High‐dimensional analyses of tissue samples from patients with rheumatic diseases are providing increasingly detailed descriptions of the immune cell populations that infiltrate tissues in different rheumatic diseases. Here we review key observations emerging from high‐dimensional analyses of T cells within tissues in different rheumatic diseases, highlighting common themes across diseases as well as distinguishing features. Single‐cell RNA sequencing analyses capture several dimensions of T‐cell states, yet surprisingly, these analyses generally have not demonstrated distinct clusters of paradigmatic T‐cell effector subsets, such as T helper (Th) 1, Th2, and Th17 cells. Rather, global transcriptomics robustly identify both proliferating T cells and regulatory T cells and have also helped to reveal new effector subsets in inflamed tissues, including T peripheral helper cells and granzyme K+ T cells. Further characterization of the T‐cell populations that accumulate within target tissues should enable more precise targeting of biologic therapies and accelerate development of more specific biomarkers to track activity of relevant immune pathways in patients with rheumatic diseases.
Background: Angiosarcoma of the breast is a rare malignancy. There are little data evaluating the survival and estimating the prognostic factors. The best surgical management and the role of systemic adjuvant therapy remain ill-defined.This study aimed to investigate the clinicopathological features, survival, and prognostic factors of breast angiosarcoma.
Methods:The data on patients diagnosed with breast angiosarcoma were extracted from the Surveillance, Epidemiology, and End Results database . Univariate and multivariate Cox regression analyses were used to estimate the influential prognostic factors. The overall survival (OS) and disease-specific survival (DSS) of patients with breast angiosarcoma were evaluated.Results: This study included 656 patients diagnosed with breast angiosarcoma between 1975 and 2016. The 5-year OS rate of all patients was 44.9% (95% CI 40.8-49.0). In both OS and DSS, Kaplan-Meier survival analyses revealed significant differences for both OS and DSS according to age, year at diagnosis, laterality, grade, and stage (all log-rank p < 0.05). Multivariate analysis suggested that lesions of the right breast, poor differentiation, and advanced stage were independent risk factors for OS or DSS (all p < 0.05). Older age was a risk factor in OS, but was protective in DSS. In primary breast angiosarcoma, age, laterality, grade, and stage were independent prognostic factors in OS and DSS (all p < 0.05).Mastectomy was also a risk factor in DSS (p = 0.034). The proportion of patients with grade III and regional disease was larger in the mastectomy group.
Conclusion:Angiosarcoma of the breast had a poor prognosis. In our study, age, laterality, histologic grade, and stage were identified as significant prognostic factors. Why patients with angiosarcoma of the right breast had a worse prognosis remains equivocal. Mastectomy was adopted more often by surgeons in this cohort study for patients with advanced primary breast angiosarcoma.
Background: Matrix metalloproteases (MMPs) are encoded by a family of genes that are related to cancer progression, and the overexpression of most MMP genes in cancers is associated with the promotion of invasion, angiogenesis, and immune surveillance avoidance. However, the expression patterns of all MMP genes at the transcriptome and single-cell levels have not been investigated from a pan-cancer perspective. Methods: The Cancer Genome Atlas transcriptome and single-cell sequencing pan-cancer data from the Gene Expression Omnibus were used in the present study to examine the expression patterns of these genes. The MMP-based diagnostic model was constructed using least absolute shrinkage and selection operator regression analysis. Tumors were classified into high and low score MMP groups using ssGSEA. Single-cell data were analyzed using the Seurat R package, and MMP gene expression was validated using quantitative real-time polymerase chain reaction.Results: MMP1, MMP11, and MMP12 expression was upregulated in almost all cancers. MMP19 and MMP27 expression was significantly downregulated in eight to nine cancer types. Correlation analysis results suggested a relationship between MMP expression and tumor immunity and stemness. Single-cell analysis results revealed diverse MMP expression patterns in different cell clusters. Conclusion: The majority of MMPs showed increased expression across cancers, with potential diagnostic value.
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