Identifying shared transcriptional risk patterns between atherosclerosis and cancer

Richard A Baylis; Hua Gao; Fudi Wang; Caitlin F Bell; Lingfeng Luo; Johan L M Björkegren; Nicholas J Leeper

doi:10.1016/j.isci.2023.107513

Identifying shared transcriptional risk patterns between atherosclerosis and cancer

iScience. 2023 Jul 29;26(9):107513. doi: 10.1016/j.isci.2023.107513. eCollection 2023 Sep 15.

Authors

Richard A Baylis^{1

2

3

4}, Hua Gao^{1

2}, Fudi Wang^{1

2}, Caitlin F Bell^{1

5}, Lingfeng Luo^{1

2}, Johan L M Björkegren^{6

7}, Nicholas J Leeper^{1

2

5}

Affiliations

¹ Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA, USA.
² Stanford Cardiovascular Institute, Stanford, CA, USA.
³ Department of Medicine, Division of Cardiology, University of California, San Francisco, CA, USA.
⁴ Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁵ Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
⁶ Department of Medicine, Karolinska Institute, Huddinge, Sweden.
⁷ Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Abstract

Cancer and cardiovascular disease (CVD) are the leading causes of death worldwide. Numerous overlapping pathophysiologic mechanisms have been hypothesized to drive the development of both diseases. Further investigation of these common pathways could allow for the identification of mutually detrimental processes and therapeutic targeting to derive mutual benefit. In this study, we intersect transcriptomic datasets correlated with disease severity or patient outcomes for both cancer and atherosclerotic CVD. These analyses confirmed numerous pathways known to underlie both diseases, such as inflammation and hypoxia, but also identified several novel shared pathways. We used these to explore common translational targets by applying the drug prediction software, OCTAD, to identify compounds that simultaneously reverse the gene expression signature for both diseases. These analyses suggest that certain tumor-specific therapeutic approaches may be implemented so that they avoid cardiovascular consequences, and in some cases may even be used to simultaneously target co-prevalent cancer and atherosclerosis.

Keywords: Cardiovascular medicine; Clinical genetics; Health sciences; Transcriptomics.

Grants and funding

R35 HL144475/HL/NHLBI NIH HHS/United States