Single-cell transcriptomics reveals pre-existing COVID-19 vulnerability factors in lung cancer patients.
Wendao LiuWenbo LiZhong-Ming ZhaoPublished in: Molecular cancer research : MCR (2023)
COVID-19 and cancer are major health threats, and individuals may develop both simultaneously. Recent studies have indicated that cancer patients are particularly vulnerable to COVID-19, but the molecular mechanisms underlying the associations remain poorly understood. To address this knowledge gap, we collected single-cell RNA sequencing data from COVID-19, lung adenocarcinoma, small cell lung carcinoma patients and normal lungs to perform an integrated analysis. We characterized altered cell populations, gene expression, and dysregulated intercellular communication in diseases. Our analysis identified pathological conditions shared by COVID-19 and lung cancer, including upregulated TMPRSS2 expression in epithelial cells, stronger inflammatory responses mediated by macrophages, increased T cell response suppression, and elevated fibrosis risk by pathological fibroblasts. These pre-existing conditions in lung cancer patients may lead to more severe inflammation, fibrosis, and weakened adaptive immune response upon COVID-19 infection. Our findings revealed potential molecular mechanisms driving an increased COVID-19 risk in lung cancer patients and suggested preventive and therapeutic targets for COVID-19 in this population. Implications: Our work reveals the potential molecular mechanisms contributing to the vulnerability to COVID-19 in lung cancer patients.
Keyphrases
- coronavirus disease
- single cell
- sars cov
- gene expression
- rna seq
- immune response
- healthcare
- respiratory syndrome coronavirus
- ejection fraction
- public health
- newly diagnosed
- high throughput
- climate change
- dna methylation
- mental health
- prognostic factors
- early onset
- inflammatory response
- cell therapy
- patient reported outcomes
- poor prognosis
- squamous cell carcinoma
- human health
- single molecule
- social media
- mesenchymal stem cells
- drug induced
- big data
- artificial intelligence
- papillary thyroid