In vitro chemosensitivity against enzastaurin correlates with gene expression of IL8 and GSK3-beta

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 13046-13046 ◽  
Author(s):  
O. Oberschmidt ◽  
U. Eismann ◽  
M. M. Lahn ◽  
J. Fleeth ◽  
F. Lüdtke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Drug Exposure ◽  
Human Tumor ◽  
Rna Isolation ◽  
Expression Data ◽  
Gene Expressions ◽  
In Vitro Chemosensitivity ◽  
Pkc Β

13046 Background: Enzastaurin (E) is an active antitumoral agent which selectively inhibits the β-isoform of protein kinase C (PKC-β). The compound blocks the enzyme’s ATP-binding site and signal transmission is abrogated resulting in the inhibition of neovascularization. The aim of the present study was to correlate gene expression with in vitro chemosensitivity of freshly explanted human tumor specimens. Such correlations in tumors taken directly from patients will help to rationally design subsequent clinical trials. Methods: Soft-agar colony forming assays were performed on freshly biopsied tumor cells exposed to various concentrations of E. Corresponding pieces of tumor specimens were shock-frozen and prepared for RNA isolation and cDNA generation followed by multiplex real-time PCR experiments. Gene expression data were correlated against cloning assay results. Results: Gene expression data of PKC-β1, PKC-β2, IL8RA, IL8RB, IL8, GSK3-β, and TGF-β were correlated against in vitro chemosensitivity pattern of E from 66 samples. After 1h-drug exposure gene expressions in sensitive versus resistant specimens were statistically significant with p = 0.013 for IL8 [median copy number (mcn): 1881 vs. 694; n = 66] and p = 0.012 for GSK3-beta (mcn: 1.6 vs. 7.0; n = 66). No correlation was detected for PKC-β1, PKC-β2, IL8RA, and IL8RB. Detection of TGF-β failed in most samples. Conclusions: Low expression of GSK3-β and high expression of IL8 correlate statistically significantly with increased in vitro sensitivity to E in freshly explanted human tumors. These findings may help direct further clinical development of this compound. No significant financial relationships to disclose.

scholarly journals Mining The Cancer Genome Atlas gene expression data for lineage markers in distinguishing bladder urothelial carcinoma and prostate adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ewe Seng Ch’ng
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
The Cancer Genome Atlas ◽  
Relative Importance ◽  
Expression Data ◽  
Gene Expressions ◽  
Urothelial Carcinomas ◽  
Cancer Genome Atlas ◽  
Lineage Markers ◽  
Genome Atlas

AbstractDistinguishing bladder urothelial carcinomas from prostate adenocarcinomas for poorly differentiated carcinomas derived from the bladder neck entails the use of a panel of lineage markers to help make this distinction. Publicly available The Cancer Genome Atlas (TCGA) gene expression data provides an avenue to examine utilities of these markers. This study aimed to verify expressions of urothelial and prostate lineage markers in the respective carcinomas and to seek the relative importance of these markers in making this distinction. Gene expressions of these markers were downloaded from TCGA Pan-Cancer database for bladder and prostate carcinomas. Differential gene expressions of these markers were analyzed. Standard linear discriminant analyses were applied to establish the relative importance of these markers in lineage determination and to construct the model best in making the distinction. This study shows that all urothelial lineage genes except for the gene for uroplakin III were significantly expressed in bladder urothelial carcinomas (p < 0.001). In descending order of importance to distinguish from prostate adenocarcinomas, genes for uroplakin II, S100P, GATA3 and thrombomodulin had high discriminant loadings (> 0.3). All prostate lineage genes were significantly expressed in prostate adenocarcinomas(p < 0.001). In descending order of importance to distinguish from bladder urothelial carcinomas, genes for NKX3.1, prostate specific antigen (PSA), prostate-specific acid phosphatase, prostein, and prostate-specific membrane antigen had high discriminant loadings (> 0.3). Combination of gene expressions for uroplakin II, S100P, NKX3.1 and PSA approached 100% accuracy in tumor classification both in the training and validation sets. Mining gene expression data, a combination of four lineage markers helps distinguish between bladder urothelial carcinomas and prostate adenocarcinomas.

