Pan-cancer analyses reveal the stratification of patient prognosis by viral composition in tumor tissues

Yumeng Zhang, Lei Ji, Huakai Wen, Yuwen Chu, Weipeng Xing, Geng Tian, Yuhua Yao, Jialiang Yang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

The associations between cancer and bacteria/fungi have been extensively studied, but the implications of cancer-associated viruses have not been thoroughly examined. In this study, we comprehensively characterized the cancer virome of tissue samples across 31 cancer types, as well as blood samples from 23 cancer types. Our findings demonstrated the presence of viral DNA at low abundances in both tissue and blood across major human cancers, with significant differences in viral community composition observed among various cancer types. Furthermore, Cox regression analyses conducted on four cancers, including Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Stomach adenocarcinoma (STAD), and Uterine Corpus Endometrial Carcinoma (UCEC), revealed strong correlation between viral composition/abundance in tissues and patient survival. Additionally, we identified virus-associated prognostic signatures (VAPS) for these four cancers, and discerned differences in the interplay between VAPS and dominant bacteria in tissues among patients with varying survival risks. Notably, clinically relevant analyses revealed prognostic capacities of the VAPS in these four cancers. Taken together, our study provides novel insights into the role of viruses in tissue in the prognosis of multiple cancers and offers guidance on the use of tissue viruses to stratify prognosis for patients with cancer.

Original languageEnglish
Article number107586
JournalComputers in Biology and Medicine
Volume167
DOIs
StatePublished - Dec 2023
Externally publishedYes

Keywords

  • Intratumoral viruses
  • Machine learning
  • Microbiome
  • Pan-cancer
  • Survival

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