Thesis Abstract

Abstract
This thesis investigates the role of MicroRNAs (miRNAs) in cancer diagnosis and prognosis within the realm of Medical Laboratory Science. MicroRNAs are small non-coding RNA molecules that play a crucial role in gene regulation and have been implicated in various diseases, including cancer. The aim of this study is to explore the potential of miRNAs as biomarkers for cancer diagnosis and prognosis, with a focus on their expression patterns and clinical implications. The research begins with a comprehensive review of the existing literature on miRNAs in cancer, highlighting their involvement in tumorigenesis, metastasis, and drug resistance. The findings from various studies are synthesized to provide a clear understanding of the current landscape of miRNA research in oncology. Methodologically, this study employs a systematic approach to identify and analyze relevant miRNAs associated with different types of cancer. Techniques such as PCR, microarray analysis, and next-generation sequencing are utilized to detect and quantify miRNA expression levels in cancer samples. The research methodology also includes bioinformatics analysis to predict miRNA targets and pathways involved in cancer progression. The results of this study demonstrate the potential of miRNAs as diagnostic and prognostic markers in cancer. Specific miRNAs are identified as having significant associations with cancer subtypes, stages, and patient outcomes. The implications of these findings for personalized medicine and targeted therapy are discussed in detail. In conclusion, this thesis contributes to the growing body of knowledge on the role of miRNAs in cancer diagnosis and prognosis. The findings underscore the importance of miRNA-based biomarkers in improving the accuracy of cancer detection and predicting patient survival. Future research directions are proposed to further explore the clinical utility of miRNAs in oncology. Keywords MicroRNAs, Cancer, Diagnosis, Prognosis, Biomarkers, Medical Laboratory Science.

Thesis Overview