A Cross-Study Comparison of Gene Expression Studies for the Molecular Classification of Lung Cancer

2004 ◽  
Vol 10 (9) ◽  
pp. 2922-2927 ◽  
Author(s):  
Giovanni Parmigiani ◽  
Elizabeth S. Garrett-Mayer ◽  
Ramaswamy Anbazhagan ◽  
Edward Gabrielson
2013 ◽  
Vol 35 ◽  
pp. 11-21 ◽  
Author(s):  
Firoza Mamdani ◽  
Maureen V. Martin ◽  
Todd Lencz ◽  
Brandi Rollins ◽  
Delbert G. Robinson ◽  
...  

Mood disorders and schizophrenia are common and complex disorders with consistent evidence of genetic and environmental influences on predisposition. It is generally believed that the consequences of disease, gene expression, and allelic heterogeneity may be partly the explanation for the variability observed in treatment response. Correspondingly, while effective treatments are available for some patients, approximately half of the patients fail to respond to current neuropsychiatric treatments. A number of peripheral gene expression studies have been conducted to understand these brain-based disorders and mechanisms of treatment response with the aim of identifying suitable biomarkers and perhaps subgroups of patients based upon molecular fingerprint. In this review, we summarize the results from blood-derived gene expression studies implemented with the aim of discovering biomarkers for treatment response and classification of disorders. We include data from a biomarker study conducted in first-episode subjects with schizophrenia, where the results provide insight into possible individual biological differences that predict antipsychotic response. It is concluded that, while peripheral studies of expression are generating valuable results in pathways involving immune regulation and response, larger studies are required which hopefully will lead to robust biomarkers for treatment response and perhaps underlying variations relevant to these complex disorders.


2009 ◽  
Vol 56 (2) ◽  
Author(s):  
Peter Gresner ◽  
Jolanta Gromadzinska ◽  
Wojciech Wasowicz

The aim of this study was to test a panel of 6 reference genes in order to identify and validate the most suitable reference genes for expression studies in paired healthy and non-small cell lung cancer tissues. Quantitative real-time PCR followed by the NormFinder- and geNorm-based analysis was employed. The study involved 21 non-small cell lung cancer patients. The analysis of experimental data revealed HPRT1 as the most stable gene followed by RPLP0 and ESD. In contrast, GAPDH was found to be the least stable gene. HPRT1 together with ESD was revealed as the pair of genes introducing the least systematic error into data normalization. Validation by bootstrap random sampling technique and by normalizing exemplary gene expression data confirmed the results. Although HPRT1 and ESD may by recommended for data normalization in gene expression studies on non-small cell lung cancer, the suitability of selected reference genes must be unconditionally validated prior to each study.


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