Other

Dataset Information

0

Multi-omic approach identifies a transcriptional network coupling innate immune response to proliferation in the blood of COVID-19 cancer patients


ABSTRACT: Clinical outcomes of COVID-19 patients are worsened by the presence of co-morbidities, especially cancer for which mortality rate in cancer patients affected by COVID-19 is elevated. SARS-CoV-2 infection is known to alter immune system homeostasis. Whether cancer patients developing COVID-19 present alterations of immune functions which might contribute to worse outcomes has been so far poorly investigated. We conducted a multi-omic analysis of immunological parameters in COVID-19 patients with and without cancer. We found that 8 pro-inflammatory factors out of 27 analysed serum cytokines were modulated in COVID-19 patients irrespective of cancer status. Diverse subpopulations of T lymphocytes such as CD8+ T, CD4+ T central memory, Mucosal associated invariant T cells (MAIT) NKT and  T cells were reduced while B memory cells, plasmablasts, late NK and plasmacytoid dendritic cells were expanded in COVID-19 cancer patients. A 19 gene expression signature of peripheral blood cells was able to discriminate COVID-19 cancer and without cancer patients. Gene set enrichment analysis highlights an increased gene expression in Interferon  response and signalling which paired with aberrant cell cycle regulation in cancer patients. Ten out of these 19 genes were specific of COVID-19 cancer patients. Our findings illustrate a repertoire of aberrant alterations of gene expression in circulating immune cells of COVID-19 cancer patients that might contribute to decipher their higher frequency of severe events. We also unveil a transcriptional network involving gene regulators of both inflammation response and proliferation in PBMCs of COVID-19 cancer patients. This might also lead to design of novel therapeutic strategies for COVID-19 cancer patients.

ORGANISM(S): Homo sapiens

PROVIDER: GSE164571 | GEO | 2021/11/10

REPOSITORIES: GEO

Similar Datasets

2020-12-31 | GSE153610 | GEO
2021-01-27 | GSE157344 | GEO
2021-03-20 | E-MTAB-10169 | biostudies-arrayexpress
2021-10-01 | GSE178967 | GEO
2021-11-24 | GSE189506 | GEO
2021-04-26 | GSE164379 | GEO
2021-04-26 | GSE164380 | GEO
2023-11-21 | GSE247914 | GEO
2023-11-21 | GSE247913 | GEO
2023-11-21 | GSE247904 | GEO