of 10 novel genes were determined in 5 cirrhotic and 5 noncirrhotic human livers. RESULTS: Overexpression of known markers of activated stellate cells including transforming growth factor beta (TGF- beta ), smooth muscle actin, and collagen was found in the hepatic AML. Three of 5 novel markers that were identified in the AML, RRAD (Ras-related associated with diabetes), CTSK (cathepsin K), and NIBAN were also found to be overexpressed in activated stellate cells compared with quiescent or myofibroblastic stellate cells. In addition, 9 of 10 novel genes overexpressed in AML were also overexpressed in cirrhotic human livers versus noncirrhotic livers. CONCLUSIONS: Hepatic AMLs share a similar gene expression profile and may differentiate toward activated stellate cells.

Angiomyolipoma↗
PubMed2004

Molecular profiling of prostate cancer.

The ability to distinguish between aggressive and nonaggressive tumors has not changed despite vast improvements in the detection of prostate cancer (PCA). To improve predictive accuracy, additional PCA-specific biomarkers must be identified and it is the emerging microarray technology and gene expression profiling that appear to be capable of achieving this goal. Through comparisons of a number of published microarray studies of PCA, several potential biomarkers appear on the horizon, including the serine protease Hepsin, a-methylacyl CoA racemase, and the human homologue of the Drosophila protein Enhancer of Zeste. Although these markers will move toward validation by eventual protein expression studies, another aspect of microarray expression, global signature expression patterns through multidimensional scaling, appears to be promising in distinguishing between aggressive and nonaggressive forms of PCA or in distinguishing PCA from benign prostatic hyperplasia or normal prostate tissue.

Gene Expression Profiling↗
PubMed2005

Internet-based Profiler system as integrative framework to support translational research.

BACKGROUND: Translational research requires taking basic science observations and developing them into clinically useful tests and therapeutics. We have developed a process to develop molecular biomarkers for diagnosis and prognosis by integrating tissue microarray (TMA) technology and an internet-database tool, Profiler. TMA technology allows investigators to study hundreds of patient samples on a single glass slide resulting in the conservation of tissue and the reduction in inter-experimental variability. The Profiler system allows investigator to reliably track, store, and evaluate TMA experiments. Here within we describe the process that has evolved through an empirical basis over the past 5 years at two academic institutions. RESULTS: The generic design of this system makes it compatible with multiple organ system (e.g., prostate, breast, lung, renal, and hematopoietic system,). Studies and folders are restricted to authorized users as required. Over the past 5 years, investigators at 2 academic institutions have scanned 656 TMA experiments and collected 63,311 digital images of these tissue samples. 68 pathologists from 12 major user groups have accessed the system. Two groups directly link clinical data from over 500 patients for immediate access and the remaining groups choose to maintain clinical and pathology data on separate systems. Profiler currently has 170 K data points such as staining intensity, tumor grade, and nuclear size. Due to the relational database structure, analysis can be easily performed on single or multiple TMA experimental results. The TMA module of Profiler can maintain images acquired from multiple systems. CONCLUSION: We have developed a robust process to develop molecular biomarkers using TMA technology and an internet-based database system to track all steps of this process. This system is extendable to other types of molecular data as separate modules and is freely available to academic institutions for licensing.

Automation↗
PubMed2006

Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics.

