BackgroundPrimary central nervous system lymphoma (PCNSL) is characterized by a lack of specificity and poor prognosis. Further understanding of the tumor heterogeneity and molecular phenotype of PCNSL is of great significance for improving the diagnosis and treatment of this disease.MethodsTo explore the distinct phenotypic states of PCNSL, transcriptome-wide single-cell RNA sequencing was performed on 34,851 PCNSL cells from patients. The cell types, heterogeneity, and gene subset enrichment of PCNSL were identified. A comparison of the PCNSL cells with 21,250 normal human fetal brain (nHFB) cells was further analyzed to reveal the differences between PCNSL and normal sample.ResultsSix cell populations were mainly identified in the PCNSL tissue, including four types of immune cells—B cell, T cell, macrophage and dendritic cell—and two types of stromal cells: oligodendrocyte and meningeal cell. There are significant cellular interactions between B cells and several other cells. Three subpopulations of B cells indicating diffident functions were identified, as well as a small number of plasma cells. Different subtypes of T cells and dendritic cells also showed significant heterogeneity. It should be noted that, compared with normal, the gene expression and immune function of macrophages in PCNSL were significantly downregulated, which may be another important feature of PCNSL in addition to B cell lesions and may be a potential target for PCNSL therapy.

Chronic obstructive pulmonary disease (COPD) is a complex disease, which involves dysfunctions in multi-omics. The changes in biological processes, such as adhesion junction, signaling transduction, transcriptional regulation, and cell proliferation, will lead to the occurrence of COPD. A novel systematic approach MMMG (Methylation-MicroRNA-MRNA-GO) was proposed to identify potential COPD genes by integrating function information with a methylation profile, a microRNA expression profile and an mRNA expression profile. 8 co-functional classes and 102 potential COPD genes were identified. These genes displayed a high performance in classifying COPD patients and normal samples, revealed COPD-related pathways, and have been confirmed to be associated with COPD by Matthews correlation coefficient (MCC)-values, literature, an independent data set, and pathways. The MMMG method that analyzed multi-omics data at the functional level could effectively identify potential COPD genes. These potential COPD genes would provide in-depth insights into understanding the complexity of COPD genome landscapes, improve the early diagnostics, and guide new efforts to develop therapeutics in the future.

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The formation and death of macrophages and foam cells are one of the major factors that cause coronary heart disease (CHD). In our study, based on the Edinburgh Human Metabolic Network (EHMN) metabolic network, we built an enzyme network which was constructed by enzymes (nodes) and reactions (edges) called the Edinburgh Human Enzyme Network (EHEN). By integrating the subcellular location information for the reactions and refining the protein-reaction relationships based on the location information, we proposed a computational approach to select modules related to programmed cell death. The identified module was in the EHEN-mitochondria (EHEN-M) and was confirmed to be related to programmed cell death, CHD pathogenesis, and lipid metabolism in the literature. We expected this method could analyze CHD better and more comprehensively from the point of programmed cell death in subnetworks.