In vitro gene expression data supporting a DNA non-reactive genotoxic mechanism for ochratoxin A

2007 ◽  
Vol 220 (2) ◽  
pp. 216-224 ◽  
Author(s):  
Leire Arbillaga ◽  
Amaia Azqueta ◽  
Joost H.M. van Delft ◽  
Adela López de Cerain
Keyword(s):  
Gene Expression ◽  
Ochratoxin A ◽  
Gene Expression Data ◽  
Expression Data

Building Gene Networks by Analyzing Gene Expression Profiles

2019 ◽  
pp. 27-44
Author(s):  
Crescenzio Gallo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Dna Microarrays ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Expression Data ◽  
Gene Expressions ◽  
Over Time

The possible applications of modeling and simulation in the field of bioinformatics are very extensive, ranging from understanding basic metabolic paths to exploring genetic variability. Experimental results carried out with DNA microarrays allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. In this chapter, the authors examine various methods for analyzing gene expression data, addressing the important topics of (1) selecting the most differentially expressed genes, (2) grouping them by means of their relationships, and (3) classifying samples based on gene expressions.

New Data on Robustness of Gene Expression Signatures in Leukemia: Comparison of Three Distinct Total RNA Preparation Procedures.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 4288-4288
Author(s):  
Marta Campo ◽  
Andrea Zangrando ◽  
Luca Trentin ◽  
Rui Li ◽  
Wei-min Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Rna Isolation ◽  
Expression Data ◽  
Microarray Technology ◽  
Isolation Method ◽  
Acute Leukemias ◽  
Preparation Methods ◽  
Total Rna ◽  
Total Rna Isolation

Abstract Gene expression microarrays had been used to classify known tumor types and various hematological malignancies (Yeoh et al, Cancer Cell 2002; Kohlmann et al, Genes Chromosomes Cancer 2003), enforcing the objective that microarray analysis could be introduced soon in the routine classification of cancer (Haferlach et al, Blood 2005). However, there’re still doubts about gene expression experiments performance in clinical laboratory diagnosis. For instance, the quality of starting material is a major concern in microarray technology and there are no data on the variation in gene expression profiles ensuing from different RNA extraction procedures. Here, as part of the internal multicenter MILE Study program, we assess the impact of different RNA preparation methods on gene expression data, analyzing 27 patients representative of nine different subtypes of pediatric acute leukemias. We compared the three currently most used protocols to isolate RNA for routine diagnosis (PCR assays) and microarray experiments. They are named as method A: lysis of mononuclear leukemia cells, followed by lysate homogeniziation, followed by total RNA isolation; method B: TRIzol RNA isolation, and method C: TRIzol RNA isolation followed by total RNA purification step. The methods were analyzed in triplicates for each sample (24) and additional three samples were performed in technical replicates of three data sets for each preparation (HG-U133 Plus 2.0). Method A results in better total RNA quality as demonstrated by 3′/5′ GAPD ratios and by RNA degradation plots. High comparability of gene expression data is found between samples in the same leukemia subclasses and collected with different RNA preparation methods thus demonstrating that sample preparation procedures do not impair the overall signal distribution. Unsupervised analyses showed clustering of samples first by each patient’s replicate conditions, then by leukemia type, and finally by leukemia lineage. In fact, B-ALL samples are clustered together, separately from T-ALL and AML, demonstrating that clustering reflects biological differences between leukemias and that the RNA isolation method is a secondary effect. Also, supervised cluster analyses highlight that samples are grouped depending on intra-lineage features (i.e. chromosomal aberrations) thus confirming the clustering organizations as reported in recent gene expression profiling studies of acute leukemias. Our study shows that biological features of pediatric acute leukemia classes largely exceed the variations between different total RNA sample preparation protocols. However, technical replicates analyses reveal that gene expression data from method A have the lowest degree of variation, are more reproducible and more precise as compared to the other two methods. Furthermore, compared to methods B and C, method A produces more differentially expressed probe sets between distinct leukemia classes and is therefore considered the more robust RNA isolation procedure for gene expression experiments using high-density microarray technology. We therefore conclude that method A (initial homogenization of the leukemia cell lysate followed by total RNA isolation) combined with a standardized microarray analysis protocol is highly reproducible and contributes to robustness of gene expression data and that this procedure is most practical for a routine laboratory use.