The goal of this chapter is to introduce some of the available computational methods for expression analysis. Genomic and proteomic experimental techniques are briefly discussed to help the reader understand these methods and results better in context with the biological significance. Furthermore, a case study is presented that will illustrate the use of these analytical methods to extract significant biomarkers from high-throughput microarray data. Genomic and proteomic data analysis is essential for understanding the underlying factors that are involved in human disease. Currently, such experimental data are generally obtained by high-throughput microarray or mass spectrometry technologies among others. The sheer amount of raw data obtained using these methods warrants specialized computational methods for data analysis. Biomarker discovery for neurological diagnosis and prognosis is one such example. By extracting significant genomic and proteomic biomarkers in controlled experiments, we come closer to understanding how biological mechanisms contribute to neural degenerative diseases such as Alzheimers' and how drug treatments interact with the nervous system. In the biomarker discovery process, there are several computational methods that must be carefully considered to accurately analyze genomic or proteomic data. These methods include quality control, clustering, classification, feature ranking, and validation. Data quality control and normalization methods reduce technical variability and ensure that discovered biomarkers are statistically significant. Preprocessing steps must be carefully selected since they may adversely affect the results of the following expression analysis steps, which generally fall into two categories: unsupervised and supervised. Unsupervised or clustering methods can be used to group similar genomic or proteomic profiles and therefore can elucidate relationships within sample groups. These methods can also assign biomarkers to sub-groups based on their expression profiles across patient samples. Although clustering is useful for exploratory analysis, it is limited due to its inability to incorporate expert knowledge. On the other hand, classification and feature ranking are supervised, knowledge-based machine learning methods that estimate the distribution of biological expression data and, in doing so, can extract important information about these experiments. Classification is closely coupled with feature ranking, which is essentially a data reduction method that uses classification error estimation or other statistical tests to score features. Biomarkers can subsequently be extracted by eliminating insignificantly ranked features. These analytical methods may be equally applied to genetic and proteomic data. However, because of both biological differences between the data sources and technical differences between the experimental methods used to obtain these data, it is important to have a firm understanding of the data sources and experimental methods. At the same time, regardless of the data quality, it is inevitable that some discovered biomarkers are false positives. Thus, it is important to validate discovered biomarkers. The validation process may be slow; yet, the overall biomarker discovery process is significantly accelerated due to initial feature ranking and data reduction steps. Information obtained from the validation process may also be used to refine data analysis procedures for future iteration. Biomarker validation may be performed in a number of ways - bench-side in traditional labs, web-based electronic resources such as gene ontology and literature databases, and clinical trials.

Animals↗
PubMed2005

Hematopathology approaches to diagnosis and prognosis of indolent B-cell lymphomas.

The advent of new technologies has contributed to improvements in the diagnosis and classification of the non-Hodgkin lymphomas (NHL). Use of a more extensive test menu of paraffin active monoclonal antibodies for immunohistochemistry, molecular cytogenetic studies including standard cytogenetics, multi-color fluorescence in-situ hybridization (FISH), polymerase chain reaction and locus-specific FISH, as well as developments in high-resolution techniques including microarray gene expression profiling and array comparative genomic hybridization (CGH) allow more accurate diagnosis and precise definition of biomarkers of value in risk stratification. The identification of disease-specific gene lists resulting from expression profiling provides a number of potential protein targets that can be validated using immunohistochemistry. We will highlight how improvements in our understanding of lymphoma biology rapidly facilitate the development of new diagnostic reagents that could be used to alter clinical practice. These changing trends allow the development of new diagnostic strategies used to render accurate sub-classification of entities within the category of indolent B-cell lymphomas, including their distinction from related but more aggressive disorders, such as mantle cell lymphoma. A comprehensive understanding of the biology of these distinct lymphoid tumors will allow us to identify novel disease-related genes and should facilitate the development of improved diagnostics, outcome prediction, and personalized approaches to treatment.

Cell Transformation, Neoplastic↗
PubMed2005

Risk factors associated with beta-amyloid(1-42) immunotherapy in preimmunization gene expression patterns of blood cells.

BACKGROUND: A phase 2a, double-blind, placebo-controlled, multicenter study was conducted to evaluate safety, tolerability, and pilot efficacy of immunization with beta-amyloid((1-42)) in patients with Alzheimer disease. Six immunizations were planned but were halted when meningoencephalitis was recognized as an adverse event in 6% of immunized patients. OBJECTIVE: To identify biomarkers associated with both the risk of meningoencephalitis and antibody responsiveness. PARTICIPANTS: One hundred fifty-three patients with mild to moderate Alzheimer disease.Main Outcome Measure Association between response to immunization and preimmunization expression levels of 8239 messenger RNA transcripts expressed in peripheral blood mononuclear cells that had been collected at the screening visit. RESULTS: Expression patterns of genes related to apoptosis and proinflammatory pathways (tumor necrosis factor pathway in particular) were identified as biomarkers of risk for the development of meningoencephalitis. Expression patterns of genes related to protein synthesis, protein trafficking, DNA recombination, DNA repair, and cell cycle were strongly associated with IgG response to immunization. CONCLUSIONS: Candidate biomarkers associated with risk of immunotherapy-related meningoencephalitis were detected in blood collected prior to treatment. In addition, a different set of biomarkers were identified that were associated with the desired outcome of IgG response.

Alzheimer Disease↗
PubMed2005

Application of an electric DNA-chip for the expression analysis of bioprocess-relevant marker genes of Bacillus subtilis.

