scholarly journals Analysis by cDNA microarrays of gene expression patterns of human adrenocortical tumors

2006 ◽  
Vol 154 (4) ◽  
pp. 587-598 ◽  
Author(s):  
E P Slater ◽  
S M Diehl ◽  
P Langer ◽  
B Samans ◽  
A Ramaswamy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Normal Tissue ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Malignant Neoplasm ◽  
Cdna Microarrays ◽  
Gene Expression Patterns ◽  
Chromogranin B ◽  
Adrenocortical Tumors

Objectives: Adrenocortical carcinoma (ACC) is a rare malignant neoplasm with extremely poor prognosis. The molecular mechanisms of adrenocortical tumorigenesis are still not well understood. The comparative analysis by cDNA microarrays of gene-expression patterns of benign and malignant adrenocortical tumors allows us to identify new tumor-suppressor genes and proto-oncogenes underlying adrenocortical tumorigenesis. Design and methods: Total RNA from fresh-frozen tissue of 10 ACC and 10 benign adrenocortical adenomas was isolated after histologic confirmation of neoplastic cellularity of at least 85%. The reference consisted of pooled RNA of 10 normal adrenal cortex samples. Amplified RNA of tumor and reference was used to synthesize Cy3- and Cy5-fluorescently labeled cDNA in a flip-color technique. D-chips containing 11 540 DNA spots were hybridized and scanned and the images were analyzed by ImaGene 3.0 software. Results: The comparative analysis of gene expression revealed many genes with more than fourfold expression difference between ACC and normal tissue (42 genes), cortical adenoma and normal tissue (11 genes), and ACC and cortical adenoma (21 genes) respectively. As confirmed by real-time PCR, the IGF2 gene was significantly upregulated in ACCs versus cortical adenomas and normal cortical tissue. Genes that were downregulated in adrenocortical tumors included chromogranin B and early growth response factor 1. Conclusions: Comprehensive expression profiling of adrenocortical tumors by the cDNA microarray technique is a very powerful tool to elucidate the molecular steps associated with the tumorigenesis of these ill-defined neoplasms. To evaluate the role of identified genes, further detailed analyses, including correlation with clinical data, are required.

Use of cDNA Microarrays to Assess DNA Gene Expression Patterns in Cancer

1998 ◽  
pp. 109-116
Author(s):  
Paul S. Meltzer ◽  
Michael Bittner ◽  
Mervi Heiskanen ◽  
Tiffany Hoffman ◽  
Yidong Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Cdna Microarrays ◽  
Gene Expression Patterns

scholarly journals Gene Expression Patterns Underlying the Reinstatement of Plasticity in the Adult Visual System

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ettore Tiraboschi ◽  
Ramon Guirado ◽  
Dario Greco ◽  
Petri Auvinen ◽  
Jose Fernando Maya-Vetencourt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Visual Cortex ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Monocular Deprivation ◽  
Adult Brain ◽  
Gene Expression Patterns ◽  
Postnatal Life ◽  
Critical Periods

The nervous system is highly sensitive to experience during early postnatal life, but this phase of heightened plasticity decreases with age. Recent studies have demonstrated that developmental-like plasticity can be reactivated in the visual cortex of adult animals through environmental or pharmacological manipulations. These findings provide a unique opportunity to study the cellular and molecular mechanisms of adult plasticity. Here we used the monocular deprivation paradigm to investigate large-scale gene expression patterns underlying the reinstatement of plasticity produced by fluoxetine in the adult rat visual cortex. We found changes, confirmed with RT-PCRs, in gene expression in different biological themes, such as chromatin structure remodelling, transcription factors, molecules involved in synaptic plasticity, extracellular matrix, and excitatory and inhibitory neurotransmission. Our findings reveal a key role for several molecules such as the metalloproteases Mmp2 and Mmp9 or the glycoprotein Reelin and open up new insights into the mechanisms underlying the reopening of the critical periods in the adult brain.

scholarly journals Microfluidic single-cell real-time PCR for comparative analysis of gene expression patterns

