scholarly journals Gene expression data from acetaminophen-induced toxicity in human hepatic in vitro systems and clinical liver samples

Data in Brief ◽  
2016 ◽  
Vol 7 ◽  
pp. 1052-1057 ◽  
Author(s):  
Robim M. Rodrigues ◽  
Olivier Govaere ◽  
Tania Roskams ◽  
Tamara Vanhaecke ◽  
Vera Rogiers ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
In Vitro Systems

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

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.

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.

COXEN prediction of antineoplastic drug sensitivity in bladder cancer patients.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 365-365
Author(s):  
Shalin Kothari ◽  
Daniel Gustafson ◽  
Keith Killian ◽  
James Costello ◽  
Daniel C. Edelman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
Gene Expression Data ◽  
Drug Sensitivity ◽  
Model Building ◽  
Response To Therapy ◽  
Antineoplastic Drug ◽  
In Vitro Assays ◽  
Expression Data

365 Background: COXEN (Co-eXpression ExtrapolatioN) uses molecular profiles as a “rosetta stone” for translating drug sensitivities of one set of cancers into predictions for another completely independent set of cell lines or human tumors. The ability of COXEN to predict drug effectiveness in pts using tumor samples from in vitro assays is unique. Methods: We tested the predictive value of COXEN for standard chemotherapies in a cohort of bladder cancer pts. Total RNA was extracted from formalin fixed paraffin embedded (FFPE) tissue and converted to cDNA, amplified with Ovation FFPE WTA, and hybridized to a GeneChip Human Genome U133 Plus 2.0 array. Using gene expression data from 278 independent bladder tumors, COXEN scores were generated using bioinformatics models originally built using the NCI-60 cell line panel and a model building algorithm (MiPP). Gene expression data was processed to score 76 FDA approved antineoplastic drugs. Results: A total of 24 samples were tested (15 tumors with 1 sample and 9 tumors with 2 biological replicas (2 samples from the same tumor)) from 15 pts who received chemotherapy (median age 64 (41-74); 73% male; with muscle invasive bladder cancer (MIBC) (12/15, 80%) or metastatic bladder cancer (mBC) (3/15, 20%)). Response to therapy was confirmed by pathologic response in MIBC pts and radiologic response in mBC pts. Chemotherapies evaluated included: methotrexate/vinblastine/doxorubicin/cisplatin; gemcitabine/cisplatin; gemcitabine/carboplatin; and cisplatin/etoposide. COXEN accurately predicted antineoplastic drug sensitivity in 11/15 (73%) pts (75% MIBC and 67% mBC), of which 7/11 pts had 2 biological samples. However, only 3/7 (43%) biological replicas confirmed COXEN prediction. COXEN accurately predicted drug sensitivity in 9/10 (90%) pts with response and 2/5 (40%) pts with resistance to therapy. Conclusions: COXEN did well in predicting antineoplastic drug response for the majority of bladder cancer pts in this cohort. However, predictions from 2 samples within the same tumor were not always consistent, likely due to the expected tumor heterogeneity found in bladder cancer tumors. A prospective clinical trial in patients with mBC using COXEN to select next best therapy is in development.

scholarly journals Assessing Chemical-Induced Liver Injury In Vivo From In Vitro Gene Expression Data in the Rat: The Case of Thioacetamide Toxicity

2019 ◽  
Vol 10 ◽  
Author(s):  
Patric Schyman ◽  
Richard L. Printz ◽  
Shanea K. Estes ◽  
Tracy P. O’Brien ◽  
Masakazu Shiota ◽  
...  
Keyword(s):  
Gene Expression ◽  
Liver Injury ◽  
Gene Expression Data ◽  
Expression Data

scholarly journals Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method

2018 ◽  
Vol 51 (5) ◽  
pp. 2073-2084 ◽  
Author(s):  
Hai-Hui Huang ◽  
Jing-Guo Dai ◽  
Yong Liang
Keyword(s):  
Gene Expression ◽  
Logistic Regression ◽  
Personalized Medicine ◽  
Gene Expression Data ◽  
Drug Response ◽  
Prediction Models ◽  
Response Prediction ◽  
Expression Data

