A deep learning approach reveals unexplored landscape of viral expression in cancer Journal Article
| Authors: | Elbasir, A.; Ye, Y.; Schäffer, D. E.; Hao, X.; Wickramasinghe, J.; Tsingas, K.; Lieberman, P. M.; Long, Q.; Morris, Q.; Zhang, R.; Schäffer, A. A.; Auslander, N. |
| Article Title: | A deep learning approach reveals unexplored landscape of viral expression in cancer |
| Abstract: | About 15% of human cancer cases are attributed to viral infections. To date, virus expression in tumor tissues has been mostly studied by aligning tumor RNA sequencing reads to databases of known viruses. To allow identification of divergent viruses and rapid characterization of the tumor virome, we develop viRNAtrap, an alignment-free pipeline to identify viral reads and assemble viral contigs. We utilize viRNAtrap, which is based on a deep learning model trained to discriminate viral RNAseq reads, to explore viral expression in cancers and apply it to 14 cancer types from The Cancer Genome Atlas (TCGA). Using viRNAtrap, we uncover expression of unexpected and divergent viruses that have not previously been implicated in cancer and disclose human endogenous viruses whose expression is associated with poor overall survival. The viRNAtrap pipeline provides a way forward to study viral infections associated with different clinical conditions. © 2023, The Author(s). |
| Keywords: | cancer survival; protein expression; human cell; overall survival; genetics; nonhuman; neoplasm; neoplasms; cancer model; rna; virus rna; virus infection; cancer tissue; tumor; virus; virus genome; rna sequence; virus expression; infectious disease; viruses; genome, viral; high throughput sequencing; high-throughput nucleotide sequencing; trichomonas vaginalis; cancer; humans; human; article; rna sequencing; ovarian cancer cell line; deep learning; malignant neoplasm; endogenous virus; geobacillus; virome |
| Journal Title: | Nature Communications |
| Volume: | 14 |
| ISSN: | 2041-1723 |
| Publisher: | Nature Publishing Group |
| Date Published: | 2023-02-11 |
| Start Page: | 785 |
| Language: | English |
| DOI: | 10.1038/s41467-023-36336-z |
| PUBMED: | 36774364 |
| PROVIDER: | scopus |
| PMCID: | PMC9922274 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 1 March 2023 -- Source: Scopus |
