scholarly journals BRB-ArrayTools Data Archive for Human Cancer Gene Expression: A Unique and Efficient Data Sharing Resource

2008 ◽  
Vol 6 ◽  
pp. CIN.S448 ◽  
Author(s):  
Yingdong Zhao ◽  
Richard Simon
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Human Cancer ◽  
Data Archive ◽  
Cancer Gene ◽  
Expression Array ◽  
Smooth Interface ◽  
Efficient Data ◽  
Potential Benefits ◽  
Cancer Types

The explosion of available microarray data on human cancer increases the urgency for developing methods for effectively sharing this data among clinical cancer investigators. Lack of a smooth interface between the databases and statistical analysis tools limits the potential benefits of sharing the publicly available microarray data. To facilitate the efficient sharing and use of publicly available microarray data among cancer investigators, we have built a BRB-ArrayTools Data Archive including over one hundred human cancer microarray projects for 28 cancer types. Expression array data and clinical descriptors have been imported into BRB-ArrayTools and are stored as BRB-ArrayTools project folders on the archive. The data archive can be accessed from: http://www.linus.nci.nih.gov/~brb/DataArchive.html Our BRB-ArrayTools data archive and GEO importer represent ongoing efforts to provide effective tools for efficiently sharing and utilizing human cancer microarray data.

SPECTRAL CLUSTERING ON GENE EXPRESSION PROFILE TO IDENTIFY CANCER TYPES OR SUBTYPES

2015 ◽  
Vol 76 (1) ◽  
Author(s):  
Ang Jun Chin ◽  
Andri Mirzal ◽  
Habibollah Haron
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Microarray Data ◽  
Spectral Clustering ◽  
Data Sets ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Cancer Types ◽  
Microarray Gene

Gene expression profile is eminent for its broad applications and achievements in disease discovery and analysis, especially in cancer research. Spectral clustering is robust to irrelevant features which are appropriated for gene expression analysis. However, previous works show that performance comparison with other clustering methods is limited and only a few microarray data sets were analyzed in each study. In this study, we demonstrate the use of spectral clustering in identifying cancer types or subtypes from microarray gene expression profiling. Spectral clustering was applied to eleven microarray data sets and its clustering performances were compared with the results in the literature. Based on the result, overall the spectral clustering slightly outperformed the corresponding results in the literature. The spectral clustering can also offer more stable clustering performances as it has smaller standard deviation value. Moreover, out of eleven data sets the spectral clustering outperformed the corresponding methods in the literature for six data sets. So, it can be stated that the spectral clustering is a promising method in identifying the cancer types or subtypes for microarray gene expression data sets.

scholarly journals The use of logic relationships to model colon cancer gene expression networks with mRNA microarray data

2008 ◽  
Vol 41 (4) ◽  
pp. 530-543 ◽  
Author(s):  
Xiaogang Ruan ◽  
Jinlian Wang ◽  
Hui Li ◽  
Rhoda E. Perozzi ◽  
Edmund F. Perozzi
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Microarray Data ◽  
Cancer Gene ◽  
Mrna Microarray

scholarly journals Prognostic significance of FAM83D gene expression across human cancer types

Oncotarget ◽  
2015 ◽  
Vol 7 (3) ◽  
pp. 3332-3340 ◽  
Author(s):  
Peter J. Walian ◽  
Bo Hang ◽  
Jian-Hua Mao
Keyword(s):  
Gene Expression ◽  
Human Cancer ◽  
Prognostic Significance ◽  
Cancer Types

Association of microRNA-21 with efficacy of cetuximab in RAS wild-type patients in the FIRE-3 study (AIO KRK-0306) and microRNA-21’s influence on gene expression in the EGFR signaling pathway.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 3593-3593
Author(s):  
Lisa Miller-Phillips ◽  
Volker Heinemann ◽  
Arndt Stahler ◽  
Ludwig Fischer von Weikersthal ◽  
Florian Kaiser ◽  
...  
Keyword(s):  
Gene Expression ◽  
Microarray Data ◽  
Multiple Testing ◽  
Predictive Biomarker ◽  
Cancer Gene ◽  
Egfr Signaling ◽  
Wild Type ◽  
Egfr Signaling Pathway ◽  
Gene Sets ◽  
Mrna Microarray