scholarly journals Gene Expression Data Classification With Kernel Principal Component Analysis

2005 ◽  
Vol 2005 (2) ◽  
pp. 155-159 ◽  
Author(s):  
Zhenqiu Liu ◽  
Dechang Chen ◽  
Halima Bensmail
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Expression Data ◽  
Human Tumor ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Expression Data ◽  
Nonlinear Version

One important feature of the gene expression data is that the number of genesMfar exceeds the number of samplesN. Standard statistical methods do not work well whenN<M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

scholarly journals Discovery and Validation of Novel Methylation Markers in Helicobacter pylori-Associated Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Huan Wang ◽  
Nian-Shuang Li ◽  
Cong He ◽  
Chuan Xie ◽  
Yin Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Helicobacter Pylori ◽  
Gene Expression Data ◽  
Functional Enrichment ◽  
Expression Data ◽  
H Pylori

Previous studies have shown that abnormal methylation is an early key event in the pathogenesis of most human cancers, contributing to the development of tumors. However, little attention has been given to the potential of DNA methylation patterns as markers for Helicobacter pylori- (H. pylori-) associated gastric cancer (GC). In this study, an integrated analysis of DNA methylation and gene expression was conducted to identify some potential key epigenetic markers in H. pylori-associated GC. DNA methylation data of 28 H. pylori-positive and 168 H. pylori-negative GC samples were compared and analyzed. We also analyzed the gene expression data of 18 H. pylori-positive and 145 H. pylori-negative GC cases. Finally, the results were verified by in vitro and in vivo experiments. A total of 5609 differentially methylated regions associated with 2454 differentially methylated genes were identified. A total of 228 differentially expressed genes were identified from the gene expression data of H. pylori-positive and H. pylori-negative GC cases. The screened genes were analyzed for functional enrichment. Subsequently, we obtained 28 genes regulated by methylation through a Venn diagram, and we identified five genes (GSTO2, HUS1, INTS1, TMEM184A, and TMEM190) downregulated by hypermethylation. HUS1, GSTO2, and TMEM190 were expressed at lower levels in GC than in adjacent samples ( P < 0.05 ). Moreover, H. pylori infection decreased HUS1, GSTO2, and TMEM190 expression in vitro and in vivo. Our study identified HUS1, GSTO2, and TMEM190 as novel methylation markers for H. pylori-associated GC.

Thymidylate synthase gene expression in solid tumors predicts for response to pemetrexed in vitro

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 13058-13058 ◽  
Author(s):  
U. Eismann ◽  
O. Oberschmidt ◽  
M. Ehnert ◽  
J. Fleeth ◽  
F. Lüdtke ◽  
...  
Keyword(s):  
Gene Expression ◽  
Statistical Tests ◽  
Human Tumor ◽  
Soft Agar ◽  
Expression Data ◽  
Glycinamide Ribonucleotide Formyltransferase ◽  
Cloning Assay ◽  
Thymidilate Synthase ◽  
Soft Agar Cloning

13058 Background: Pemetrexed (P) is a novel antifolate which targets thymidilate synthase (TS), dihydrofolate reductase (DHFR), and glycinamide ribonucleotide formyltransferase (GARFT). The aim of the present study was to identify gene expression thresholds for these enzymes in human tumor specimens in order to separate P-sensitive from P-resistant patients. Methods: Soft-agar cloning assays were performed on freshly biopsied tumor cells exposed one hour to clinically achievable concentrations of P. In parallel, RNA was isolated, transcribed to cDNA and subsequently used for multiplex real-time PCR. Gene expression data were normalized against beta-actin transcripts followed by correlation against cloning assay results. Iterative calculations (fourfold analysis) were done for each enzyme separately to find the best cutoff for prediction of sensitivity to P. Results: Sensitive and resistant tumor samples were statistically significant different in gene expression of TS, DHFR, and GARFT (p < 0.003). 81% of all tumors with a TS copy number < 144 (related to 104 copies β-actin) were sensitive to P in vitro. (specificity = 0.69; chi2 = 14.14). Statistical tests demonstrated that gene expression of TS, DHFR, and GARFT are dependent variables and that TS transcription is the leading variable. The combination of TS, DHFR, and GARFT expression data was not superior to TS alone. Conclusions: TS expression is the most meaningful predictor for sensitivity (≤ 144 copies) or resistance (> 144 copies) to Pemetrexed in fresh tumor tissue. This observation forms a rationale for clinical trials using TS expression as predictor for clinical response. No significant financial relationships to disclose.

