VaccinesBioinformaticsPeer-reviewed journal article
Genomic Biomarker Heterogeneities Between SARS-CoV-2 and COVID-19
Abstract
Genes functionally associated with SARS-CoV-2 infection and genes functionally related to COVID-19 disease can be different, whose distinction will become the first essential step for successfully fighting against the COVID-19 pandemic. Unfortunately, this first step has not been completed in all biological and medical research. Using a newly developed maxcompeting logistic classifier, two genes, ATP6V1B2 and IFI27, stand out to be critical in transcriptional response to SARS-CoV-2 infection with differential expressions derived from NP/OP swab PCR. This finding is evidenced by combining these two genes with one another gene in predicting disease status to achieve better-indicating accuracy than existing classifiers with the same number of genes. In addition, combining these two genes with three other genes to form a five-gene classifier outperforms existing classifiers with ten or more genes. These two genes can be critical in fighting against the COVID-19 pandemic as a new focus and direction with their exceptional predicting accuracy. Comparing the functional effects of these genes with a five-gene classifier with 100% accuracy identified and tested from blood samples in our earlier work, genes and their transcriptional response and functional effects to SARS-CoV-2 infection and genes and their functional signature patterns to COVID-19 antibody are significantly different, which can be interpreted as the former is the point of a phenomenon, and the latter is the essence of the disease. We will use a total of fourteen cohort studies (including breakthrough infections and omicron variants) with 1481 samples to justify our results. Such significant findings can help explore the causal and pathological clue between SARS-CoV-2 infection and COVID-19 disease and fight against the disease with more targeted genes, vaccines, antiviral drugs, and therapies.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- Citations
- 10
- FWCI
- 1.10
- Field percentile
- top 21%
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