3593 Background: FIRE-3 compared first-line therapy with FOLFIRI plus cetuximab (cet) or bevacizumab (bev) in KRAS exon 2 wild-type (wt) patients with metastatic colorectal cancer. Recent analyses showed mircoRNA-21 (miR-21) expression level may be a predictive biomarker for anti-EGFR-therapy raising the question whether miR-21 influences gene expression in the EGFR signaling pathway. Methods: Reverse-transcription quantitative polymerase chain reaction assay identified quantitative miR-21 expression. Median expression was defined as a threshold value to discriminate FIRE-3 population into miR-21 low and high groups. Differential gene expression based on additional mRNA microarray data (Almac Inc, Xcel Array) was calculated by linear models adjusted for multiple testing followed by single sample gene set enrichment analysis (ssGSEA) to compare differentially enriched hallmarks of cancer gene sets. Overall response rate (ORR) was compared using Fisher´s exact test. Median progression-free (PFS) and overall survival (OS) were analyzed using Kaplan-Meier estimation and log-rank test. Results: 333 RAS wt patients provided material for miR-21 expression analysis. In these patients, low miR-21 expression was associated with higher ORR (80.0% vs. 57.9%; p = 0.005) and longer OS (35.8 months (mo) vs. 25.9 mo; p = 0.005) when cet vs bev was added to FOLFIRI. High miR-21 expression was associated with comparable ORR (74.6% vs. 64.0%; p = 0.21) and OS (24.5 mo vs. 23.8 mo; p = 0.4). There was no significant difference in PFS in either group. By comparing miR-21 low and high groups using normalized mRNA microarray data, 538 genes were found to be significantly differentially expressed in RAS wt patients after adjustment for multiple testing. Including data from the two groups into ssGSEA yielded 23 hallmark of cancer gene sets that were significantly differentially enriched; among them, KRAS-signaling showed higher enrichment in the miR-21 high group (adjusted p = 2.09 E-13). Conclusions: MiR-21 expression level might be a predictive biomarker for anti-EGFR-therapy by modulating KRAS signaling in FIRE-3 patients.

scholarly journals Effects of TP53 mutational status on gene expression patterns across 10 human cancer types

2014 ◽  
Vol 232 (5) ◽  
pp. 522-533 ◽  
Author(s):  
Neha Parikh ◽  
Susan Hilsenbeck ◽  
Chad J Creighton ◽  
Tajhal Dayaram ◽  
Ryan Shuck ◽  
...  
Keyword(s):  
Gene Expression ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Mutational Status ◽  
Cancer Types ◽  
Tp53 Mutational Status

scholarly journals Discretization of Gene Expression Data Unmasks Molecular Subgroups Recurring in Different Human Cancer Types

PLoS ONE ◽  
2016 ◽  
Vol 11 (8) ◽  
pp. e0161514 ◽  
Author(s):  
Manfred Beleut ◽  
Robert Soeldner ◽  
Mark Egorov ◽  
Rolf Guenther ◽  
Silvia Dehler ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Human Cancer ◽  
Expression Data ◽  
Molecular Subgroups ◽  
Cancer Types

scholarly journals Phosphodiesterase 4D Depletion/Inhibition Exerts Anti-Oncogenic Properties in Hepatocellular Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2182
Author(s):  
Federica Ragusa ◽  
Nadia Panera ◽  
Silvia Cardarelli ◽  
Marco Scarsella ◽  
Marzia Bianchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Human Cancer ◽  
Specific Inhibitor ◽  
Protein Activity ◽  
Over Expression ◽  
Insulin Growth Factor ◽  
Cancer Types ◽  
Phosphodiesterase 4D ◽  
Hcc Cells

