Identification and Analysis of Sex-Biased Copy Number Alterations

Chenhao Zhang; Yang Yang; Qinghua Cui; Dongyu Zhao; Chunmei Cui

doi:10.34133/hds.0121

Health Data Science (Jan 2024)

Identification and Analysis of Sex-Biased Copy Number Alterations

Chenhao Zhang,
Yang Yang,
Qinghua Cui,
Dongyu Zhao,
Chunmei Cui

Affiliations

Chenhao Zhang: Department of Biomedical Informatics, Center for Noncoding RNA Medicine, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing 100191, China.
Yang Yang: Department of Biochemistry and Biophysics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China.
Qinghua Cui: Department of Biomedical Informatics, Center for Noncoding RNA Medicine, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing 100191, China.
Dongyu Zhao: Department of Biomedical Informatics, Center for Noncoding RNA Medicine, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing 100191, China.
Chunmei Cui: Department of Biomedical Informatics, Center for Noncoding RNA Medicine, State Key Laboratory of Vascular Homeostasis and Remodeling, School of Basic Medical Sciences, Peking University, 38 Xueyuan Rd, Beijing 100191, China.

DOI: https://doi.org/10.34133/hds.0121
Journal volume & issue: Vol. 3

Abstract

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Background: Sex difference has long been recognized at cancer incidence, outcomes, and responses to therapy. Analyzing the somatic mutation profiles of large-scale cancer samples between the sexes have revealed several potential drivers of cancer with sex difference. However, it is still a demand for in-depth scrutinizing the sex-biased characteristics of genome instability to link the clinical differences for individual cancer type. Methods: Here, we utilized a published framework devised to specifically compare the copy number profiles between 2 groups to identify the sex-biased copy number alterations (CNAs) across 16 cancer types from the The Cancer Genome Atlas Program database, and dissected the impact of those CNAs. Results: Totally, 81 male-biased CNA regions and 23 female-biased CNA regions in 16 cancer types were found. Functional annotation analysis showed that several critical biological functions associated with sex-biased CNAs are shared in multiple cancer types, including immune-related pathways and regulation of cellular signaling. Most sex-biased CNAs have a substantial effect on transcriptional consequence, where the average of over 68% of genes have a linear relationship with CNAs across cancer types, and 14% of those genes are affected by the combination of the sex and copy number. Furthermore, 29 sex-biased CNA regions show latent capacity to be sex-specific prognostic biomarker such as CNA on 11q13.4 for head and neck cancer and lung cancer. Conclusions: This analysis offers new insights into the role of sex in cancer etiology and prognosis through a detailed characterization of sex differences in genome instability of diverse cancers.

Published in Health Data Science

ISSN: 2765-8783 (Online)
Publisher: American Association for the Advancement of Science (AAAS)
Country of publisher: United States
LCC subjects: Medicine: Medicine (General): Computer applications to medicine. Medical informatics
Website: https://spj.sciencemag.org/journals/hds/

About the journal