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Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: Subcutaneous panniculitis-like T-cell lymphoma (SPTCL) is a malignant primary T-cell lymphoma that is challenging to distinguish from autoimmune disorders and reactive panniculitides. Delay in diagnosis and a high misdiagnosis rate affect the prognosis and survival of patients. The difficulty of diagnosis is mainly due to an incomplete understanding of disease pathogenesis. Methods: We performed single-cell RNA sequencing of matched subcutaneous lesion tissue, peripheral blood, and bone marrow from a patient with SPTCL, as well as peripheral blood, bone marrow, lymph node, and lung tissue samples from healthy donors as normal controls. We conducted cell clustering, gene expression program identification, gene differential expression analysis, and cell-cell interaction analysis to investigate the ecosystem of SPTCL. Results: Based on gene expression profiles in a single-cell resolution, we identified and characterized the malignant cells and immune subsets from a patient with SPTCL. Our analysis showed that SPTCL malignant cells expressed a distinct gene signature, including chemokines families, cytotoxic proteins, T cell immune checkpoint molecules, and the immunoglobulin family. By comparing with normal T cells, we identified potential novel markers for SPTCL (e.g., CYTOR, CXCL13, VCAM1, and TIMD4) specifically differentially expressed in the malignant cells. We also found that macrophages and fibroblasts dominated the cell-cell communication landscape with the SPTCL malignant cells. Conclusions: This work offers insight into the heterogeneity of subcutaneous panniculitis-like T-cell lymphoma, providing a better understanding of the transcription characteristics and immune microenvironment of this rare tumor. [ABSTRACT FROM AUTHOR]