Array comparative genomic hybridi-zation (array CHG) analysis in di̇fferent cancer cell lines

In recent years, the importance of genomic instability in cancer development has become increasingly well understood, leading to a growing interest in the detailed investigation of structural genetic alterations in tumors. Array Comparative Genomic Hybridization (Array CGH) has emerged as a non-invasive molecular analysis technique that enables the detection of copy number variations (CNVs) across the entire genome with high resolution. The primary aim of this thesis is to explore how genetic variations differ based on the tissue specificity of cancer and to identify critical CNVs involved in cancer biology. In this study, three cancer cell lines of different tissue origins—K562 (chronic myeloid leukemia), MCF7 (breast cancer), and HeLa (cervical cancer)—were comparatively analyzed. Genomic DNA samples were examined using the Agilent SurePrint G3 Human CGH Microarray platform, and the analyses revealed distinct structural genetic alterations in all three cell lines. In K562 cells, significant gains and losses were identified in genomic regions associated with blood cell lineages. In MCF7 cells, CNVs were detected in gene regions specific to breast cancer, which were considered potentially related to molecular subtypes of the disease. In HeLa cells, numerous structural abnormalities were observed; however, these alterations could not be directly associated with cervical cancer. This finding is consistent with the known high degree of aneuploidy and genetic heterogeneity in the HeLa cell line. The results of this thesis demonstrate that copy number variations vary according to the tissue origin of cancer and that certain genetic alterations may serve as potential biomarkers for specific cancer types. For future studies, integrating these findings with data from primary tumor tissues and gene expression profiles will enhance the understanding of the functional and clinical significance of CNVs. Furthermore, validating the identified candidate regions using advanced molecular techniques, such as qPCR or RNA sequencing, will strengthen the biological relevance of the results.