Adv Exp Med Biol
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Lung cancer is the leading cause of cancer-related deaths worldwide with a 5-year overall survival rate of less than 20 %. Considering the treatments currently available, this statistics is shocking. A possible explanation for the disconnect between sophisticated treatments and the survival rate can be related to the post-treatment enrichment of Cancer Stem Cells (CSCs), which is one of a sub-set of drug resistant tumor cells with abilities of self-renewal, cancer initiation, and further maintenance of tumors. ⋯ Through the processes of EMT, epithelial cells lose their epithelial phenotype and gain mesenchymal properties, rendering EMT phenotypic cells acquire drug-resistance. In this chapter, we will further discuss the role of microRNAs (miRNAs) especially because miRNA-based therapies are becoming attractive target with respect to therapeutic resistance and CSCs. Finally, the potential role of the natural agents and synthetic derivatives of natural compounds with anti-cancer activity, e.g. curcumin, CDF, and BR-DIM is highlighted in overcoming therapeutic resistance, suggesting that the above mentioned agents could be important for better treatment of lung cancer in combination therapy.
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Shotgun proteomics is a high throughput technique for protein identification able to identify up to several thousand proteins from a single sample. In order to make sense of this large amount of data, proteomics analysis software is needed, aimed at making the data intuitively accessible to beginners as well as experienced scientists. This chapter provides insight on where to start when analyzing shotgun proteomics data, with a focus on explaining the most common pitfalls in protein identification analysis and how to avoid them. Finally, the move to seeing beyond the list of identified proteins and to putting the results into a bigger biological context is discussed.
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Quantification of individual proteins and even entire proteomes is an important theme in proteomics research. Quantitative proteomics is an approach to obtain quantitative information about proteins in a sample. Compared to qualitative or semi-quantitative proteomics, this approach can provide more insight into the effects of a specific stimulus, such as a change in the expression level of a protein and its posttranslational modifications, or to a panel of proposed biomarkers in a given disease state. ⋯ As the theory and technological aspects underlying the proteomics methodologies will be extensively described in Chap. 20 , and protein identification as a prerequisite of quantification has been discussed in Chap. 17 , we will focus on the quantitative proteomics bioinformatics algorithms and software tools in this chapter. Our goal is to provide researchers and newcomers a rational framework to select suitable bioinformatics tools for data analysis, interpretation, and integration in protein quantification. Before doing so, a brief overview of quantitative proteomics is provided.
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The first step in identifying proteins from mass spectrometry based shotgun proteomics data is to infer peptides from tandem mass spectra, a task generally achieved using database search engines. In this chapter, the basic principles of database search engines are introduced with a focus on open source software, and the use of database search engines is demonstrated using the freely available SearchGUI interface. This chapter also discusses how to tackle general issues related to sequence database searching and shows how to minimize their impact.
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Several cells are endowed in the interstitial space of the connective tissue; among them, a peculiar type has been recently described and named telocyte (TC). The increasing interest on this cell type has allowed identifying it in almost all the organs. All TCs have a proper ultrastructural feature that makes them undoubtedly recognizable under the transmission electron microscope (TEM). ⋯ On the basis of their ubiquitous distribution, TCs are unanimously considered organizers of the connective tissue because of their ability to form 3-D networks. Close to this common role, numerous other roles have been attributed to the TC. Indeed, each of the TC subtype likely plays an own organ-/tissue-specific role contributing to different aspects of physiological regulation in the various anatomical niches they occupy.