Developing a novel SARS-CoV-2 risk index to predict the prognostic and therapeutic effects in acute myeloid leukemia

Abstract

There is growing evidence of a strong association between SARS-CoV-2 and cancer prognosis and treatment outcome. However, there are no reliable SARS-CoV-2 assessment models to accurately predict prognostic and therapeutic effects in acute myeloid leukemia (AML). Here, differentially expressed genes associated with SARS-CoV-2 were detected, and multiple Cox regression methods were used to construct a SARS-CoV-2 risk index (SC2RI). Then, RT-qPCR was used to validate the gene expression levels in the AML samples. Finally, we explored how the SC2RI affected prognosis, immune infiltration, immunotherapy, and drug sensitivity in AML. We found that CYB5R3 and CLIP4 had been confirmed as hub genes in AML and were used to generate the SC2RI. The datasets indicated that the SC2RI had a superior predictive impact on the prognosis of AML. In addition, high expression of immune checkpoints and numerous immunological infiltrations were substantially correlated with a high SC2RI. However, it responded poorly to immune checkpoint blockade, which may be related to T-cell dysfunction, lack of effective antigens, and deficiency of synaptic capacity. Moreover, a high SC2RI was less susceptible to mTOR-related pathway medications but more sensitive to cell cycle suppressors. Therefore, categorization based on SC2RI could enhance the prognostic prediction of AML and help identify novel therapeutic approaches.

Keywords

• SC2RI
• AML
• Prognosis
• Immunotherapy
• Drug sensitivity