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Background The new coronavirus pneumonia (NCP) is now causing a severe public health emergency. The novel coronavirus 2019 (2019-nCoV) infected individuals by binding human angiotensin converting enzyme II (ACE2) receptor. ACE2 is widely expressed in multiple organs including respiratory, cardiovascular, digestive and urinary systems in healthy individuals. These tissues with high expression level of ACE2 seemed to be more vulnerable to SARS-CoV-2 infection. Recently, it has been reported that patients with tumors were likely to be more susceptible to SARS-CoV-2 infection and indicated poor prognosis. Methods The tissue atlas database and the blood atlas were used to analyze the distribution of ACE2 in human tissues or organs of cancers and normal samples. Starbase dataset was applied to predict the prognosis of cancers according to expression level of ACE2. Results In this study, we demonstrated a landscape profiling analysis on expression level of ACE2 in pan-cancers and showed the risky of different type of cancers to SARS-CoV-2 according to the expression level of ACE2. In addition, we found that ACE2 was both differential expression and related to the prognosis only in liver hepatocellular carcinoma (LIHC). Relative high expression of ACE2 indicated a favorable prognosis in LIHC, but they might be more susceptible to SARS-CoV-2. Conclusions We indeed emphasized that LIHC patients with high expression level of ACE2 should be more cautious of the virus infection. Our study might provide a potential clue for preventing infection of SARS-CoV-2 in cancers.

BACKGROUND: Acute myeloid leukemia (AML) is a type of cancer that consists of a group of hematological malignancies with high heterogeneity. DNA methyltransferase 3A (DNMT3A)-mutated AML patients have a poor prognosis. Some long non-coding RNAs (lncRNAs) have been reported to enhance therapeutic sensitivity, and so could affect the overall survival rate of elderly cytogenetically normal acute myeloid leukemia (CN-AML) patients; however, studies on the lncRNA signature in DNMT3A-mutated AML are rare. METHOD: The DNMT3A R878H conditional knock-in mouse model was constructed to explore the lncRNAs of DNMT3A mutation by using the Cuffcomparison method. Cis and trans regulation networks were used to predict candidate genes. The expression levels in leukemic cell lines and the prognostic index of these candidate genes were analyzed with the Broad Institute Cancer Cell Line Encyclopedia (CCLE) and OncoLnc databases. The data for each sample were statistically analyzed using GraphPad Prism. RESULTS: In this study, we applied the DNMT3A R878H conditional knock-in mouse model to explore the lncRNA epigenetic landscape of DNMT3A mutation by using the Cuffcomparison method. Twenty-three differentially expressed lncRNAs were identified in Dnmt3a(R878H/WT)Mx1-Cre(+) mice. We next predicted the downstream targetable genes regulated by these lncRNAs through cis and trans regulation networks and found 124 candidate genes are related to these lncRNAs. In further analysis of 124 genes, we found that increased mRNA expression levels of interleukin 1 receptor type 2 (IL1R2), Krüppel-like factor 13 (KLF13), ATPase H+ transporting V1 subunit A (ATP6V1A), proteasome 26S Subunit, non-ATPase 3 (PSMD3), and pyrroline-5-carboxylate reductase 2 (PYCR2) were associated with poor prognosis in AML. Functional analysis of these genes demonstrated that the pathways involved in autophagy, cell cycle, and hematopoietic stem cell differentiation were more enriched in Dnmt3a(R878H/WT)Mx1-Cre(+) mice. CONCLUSION: Our study was the first to use DNMT3A R878H conditional knock-in mouse model to predict the specific lncRNAs regulated by the DNMT3A mutation in AML. Six candidate genes were found to be associated with DNMT3A mutation with poor prognosis. Our results provided a possible treatment strategy for this disease.