The coronavirus disease 2019 (COVID-19) pandemic has so far damaged the health of millions and has made the treatment of cancer patients more complicated, and so did acute myeloid leukemia (AML). The current problem is the lack of understanding of their interactions and suggestions of evidence-based guidelines or historical experience for the treatment of such patients. Here, we first identified the COVID-19-related differentially expressed genes (C-DEGs) in AML patients by analyzing RNA-seq from public databases and explored their enrichment pathways and candidate drugs. A total of 76 C-DEGs associated with the progress of AML and COVID-19 infection were ultimately identified, and the functional analysis suggested that there are some shared links between them. Their protein–protein interactions (PPIs) and protein–drug interactions were then recognized by multiple bioinformatics algorithms. Moreover, a COVID-19 gene-associated prognostic model (C-GPM) with riskScore was constructed, patients with a high riskScore had poor survival and apparently immune-activated phenotypes, such as stronger monocyte and neutrophil cell infiltrations and higher immunosuppressants targeting expressions, meaning which may be one of the common denominators between COVID-19 and AML and the reason what complicates the treatment of the latter. Among the study’s drawbacks is that these results relied heavily on publicly available datasets rather than being clinically confirmed. Yet, these findings visualized those C-DEGs’ enrichment pathways and inner associations, and the C-GPM based on them could accurately predict survival outcomes in AML patients, which will be helpful for further optimizing therapies for AML patients with COVID-19 infections.

Mounting evidence indicates that immune status plays a crucial role in tumor progress and metastasis, while there are no effective and easily assayed biomarkers to reflect it in uterine corpus endometrial carcinoma (UCEC) patients. Here, we attempted to identify the potential biomarkers that were differentially expressed between normal and tumor tissues and involved in prognosis and immune microenvironment of UCEC patients. RNA-seq data with relevant clinical information were obtained from The Cancer Genome Atlas (TCGA). ssGSEA algorithm was applied to calculate the enrichment scores of every tumor infiltration lymphocyte (TIL) set in each sample, and patients were then divided into three clusters using multiple R packages. Cox analysis, ESTIMATE, and CIBERSORT were utilized to determine the differentially expressed immune-related genes (DEIGs) with overall survival, and to explore their roles in prognosis, immune microenvironment, and immunotherapeutic response. The TIMER and TISIDB databases were utilized to predict the effectiveness of immunotherapy in UCEC patients. LTA was finally identified to be significantly upregulated in tumor tissues and closely associated with prognosis and immunological status, which was then verified in GSE17025. In multivariate analysis, the hazard ratio of LTA was 0.42 with 95% CI (0.22–0.80) (p = 0.008). Patients with high LTA expression had better survival and apparently immune-activated phenotypes, such as more tumor mutation burden (TMB), stronger immune cell infiltrations, higher expression of immunosuppressive points, and higher immunophenoscore, meaning they had an immunotherapeutic advantage over those with low LTA expression. TIMER and TISIDB indicated that LTA was highly expressed in UCEC, and its expression was negatively correlated with stages and positively related to prognosis. Additionally, we found that LTA ectopic expression weakened the proliferation ability of RL95-2 cells. All these findings indicated that LTA could act as a novel and easily assayed biomarker to predict immunological status and clinical outcomes and even as an antioncogene to explore UCEC in depth.