2012 ◽  
Vol 7 (5) ◽  
pp. 829-838 ◽  
Author(s):  
Veronica Sanchez-Freire ◽  
Antje D Ebert ◽  
Tomer Kalisky ◽  
Stephen R Quake ◽  
Joseph C Wu
Keyword(s):  
Gene Expression ◽  
Comparative Analysis ◽  
Single Cell ◽  
Real Time ◽  
Real Time Pcr ◽  
Expression Patterns ◽  
Gene Expression Patterns

scholarly journals Unraveling the molecular heterogeneity in type 2 diabetes: A potential subtype discovery study followed by metabolic modeling

2020 ◽  
Author(s):  
Maryam Khoshnejat ◽  
Kaveh Kavousi ◽  
Ali Mohammad Banaei-Moghaddam ◽  
Ali Akbar Moosavi-Movahedi
Keyword(s):  
Gene Expression ◽  
Type 2 Diabetes ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Critical Role ◽  
Gene Expression Pattern ◽  
Gene Expression Patterns ◽  
Diabetic Patients ◽  
Molecular Heterogeneity

Abstract BackgroundType 2 diabetes mellitus (T2DM) is a complex multifactorial disease with a high prevalence in the world. Insulin resistance and impaired insulin secretion are the two major abnormalities in the pathogenesis of T2DM. Skeletal muscle is responsible for over 75% of the glucose uptake, thus plays a critical role in T2DM. Here, we attempted to provide a better understanding of abnormalities in this tissue. MethodsThe muscle gene expression patterns in healthy and newly diagnosed T2DM individuals were explored using supervised and unsupervised classification approach. Moreover, the potential of sub-typing T2DM patients based on the gene expression patterns was evaluated.ResultsA machine-learning technique was applied to identify a gene expression pattern that could discriminate between normoglycemic and diabetic groups. A gene set comprises of 26 genes was found that was able to discriminate healthy from diabetic individuals with 94% accuracy. In addition, three distinct clusters of diabetic patients with different dysregulated genes and metabolic pathways were identified. Conclusions This study implies that it seems the disease has triggered through different cellular/molecular mechanisms, and it has the potential to be categorized in different sub-types. Possibly, subtyping of T2DM patients in combination with their real clinical profiles will provide a better understanding of abnormalities in each group. Thus, this approach will help to recommend the appropriate treatment for each subtype in the future.

scholarly journals Unraveling the molecular heterogeneity in type 2 diabetes: A potential subtype discovery followed by metabolic modeling

2020 ◽  
Author(s):  
Maryam Khoshnejat ◽  
Kaveh Kavousi ◽  
Ali Mohammad Banaei-Moghaddam ◽  
Ali Akbar Moosavi-Movahedi
Keyword(s):  
Gene Expression ◽  
Type 2 Diabetes ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Critical Role ◽  
Unsupervised Classification ◽  
Gene Expression Patterns ◽  
Diabetic Patients ◽  
Molecular Heterogeneity

Abstract BackgroundType 2 diabetes mellitus (T2DM) is a complex multifactorial disease with a high prevalence worldwide. Insulin resistance and impaired insulin secretion are the two major abnormalities in the pathogenesis of T2DM. Skeletal muscle is responsible for over 75% of the glucose uptake and plays a critical role in T2DM. Here, we sought to provide a better understanding of the abnormalities in this tissue. MethodsThe muscle gene expression patterns were explored in healthy and newly diagnosed T2DM individuals using supervised and unsupervised classification approaches. Moreover, the potential of subtyping T2DM patients was evaluated based on the gene expression patterns.ResultsA machine-learning technique was applied to identify a set of genes whose expression patterns could discriminate diabetic subjects from healthy ones. A gene set comprising of 26 genes was found that was able to distinguish healthy from diabetic individuals with 94% accuracy. In addition, three distinct clusters of diabetic patients with different dysregulated genes and metabolic pathways were identified. Conclusions This study indicates that T2DM is triggered by different cellular/molecular mechanisms, and it can be categorized into different subtypes. Subtyping of T2DM patients in combination with their real clinical profiles will provide a better understanding of the abnormalities in each group and more effective therapeutic approaches in the future.

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