The knowledge of critical process-relevant genes can be used for an improved control of bioprocesses. So far bioprocess-relevant marker genes can be analyzed by established expression analysis methods only off-line. In this study, an alternative approach for a potential at-line monitoring of gene expression during bioprocesses is suggested. This approach is based on the measurement of specific mRNAs on an electric DNA-chip in connection with a magnetic bead-based sandwich hybridization. In order to allow an at-line measurement of specific mRNAs an improved method for a fast and partially automated isolation of high quality-RNA samples was developed. The expression analysis of the electric DNA-chip was compared with optical DNA micro arrays and the real time RT-PCR for three selected process-relevant genes of Bacillus subtilis. We demonstrate that the mRNA analysis by means of the electric DNA-chip gives similar results compared to the micro array analysis and the real time RT-PCR technique.

Bacillus subtilis↗
PubMed2005

Molecular classification of melanoma using real-time quantitative reverse transcriptase-polymerase chain reaction.

BACKGROUND: The early detection and characterization of metastatic melanoma are important for prognosis and management of the disease. Molecular methods are more sensitive in detecting occult lymph node metastases compared with standard histopathology and are reported to have utility in clinical diagnostics. METHODS: Using real-time quantitative reverse transcriptase-polymerase chain reaction ([q]RT-PCR), the authors examined 36 samples (30 melanomas, 4 benign nevi, and 2 reactive lymph nodes) for the expression of 20 melanoma-related genes that function in cell growth and differentiation (epidermal growth factor receptor [EGFR], WNT5A, BRAF, FOS, JUN, MATP, and TMP1), cell proliferation (KI-67, TOP2A, BUB1, BIRC5, and STK6), melanoma progression (CD63, MAGEA3, and GALGT), and melanin synthesis (TYR, MLANA, SILV, PAX3, and MITF). In addition, samples were tested for mutations in BRAF (exons 11 and 15) and NRAS (exons 2 and 3). RESULTS: Hierarchical clustering analysis of the expression data was able to distinguish between the melanoma and nonmelanoma samples and further stratified the melanoma samples into two groups differentiated by high expression of the genes involved in beta-catenin activation (EGFR and WNT5A) and the MAPK/ERK pathway (BRAF, FOS, and JUN). Eighteen of the 28 patients (64%) were found to have mutations in either exon 15 of BRAF (V599 substitution) or codon 61 of NRAS. The mutations were mutually exclusive and did not appear to be associated with the different expression subtypes. CONCLUSIONS: The results of the current study demonstrate that real-time qRT-PCR can be analyzed using hierarchical clustering to identify expression patterns that differentiate between melanomas and other tissue types. Using a supervised analysis of the data, the authors found that the best discriminators for molecularly distinguishing between melanoma, benign nevi, and lymph nodes were MLANA, CD63, and BUB1. These markers could have diagnostic utility for the detection of melanoma micrometastasis in sentinel lymph nodes.

Biomarkers, Tumor↗
PubMed2002

Application of differential display to identify genes for lung cancer detection in peripheral blood.

A blood assay for detection of lung cancer biomarkers could significantly improve cancer patient prognosis and survival rates. Amplified fragment length polymorphism-differential display (AFLP-DD) was used to identify gene transcripts found in lung cancer tissue and the peripheral blood of lung cancer patients. The clones were evaluated for gene expression in lung cancer tissue, peripheral blood of lung cancer patients and healthy volunteers' blood. The isolated gene transcript clones were found to be from the syndecan 1 gene, collagen 1 gene and 2 novel genes. All 4 transcripts were expressed in normal lung tissue, 4 cultured primary lung cells and 6 lung cancer cell lines. RNA was isolated from peripheral blood samples of 69 lung cancer patients. Reverse transcriptase polymerase chain reaction (RT-PCR) was used to test for the presence of cytokeratin 19 and the 4 gene mRNA transcripts in blood RNA. The positive detection rate of at least 1 of the 5 transcripts was 79% for lung adenocarcinoma and 62% for squamous carcinoma. Using RT-PCR, at least 1 of the markers was found in 53% of stage I patients, 100% of stage II, 71% of stage III and 81% of stage IV lung cancer patients. Blood samples from 20 healthy volunteers were also tested, but only 1 of the 5 transcripts was found in 1 patient. These new molecular markers may aid early detection, staging and follow-up of lung cancer patients by RNA isolated from blood.

Adult↗