Background/Aims: One of the most important impacts of personalized medicine is the connection between patients’ genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. Methods: Here we present the Lq penalized network-constrained logistic regression (Lq-NLR) method to meet this need, in which the predictors are integrated into the gene expression data and biological network knowledge and are combined with a more aggressive penalty function. Response prediction models for two cancer targeting drugs (erlotinib and sorafenib) were developed from gene expression data and IC50 values from a large panel of cancer cell lines by utilizing the proposed approach. Then the drug responders were tested with the baseline tumor gene expression data, yielding an in vivo drug sensitivity prediction. Results: These results demonstrated the high effectiveness of this approach. One of the best results achieved by our method was a correlation of 0.841 between the cell line in vitro drug response and patient’s in vivo drug response. We then applied these two drug prediction models to develop a personalized medicine approach in which the subsequent treatment depends on each patient’s gene-expression profile. Conclusion: The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.

Development of a Robust and User-Friendly Method for Leukemia Classification Using Microarrays.

Blood ◽  
2006 ◽  
Vol 108 (11) ◽  
pp. 4304-4304
Author(s):  
Wen Wei ◽  
Julie Tsai ◽  
Barbara Brady ◽  
Wei-min Liu ◽  
Xiaoying Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sample Preparation ◽  
Gene Expression Data ◽  
Development Program ◽  
Normal Karyotype ◽  
In Vitro Transcription ◽  
Ease Of Use ◽  
Expression Data ◽  
Total Rna

Abstract Gene expression profiling (GEP) is a powerful technology for the molecular analysis of leukemia and it groups biologically defined disease entities into distinct sub-classes that can provide diagnosis, guide therapy, and even correlate with disease prognosis. The experimental procedures of micorarray analysis are often cumbersome and provide ample opportunity for variability in gene expression data. We previously reported on our efforts to standardize micorarray analysis across 11 participating laboratories within the international MILE study (Microarray Innovations in LEukemia) where a large dataset of over 4,000 leukemia patient samples is being generated using both Affymetrix HG-U133 Plus 2.0 and custom format microarrays. For a better applicability in a routine laboratory workflow and in order to improve the robustness of the micorarray analysis we now have modified the original micorarray sample preparation protocols as originally published by the manufacturer. Here we report the final results of this effort to minimize the complexity of the sample preparation protocol and to reduce the time that is necessary to run the assay. We designed pre-assembled kits for total RNA preparation, nucleic acid cleanup, cDNA synthesis, in vitro transcription, hybridization and staining, and wash buffers guiding the operator through the whole process of sample preparation to microarray result generation. To further improve the ease of use of this assay we minimized to a large extent the overall complexity of sample amplification and labeling, as well as target hybridization and detection procedures. For example, for the RNA amplification, cRNA labeling, and signal detection process, the number of individual reagent vials was reduced from 32 to 13 vials. This was achieved by combining individual components to ready-to-use master mixes. Furthermore, starting from total RNA, the time required for generation of labeled and fragmented cRNA has been reduced to a convenient eight hour work-shift. Overall, compared to the original 48 hour protocol as recommended by the manufacturer the new workflow generates microarray data in 26 hours. In total, this development program included n=900 whole genome microarray tests. By comparison testing of the original and the final modified protocols on we further can demonstrate by squared correlation coefficients both high inter-assay (R2 > 0.9) and intra-assay (R2 > 0.9) reproducibility and precision of gene expression data of this new sample preparation method. Data from cell lines, normal bone marrow, as well as leukemia samples representing the subclasses AML with normal karyotype or other abnormalities, AML with complex aberrant karyotype, CML, and CLL indicate that reproducible subclassification of leukemias is feasible as all samples were predicted by a classification algorithm as the same class as when the samples were prepared according to the the original method. In conclusion, we developed a robust sample processing methodology for microarray analysis of leukemia samples that allows to generate standardized and reproducible microarray results in multiple laboratories.

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.

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