bioRxivBioinformaticsPreprint
Connecting Cancers to Chromosomal Locations Through Copy Number Variations
Abstract
BackgroundIn 1960, scientists Peter Nowell and David Hungerford made a groundbreaking discovery by identifying the first genetic abnormality linked to cancer using cytogenetics. This discovery marked the beginning of research into many other genetic changes associated with cancer. These changes in chromosomes are like fingerprints, revealing how genes go haywire in cancer and destabilize the cells genetic makeup. In this paper, we focus on a clear connection between changes in the number of copies of certain genes at specific spots on chromosomes (called copy number variations or CNVs) and their role in various types of cancer. MethodWe obtained copy number data from various cancers cell lines through the DepMap portal website (https://depmap.org/portal/download/) and conducted diverse statistical analyses. Our approach involved comparing a single cancer cell line (primary disease) against the non-cancer cells. We used t-test to identify the top 100 genes that exhibited significant alterations in their copy number levels, both upregulated and downregulated. To enhance our understanding, we then linked these selected top 100 genes to their respective positions on the chromosomes. ResultsThe top 100 upregulated genes associated with different cancers exhibit distinct chromosomal locations. For example all top 100 upregulated genes in Acute Myeloid Leukemia and Non-Hodgkin Lymphoma are on chromosome 17. Similarly, the top 100 downregulated genes linked to various cancers have their designated chromosomal locations. ConclusionsUtilizing the DepMap copy number data, we have unveiled a distinctive linkage between cancer types and their specific chromosomal locales. The significance of these observed associations, when substantiated by biological evidence, holds the potential to significantly enhance our comprehension of cancers origins. Furthermore, such confirmation could pave the way for the development of more efficacious diagnostic, prognostic, and therapeutic strategies, promising a brighter outlook in the fight against cancer.
- organism: human
- assay: whole-genome sequencing
- research focus: differential expression
- approach: statistical model
- Citations
- 1
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