Breast cancer derived extracellular vesicles transcriptomic and proteomic analysis and bioinformatics
Role of extracellular vesicles (EVs) in cancer development is a much more complex and rapidly evolving field. EVs include exosomes and microvesicles secreted from many cell types, but are released in higher concentration from cancer cells, into the intercellular microenvironment. Studies have shown that EVs derived from breast cancer cells are key players in escape of tumor cells to immune system, differentiation of cancer associated fibroblasts and transition of epithelial to mesenchymal cells. Induction of phenotypic changes in recipient cells is because of DNA, RNA, proteins and lipids present in EVs. Therefore, EVs content can play a role in understanding of intercellular communication in breast cancer progression. Comparative studies of extracellular vesicles secreted in normal cells and during breast cancer development and metastasis will help in understanding of their role in tumorigenesis. This can be achieved by total RNA sequencing present in EVs, mass spectrometry for the analysis of EV’s protein content and bioinformatics.
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Integrated analysis of differentially expressed gene patterns in Alzheimer’s disease
Integrated omic data is a quite recent productive approach in order to identify more reliable biomarkers and/or molecules for complex neurodegenerative diseases such as Alzheimer’s disease (AD). Transcriptome analysis for elucidating differentially expressed genes (DEGs) in AD and normal samples, in silico analysis of DEGs, microRNA omics (miRNomics) and methylation status are important aspects in diseases development and progression. Computational analysis of integrated data will be validated using in vitro model. In the first part of this study, an identified set of DEGs associated with Alzheimer’s disease development will be validated. Based on meta-analysis panels of miRNAs for DEGs expression and methylation patterns in DEGs will be determined. These two independent panels of genes would be helpful to ascertain sequential explanation of non-randomized events of AD. Findings from this study will provide comprehensive understanding of this degenerative neural disease, which despite being studied for years, still has higher prevalence in the USA. Based on these findings potential biomarkers for diagnostic and therapeutic relevance may also be proposed.
