| 1. |
- Carlsson, Jessica, 1984-
(författare)
-
Identification of miRNA expression profiles for diagnosis and prognosis of prostate cancer
- 2012
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer of the prostate (CaP) is the most common malignancy diagnosed in men in the Western society. During the last years, prostate specific antigen (PSA) has been used as a biomarker for CaP, although a high PSA value is not specific for CaP. Thus, there is an urgent need for new and improved diagnostic markers for CaP.In this thesis, the aim was to find a miRNA signature for diagnosis of CaP and to elucidate if differences in behavior between transition zone and peripheral zone tumors are reflected in miRNA expression. One of the major findings is anexpression signature based on nine miRNAs that with high accuracy (85%) could classify normal and malignant tissues from the transition zone of the prostate. The results furthermore show that the major differences in miRNA expression are found between normal and malignant tissues, rather than between the different zones. In addition, tumors arising in the peripheral zone have fewer changes in miRNA expression compared to tumors in the transition zone, indicating that the peripheral zone is more prone to tumor development compared to the transition zone of the prostate.A crucial step in pre-processing of expression data, in order to differentiate true biological changes, is the normalization step. Therefore, an additional aim of this thesis was to compare different normalization methods for qPCR array data in miRNA expression experiments. The results show that data-driven methods based on quantile normalization performs the best. The results also show that in smaller miRNA expression studies, only investigating a few miRNAs, RNU24 is the most suitable endogenous control gene for normalization.Taken together, the results in this thesis show the importance of miRNAs and the possibility of their future use as biomarkers in the field of prostate cancer.
|
|
| 2. |
|
|
| 3. |
- Falck, Eva, 1975-
(författare)
-
Genomic and genetic alterations in endometrial adenocarcinoma
- 2013
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The most frequently diagnosed cancer of the female genital tract is cancer of the endometrium (endometrial cancer), ranking fourth among the invasive tumors that affect women in Europe and North America. As most other cancer types, endometrial cancer is a complex genetic disease influenced by both genetic and environmental factors.The human population is genetically heterogeneous and studies of complex diseases in human are proven to be difficult. By using a model system such as the BDII rat, some of the obstacles related to the study of complex diseases can be avoided. The BDII rat strain is prone to spontaneously develop endometrial adenocarcinoma (EAC) and more than 90% of the virgin females develop EAC during their lifetime. Development of EAC tumors in BDII rats is comparable in pathogenesis and histopathological properties to that of human.The aims of this thesis were i/ to characterize EAC in the BDII rat experimental model system by analyzing structural and numerical chromosome aberrations, ii/ to evaluate the importance of the genetic set-up in EAC development, and iii/ to determine the impact of genomic and genetic alterations on the functionality of candidate genes in rat EAC and in human endometrial tumors of different FIGO grades.Non-random numerical and structural aberrations that could contribute to tumor formation were identified, and evidence that the genetic background had a significant influence on the genome make-up of tumor cells was provided. Certain genes (Gpx3/GPX3, Met/MET, Phf5a/PHF5A, and Gja1/GJA1) were selected for further analysis and aberrant expression of some of them were found in both rat and human EACs. By separating EAC cell lines according to the genetic cross background, for two of the genes (Phf5 and Met), we showed that the expression pattern differed significantly between different cross backgrounds, which clearly pinpoint the importance of using animal models as a complement to clinical studies in identification of cancer-related genes.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Ulfenborg, Benjamin, 1985-
(författare)
-
Bioinformatics tools for discovery and evaluation of biomarkers : Applications in clinical assessment of cancer
- 2016
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Cancer is a disease characterized by abnormal proliferation of cells in the body and ranks as the second leading cause of death worldwide. In order to improve cancer patient care, a major focus of cancer research is to discover biomarkers. A biomarker is a biological molecule found in tissues or body fluids and can be used to predict or assess disease states. The aim of this thesis is to develop bioinformatics tools for discovery and evaluation of novel biomarkers from high-throughput datasets.MicroRNAs (miRNAs) are short non-coding RNAs that function as negative regulators of gene expression. Dysregulation of miRNAs in cancer is frequently reported, making them interesting as biomarker candidates. GenoScan was developed for genome-wide discovery of miRNA-coding genes, as a first step in the identification of novel mi-RNA biomarkers.High-throughput technologies such as microarrays allow researchers to measure the expression of thousands of genes or miRNAs simultaneously. The Decision Trunk Classifier (DTC) algorithm has been developed to screen datasets from these experiments for biomarker candidates. When applied to a miRNA expression dataset for endometrial cancer (EC) samples vs. controls, a two-marker model with 98 % accuracy was generated. These miRNAs (hsa-miR-183-5p and hsa-miRPlus-C1070) are promising as biomarkers for EC screening.The miREC database was developed to store gene and miRNA data from curated expression profiling studies of EC, as well as gene-miRNA regulatory connections. Using gene-miRNA interaction networks from miREC, the roles of miRNAs in cancer hallmark acquisition can be clarified. To further support exploratory analysis of expression data, DTC was extended with partial least squares regression models. The resulting PLS-DTC algorithm can be used to gain deeper insights into the perturbation of biological processes and pathways.
|
|
