“…Some differentially expressed non-marker genes confirmed clustering results from another perspective. Overexpression of GKN1 in epithelial cells ( Yoon et al, 2013 ), IGLL5 in B cells ( Zheng et al, 2021 ), TPSAB1 in mast cells ( Konnikova et al, 2021 ), and RGS5 in smooth muscle cells ( Silini et al, 2012 ) are consistent with previous studies, while CXCL14 in fibroblasts ( Wu et al, 2020 ) suggests that the conclusion from bulk RNA-seq may not be precise or versatile enough. Together with marker genes, these genes constitute 64 DEGs between IM and EGC, which reflect transcriptional heterogeneity among different cell types.…”
Tumor microenvironment and heterogeneity play vital roles in the development and progression of gastric cancer (GC). In the past decade, a considerable amount of single-cell RNA-sequencing (scRNA-seq) studies have been published in the fields of oncology and immunology, which improve our knowledge of the GC immune microenvironment. However, much uncertainty still exists about the relationship between the macroscopic and microscopic data in transcriptomics. In the current study, we made full use of scRNA-seq data from the Gene Expression Omnibus database (GSE134520) to identify 25 cell subsets, including 11 microenvironment-related cell types. The MIF signaling pathway network was obtained upon analysis of receptor–ligand pairs and cell–cell interactions. By comparing the gene expression in a wide variety of cells between intestinal metaplasia and early gastric cancer, we identified 64 differentially expressed genes annotated as immune response and cellular communication. Subsequently, we screened these genes for prognostic clinical value based on the patients’ follow-up data from The Cancer Genome Atlas. TMPRSS15, VIM, APOA1, and RNASE1 were then selected for the construction of LASSO risk scores, and a nomogram model incorporating another five clinical risk factors was successfully created. The effectiveness of least absolute shrinkage and selection operator risk scores was validated using gene set enrichment analysis and levels of immune cell infiltration. These findings will drive the development of prognostic evaluations affected by the immune tumor microenvironment in GC.
“…Some differentially expressed non-marker genes confirmed clustering results from another perspective. Overexpression of GKN1 in epithelial cells ( Yoon et al, 2013 ), IGLL5 in B cells ( Zheng et al, 2021 ), TPSAB1 in mast cells ( Konnikova et al, 2021 ), and RGS5 in smooth muscle cells ( Silini et al, 2012 ) are consistent with previous studies, while CXCL14 in fibroblasts ( Wu et al, 2020 ) suggests that the conclusion from bulk RNA-seq may not be precise or versatile enough. Together with marker genes, these genes constitute 64 DEGs between IM and EGC, which reflect transcriptional heterogeneity among different cell types.…”
Tumor microenvironment and heterogeneity play vital roles in the development and progression of gastric cancer (GC). In the past decade, a considerable amount of single-cell RNA-sequencing (scRNA-seq) studies have been published in the fields of oncology and immunology, which improve our knowledge of the GC immune microenvironment. However, much uncertainty still exists about the relationship between the macroscopic and microscopic data in transcriptomics. In the current study, we made full use of scRNA-seq data from the Gene Expression Omnibus database (GSE134520) to identify 25 cell subsets, including 11 microenvironment-related cell types. The MIF signaling pathway network was obtained upon analysis of receptor–ligand pairs and cell–cell interactions. By comparing the gene expression in a wide variety of cells between intestinal metaplasia and early gastric cancer, we identified 64 differentially expressed genes annotated as immune response and cellular communication. Subsequently, we screened these genes for prognostic clinical value based on the patients’ follow-up data from The Cancer Genome Atlas. TMPRSS15, VIM, APOA1, and RNASE1 were then selected for the construction of LASSO risk scores, and a nomogram model incorporating another five clinical risk factors was successfully created. The effectiveness of least absolute shrinkage and selection operator risk scores was validated using gene set enrichment analysis and levels of immune cell infiltration. These findings will drive the development of prognostic evaluations affected by the immune tumor microenvironment in GC.
Mast cells are derived from hematopoietic stem cell precursors and are essential to the genesis and manifestations of the allergic response. Activation of these cells by allergens leads to degranulation and elaboration of inflammatory mediators, responsible for regulating the acute dramatic inflammatory response seen. Mast cells have also been incriminated in such diverse disorders as malignancy, arthritis, coronary artery disease, and osteoporosis. There has been a recent explosion in our understanding of the mast cell and the associated clinical conditions that affect this cell type. Some mast cell disorders are associated with specific genetic mutations (such as the D816V gain-of-function mutation) with resultant clonal disease. Such disorders include cutaneous mastocytosis, systemic mastocytosis (SM), its variants (indolent/ISM, smoldering/SSM, aggressive systemic mastocytosis/ASM) and clonal (or monoclonal) mast cell activation disorders or syndromes (CMCAS/MMAS). Besides clonal mast cell activations disorders/CMCAS (also referred to as monoclonal mast cell activation syndromes/MMAS), mast cell activation can also occur secondary to allergic, inflammatory, or paraneoplastic disease. Some disorders are idiopathic as their molecular pathogenesis and evolution are unclear. A genetic disorder, referred to as hereditary alpha-tryptasemia (HαT) has also been described recently. This condition has been shown to be associated with increased severity of allergic and anaphylactic reactions and may interact variably with primary and secondary mast cell disease, resulting in complex combined disorders. The role of this review is to clarify the classification of mast cell disorders, point to molecular aspects of mast cell signaling, elucidate underlying genetic defects, and provide approaches to targeted therapies that may benefit such patients.
“…It is also worth noting that accumulations of tissue MCs can also be detected in various inflammatory disorders or genetic conditions. [43][44][45][46][47][48]…”
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