A mixture model approach for the analysis of small exploratory microarray experiments

Classification of Death Rate due to Women's Cancers in Different Countries

BACKGROUND

The two most frequently diagnosed cancers among women worldwide are breast and cervical cancers. The objective of the present study was to classify the different countries based on the death rates from sex specific cancers.

METHODS

In this cross-sectional study, we used dataset regarding death rate from breast, cervical, uterine, and ovarian cancers in 190 countries worldwide reported by World Health Organization. Normal mixture models were fitted with different numbers of components to these data. The model's parameters estimated using the EM algorithm. Then, appropriate number of components was determined and was selected the best-fit model using the BIC criteria. Next, model-based clustering was used to allocate the world countries into different clusters based on the distribution of women's cancers. The MIXMOD program using MATLAB software was used for data analysis.

RESULTS

The best model selected with four components. Then, countries were allocated into four clusters including 43 (23%) in the first cluster, 28 (14%) in the second cluster, 75 (39%) in the third cluster, and 44 (24%) in the fourth cluster. Most countries in South America were to the first cluster. In addition, most countries in Africa, Central, and Southeast Asia were located to the third cluster. Furthermore, the fourth cluster consisted of Pacific continent, North America and European countries.

CONCLUSION

Considering the benefits of clustering based on normal mixture models, it seems that can be applied this method in wide variety of medical and public heath contexts.

Epigenetic analysis: ChIP-chip and ChIP-seq

The access of transcription factors and the replication machinery to DNA is regulated by the epigenetic state of chromatin. In eukaryotes, this complex layer of regulatory processes includes the direct methylation of DNA, as well as covalent modifications to histones. Using next-generation sequencers, it is now possible to obtain profiles of epigenetic modifications across a genome using chromatin immunoprecipitation followed by sequencing (ChIP-seq). This technique permits the detection of the binding of proteins to specific regions of the genome with high resolution. It can be used to determine the target sequences of transcription factors, as well as the positions of histones with specific modification of their N-terminal tails. Antibodies that selectively bind methylated DNA may also be used to determine the position of methylated cytosines. Here, we present a data analysis pipeline for processing ChIP-seq data, and discuss the limitations and idiosyncrasies of these approaches.

Gene Profiling of Aortic Valve Interstitial Cells under Elevated Pressure Conditions: Modulation of Inflammatory Gene Networks

The study aimed to identify mechanosensitive pathways and gene networks that are stimulated by elevated cyclic pressure in aortic valve interstitial cells (VICs) and lead to detrimental tissue remodeling and/or pathogenesis. Porcine aortic valve leaflets were exposed to cyclic pressures of 80 or 120 mmHg, corresponding to diastolic transvalvular pressure in normal and hypertensive conditions, respectively. Linear, two-cycle amplification of total RNA, followed by microarray was performed for transcriptome analysis (with qRT-PCR validation). A combination of systems biology modeling and pathway analysis identified novel genes and molecular mechanisms underlying the biological response of VICs to elevated pressure. 56 gene transcripts related to inflammatory response mechanisms were differentially expressed. TNF-α, IL-1α, and IL-1β were key cytokines identified from the gene network model. Also of interest was the discovery that pentraxin 3 (PTX3) was significantly upregulated under elevated pressure conditions (41-fold change). In conclusion, a gene network model showing differentially expressed inflammatory genes and their interactions in VICs exposed to elevated pressure has been developed. This system overview has detected key molecules that could be targeted for pharmacotherapy of aortic stenosis in hypertensive patients.