Statistical Analyses of High Dimensional MicroRNA Data in Relation to Incidence and Survival After Cancer |
| | Abstract | Pancreatic cancer is globally the 4th most common cause of cancer death and the overall 5-year survival rate among patients is less than 5%. Often the pancreatic cancer is already at advance stages when discovered, so the difficulties of an early diagnosis makes the life prognosis for these patients very dismal. Part of the problem with detecting this type of cancer in time, is that there are no typical symptoms. Incidence and prognosis prediction from high dimensional gene expression data have been subject to much research during recent years. This thesis examines the relationship between microRNA expression pro files and their ability to predict correct diagnostics and expected survival from time of operation. This research area can hopefully reform future courses of treatment by providing patients with pancreatic cancer earlier diagnosis, and thus improve their prognosis. This thesis deals with the statistical modelling of microRNA measurements from serum samples of both pancreatic patients and healthy controls. The analyses are divided into two parts. The incidence part focuses on the logistic model for predicting a binary outcome and the prognostic part considers Cox's proportional hazards model in order to handle censored survival times. However since parsimonious models are of clinical relevance, these models are used in combination with coefficient shrinkage techniques, where the shrinkage methods used here are univariate selection, backwards stepwise selection, Ridge regression, Lasso regression and naive elastic net regression. These shrinkage methods require estimation of penalty parameters for which cross-validation have served as an excellent tool. Results based on five diff erent normalization methods indicate that models with only a few microRNAs are good predictors of cancer. The comparative study of the incidence analyses show no signi ficant diff erence in prediction ability between the various shrinkage methods considered. The analyses of prognosis reveal no clear signal in the microRNAs in terms of predicting survival, which could be a result of scarce data. All in all, microRNA expression pro files are promising candidate biomarkers of pancreas cancer. | | Type | Master's thesis [Academic thesis] | | Year | 2012 | | Publisher | Technical University of Denmark, DTU Informatics, E-mail: reception@imm.dtu.dk | | Address | Asmussens Alle, Building 305, DK-2800 Kgs. Lyngby, Denmark | | Series | IMM-M.Sc.-2012-25 | | Note | Supervised by Professor Per Brockho ff, pbb@imm.dtu.dk, DTU Informatics, and supervisors Klaus K. Andersen and Christian Dehlendor from Danish Cancer Society | | Electronic version(s) | [pdf] | | Publication link | http://www.imm.dtu.dk/English.aspx | | BibTeX data | [bibtex] | | IMM Group(s) | Mathematical Statistics |
|