Isoform D of type 4 phosphodiesterase (PDE4D) has recently been associated with several human cancer types with the exception of human hepatocellular carcinoma (HCC). Here we explored the role of PDE4D in HCC. We found that PDE4D gene/protein were over-expressed in different samples of human HCCs compared to normal livers. Accordingly, HCC cells showed higher PDE4D activity than non-tumorigenic cells, accompanied by over-expression of the PDE4D isoform. Silencing of PDE4D gene and pharmacological inhibition of protein activity by the specific inhibitor Gebr-7b reduced cell proliferation and increased apoptosis in HCC cells, with a decreased fraction of cells in S phase and a differential modulation of key regulators of cell cycle and apoptosis. PDE4D silencing/inhibition also affected the gene expression of several cancer-related genes, such as the pro-oncogenic insulin growth factor (IGF2), which is down-regulated. Finally, gene expression data, available in the CancerLivER data base, confirm that PDE4D over-expression in human HCCs correlated with an increased expression of IGF2, suggesting a new possible molecular network that requires further investigations. In conclusion, intracellular depletion/inhibition of PDE4D prevents the growth of HCC cells, displaying anti-oncogenic effects. PDE4D may thus represent a new biomarker for diagnosis and a potential adjuvant target for HCC therapy.

scholarly journals Molecular correlates and therapeutic targets in T cell-inflamed versus non-T cell-inflamed tumors across cancer types

2019 ◽  
Author(s):  
Riyue Bao ◽  
Jason J. Luke
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Hedgehog Signaling ◽  
Human Cancer ◽  
Expression Patterns ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Type I ◽  
Tumor Type ◽  
Cancer Types

AbstractThe T cell-inflamed tumor microenvironment, characterized by CD8 T cells and type I/II interferon transcripts, is an important cancer immunotherapy biomarker. Tumor mutational profile may also dictate response with some oncogenes (i.e. WNT/β-catenin) known to mediate immuno-suppression. Building on these observations we performed a multi-omic analysis of human cancer correlating the T cell-inflamed gene expression signature with the somatic mutanome and transcriptome for different immune phenotypes, by tumor type and across cancers. Strong correlations were noted between mutations in oncogenes and non-T cell-inflamed tumors with examples including IDH1 and GNAQ as well as less well-known genes including KDM6A, CD11c and genes with unknown functions. Conversely, we observe many genes associating with the T cell-inflamed phenotype including VHL and PBRM1, among others. Analyzing gene expression patterns, we identify oncogenic mediators of immune exclusion broadly active across cancer types including HIF1A and MYC. Novel examples from specific tumors include sonic hedgehog signaling in ovarian cancer or hormone signaling and novel transcription factors across multiple tumors. Using network analysis, somatic and transcriptomic events were integrated, demonstrating that most non-T cell-inflamed tumors are influenced by multiple pathways. Validating these analyses, we observe significant inverse relationships between protein levels and the T cell-inflamed gene signature with examples including NRF2 in lung, ERBB2 in urothelial and choriogonadotropin in cervical cancer. Finally, we integrate available databases for drugs that might overcome or augment the identified mechanisms. These results nominate molecular targets and drugs potentially available for immediate translation into clinical trials for patients with cancer.

DNA Microarrays in Clinical Oncology

2002 ◽  
Vol 20 (7) ◽  
pp. 1932-1941 ◽  
Author(s):  
Sridhar Ramaswamy ◽  
Todd R. Golub
Keyword(s):  
Gene Expression ◽  
Clinical Oncology ◽  
Expression Profiling ◽  
Dna Microarrays ◽  
Human Cancer ◽  
Clinical Applications ◽  
Cancer Gene ◽  
Comprehensive Understanding ◽  
Aberrant Gene Expression ◽  
Aberrant Gene

ABSTRACT: Aberrant gene expression is critical for tumor initiation and progression. However, we lack a comprehensive understanding of all genes that are aberrantly expressed in human cancer. Recently, DNA microarrays have been used to obtain global views of human cancer gene expression and to identify genetic markers that might be important for diagnosis and therapy. We review clinical applications of these novel tools, discuss some important recent studies, identify promising avenues of research in this emerging field of study, and discuss the likely impact that expression profiling will have on clinical oncology.

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