Preprocess and data analysis techniques for affymetrix DNA microarrays using bioconductor: a case study in Alzheimer disease

DNA microarray, or DNA chip, is a technology that allows us to obtain the expression level of many genes in a single experiment. The fact that numerical expression values can be easily obtained gives us the possibility to use multiple statistical techniques of data analysis. In this project microarray data is obtained from Gene Expression Omnibus, the repository of National Center for Biotechnology Information (NCBI). Then, the noise is removed and data is normalized, also we use hypothesis tests to find the most relevant genes that may be involved in a disease and use machine learning methods like KNN, Random Forest or Kmeans. For performing the analysis we use Bioconductor, packages in R for the analysis of biological data, and we conduct a case study in Alzheimer disease. The complete code can be found in https://github.com/alberto-poncelas/ bioc-alzheimer

Design,optimization and in vivo evaluation of non-viral vectors for gene therapy. Applications in ocular disorders

227 págs.

Ingurumen epigenetika : ingurumen faktoreek gure epigenoman eta gure ondorengoenetan duten eragina

Epigenetika DNA sekuentzian aldaketarik gertatu gabe, geneen adierazpenean sortzen diren aldaketa heredagarrien multzoa da. Lan honetan ingurune faktore ezberdinak aztertzen dira, gugan duten eragin epigenetikoak ezagutzeko helburuarekin. Nahiz eta guztiz ulertzen ez diren, hiru mekanismo nagusi bereizten dira; DNA metilazioa, histonen eraldaketa eta RNA ez-kodetzailea. Inguruneak gure DNA eta kromosomen egoera baldintzatzen du eta, gainera, ondorengo belaunaldietan hereda daitezken sortutako aldaketak, gametogenesian epigenomaren berrantolaketa globala gertatu arren. Lan hau burutzeko “National Center for Biotechnology Information” web-guneko PudMed datu-basetik hainbat artikulu jaso ditut. Ondoren, artikuluetan oinarrituta lau multzotan sailkatutako eragileek (dieta, eguneko aktibitatea, tabako eta alkohola, metalak eta beste konposatu kimikoak) gene eta sekuentzia errepikakor desberdinetan duten eragina aztertu dut. Bildutako informazioa, hiru tauletan jaso ditut. Ikerlari desberdinek gene eta sekuentzia errepikakorren metilazio-maila neurtzeko erabiltzen dituzten metodo desberdinak ere bildu ditut. Amaitzeko, gene eta sekuentzia errepikakorrak erkatzen dira, eta etorkizunean, ikerketetan biomarkatzeile modura erabilita eskaini ditzeketen zenbait aukera aipatzen dira. Horrela, ingurune faktoreek sortutako gaixotasun eta ondorioak hobeto kontrolatu ahal izango dira.

Is there a subgroup of long-term evolution among patients with advanced lung cancer?: Hints from the analysis of survival curves from cancer registry data

Background: Recently, with the access of low toxicity biological and targeted therapies, evidence of the existence of a long-term survival subpopulation of cancer patients is appearing. We have studied an unselected population with advanced lung cancer to look for evidence of multimodality in survival distribution, and estimate the proportion of long-term survivors. Methods: We used survival data of 4944 patients with non-small-cell lung cancer (NSCLC) stages IIIb-IV at diagnostic, registered in the National Cancer Registry of Cuba (NCRC) between January 1998 and December 2006. We fitted one-component survival model and two-component mixture models to identify short-and long-term survivors. Bayesian information criterion was used for model selection. Results: For all of the selected parametric distributions the two components model presented the best fit. The population with short-term survival (almost 4 months median survival) represented 64% of patients. The population of long-term survival included 35% of patients, and showed a median survival around 12 months. None of the patients of short-term survival was still alive at month 24, while 10% of the patients of long-term survival died afterwards. Conclusions: There is a subgroup showing long-term evolution among patients with advanced lung cancer. As survival rates continue to improve with the new generation of therapies, prognostic models considering short-and long-term survival subpopulations should be considered in clinical research.