scholarly journals The Role of Scale in the Estimation of Cell-type Proportions

2019 ◽  
Author(s):  
Gregory J. Hunt ◽  
Johann A. Gagnon-Bartsch
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Hybrid Approach ◽  
Expression Data ◽  
Cell Type ◽  
Statistical Efficiency ◽  
Gene Expressions ◽  
New Approach ◽  
Cell Type Composition ◽  
Type Composition

ABSTRACTComplex tissues are composed of a large number of different types of cells, each involved in a multitude of biological processes. Consequently, an important component to understanding such processes is understanding the cell-type composition of the tissues. Estimating cell type composition using high-throughput gene expression data is known as cell-type deconvolution. In this paper, we first summarize the extensive deconvolution literature by identifying a common regression-like approach to deconvolution. We call this approach the Unified Deconvolution-as-Regression (UDAR) framework. While methods that fall under this framework all use a similar model, they fit using data on different scales. Two popular scales for gene expression data are logarithmic and linear. Unfortunately, each of these scales has problems in the UDAR framework. Using log-scale gene expressions proposes a biologically implausible model and using linear-scale gene expressions will lead to statistically inefficient estimators. To overcome these problems, we propose a new approach for cell-type deconvolution that works on a hybrid of the two scales. This new approach is biologically plausible and improves statistical efficiency. We compare the hybrid approach to other methods on simulations as well as a collection of eleven real benchmark datasets. Here, we find the hybrid approach to be accurate and robust.deconvolution, gene expression, microarray, RNA-seq

Selection of Reference Genes for Transcription Studies on Adventitious Root Induction in Olive (Olea Europaea L.) Microshoots Considering Co-expression and Average Transcriptional Stability

2020 ◽  
Author(s):  
Carlos Noceda ◽  
Augusto Peixe ◽  
Birgit Arnholdt-Schmitt
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Reference Genes ◽  
Ad Hoc ◽  
Root Induction ◽  
Expression Data ◽  
Internal Reference ◽  
Experimental Conditions ◽  
Selection Of

Abstract BackgroungSelection of reference genes (RGs) for normalization of PCR-gene expression data includes two crucial steps: determination of the among-sample transcriptionally more stable genes and subsequent choosing of the most suitable genes as internal controls. Both steps can be carried-out through generally accepted strategies each having different strengths and weaknesses. The present study proposes to reinforce normalization of gene expression data by integrating and adding analytical revision at critical steps of those accepted procedures. Especially crucial is to counterbalance a higher representative number of RGs with a correspondent increase in their average transcriptional instability or a generalised co-expression trend among the samples. This methodological study used in vitro olive adventitious rooting as an experimental system, since the underlying morphogenetic process -wich is common to diverse species- is still not completely understood.ResultsFirstly, RG candidates were ranked according to transcriptional stability following a simple statistical method that reduces biasing effects of concomitant, systematic biological variations associated to experimental conditions, such as the variations caused by gene co-regulation. Those types of systematic co-variation are unconsidered by several popular ad hoc informatics programmes. To select the adequate genes among those already ranked, an algorithm of one of the ad hoc informatics programmes (GeNorm) was adapted to allow partial automatization of RG selection for any strategy of transcriptional-gene stability ordering. In order to delve into the resulting possible RG sets suitability for inter-assay comparisons and technical-error compensation, separate statistics were formulated. The achieved results were compared with those obtained by standard stability ranking methods. Finally, a double evaluation was performed to accurately contrast two choice RG sets. The whole strategy was applied to a panel considering several independent factors, but the suitability of the obtained putative RG sets was tested for cases restricted to fewer variables. H2B, OUB and ACT are valid for normalization in transcriptional studies on olive microshoot rooting when comparing treatments, time points and assays.ConclusionsThe set of genes identified as internal reference is now available for wider expression studies on any target gene in similar biological systems. The overall methodology aims to constitute a guide for general application.

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