Background and objectives Phenotypic switching in vascular smooth muscle cells (VSMCs) has been linked to aortic aneurysm, but the phenotypic landscape in aortic aneurysm is poorly understood. The present study aimed to analyse the phenotypic landscape, phenotypic differentiation trajectory, and potential functions of various VSMCs phenotypes in aortic aneurysm. Methods Single-cell sequencing data of 12 aortic aneurysm samples and 5 normal aorta samples (obtained from GSE166676 and GSE155468) were integrated by the R package Harmony. VSMCs were identified according to the expression levels of ACTA2 and MYH11. VSMCs clustering was determined by the R package ‘Seurat’. Cell annotation was determined by the R package ‘singleR’ and background knowledge of VSMCs phenotypic switching. The secretion of collagen, proteinases, and chemokines by each VSMCs phenotype was assessed. Cell‒cell junctions and cell–matrix junctions were also scored by examining the expression of adhesion genes. Trajectory analysis was performed by the R package ‘Monocle2’. qPCR was used to quantify VSMCs markers. RNA fluorescence in situ hybridization (RNA FISH) was performed to determine the spatial localization of vital VSMCs phenotypes in aortic aneurysms. Results A total of 7150 VSMCs were categorize into 6 phenotypes: contractile VSMCs, fibroblast-like VSMCs, T-cell-like VSMCs, adipocyte-like VSMCs, macrophage-like VSMCs, and mesenchymal-like VSMCs. The proportions of T-cell-like VSMCs, adipocyte-like VSMCs, macrophage-like VSMCs, and mesenchymal-like VSMCs were significantly increased in aortic aneurysm. Fibroblast-like VSMCs secreted abundant amounts of collagens. T-cell-like VSMCs and macrophage-like VSMCs were characterized by high chemokine levels and proinflammatory effects. Adipocyte-like VSMCs and mesenchymal-like VSMCs were associated with high proteinase levels. RNA FISH validated the presence of T-cell-like VSMCs and macrophage-like VSMCs in the tunica media and the presence of mesenchymal-like VSMCs in the tunica media and tunica adventitia. Conclusion A variety of VSMCs phenotypes are involved in the formation of aortic aneurysm. T-cell-like VSMCs, macrophage-like VSMCs, and mesenchymal-like VSMCs play pivotal roles in this process.
Background. Invasive candidiasis is a common cancer-related complication with a high fatality rate. If patients with a high risk of dying in the hospital are identified early and accurately, physicians can make better clinical judgments. However, epidemiological analyses and mortality prediction models of cancer patients with invasive candidiasis remain limited. Method. A set of 40 potential risk factors was acquired in a sample of 258 patients with both invasive candidiasis and cancer. To begin, risk factors for Candida albicans vs. non-Candida albicans infections and persistent vs. nonpersistent Candida infections were analysed using classic statistical methods. Then, we applied three machine learning models (random forest, logistic regression, and support vector machine) to identify prognostic indicators related to mortality. Prediction performance of different models was assessed by precision, recall, F 1 score, accuracy, and AUC. Results. Of the 258 patients both with invasive candidiasis and cancer included in the analysis. The median age of patients was 62 years, and 95 (36.82%) patients were older than 65 years, of which 178 (66.28%) were male. And 186 (72.1%) patients underwent surgery 2 weeks before data collection, 100 (39.1%) patients stayed in ICU during hospitalisation, 99 (38.4%) patients had bacterial blood infection, 85 (32.9%) patients had persistent invasive candidiasis, and 41 (15.9%) patients died within 30 days. The usage of drainage catheter and prolonged length of hospitalisation are the dominant risk factors for non-Candida albicans infections and persistent Candida infections, respectively. Risk factors, such as septic shock, history of surgery within the past 2 weeks, usage of drainage tubes, length of stay in ICU, total parenteral nutrition, serum creatinine level, fungal antigen, stay in ICU during hospitalisation, and total bilirubin level, were significant predictors of death. The RF model outperformed the LR and SVM models. Precision, recall, F 1 score, accuracy, and AUC for RF were 64.29%, 75.63%, 69.23%, 89.61%, and 91.28%. Conclusions. In this study, the machine learning-based models accurately predicted the prognosis of cancer and invasive candidiasis patients. The algorithm could be used to help clinicians in high-risk patients’ early intervention.
Background Melanoma is a highly malignant skin tumor with a poor prognosis. Identification of novel biomarkers might potentially reveal the underlying mechanisms of melanoma progression. Methods We demonstrated the relationship between pan-cancer CLEC2B expression and melanoma samples in The Cancer Genome Atlas (TCGA) database. Next, the Kaplan-Meier plot and Cox regression analysis determined the prognostic value of CLEC2B in melanoma. Biological pathway enrichment was screened by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), enabling the correlation analysis between the immune infiltration level and CLEC2B expression in melanoma. Our final claim was validated using qPCR, immunohistochemistry, Western blot, cell colony formation assays, ethynyldeoxyuridine (Edu) analysis, and cell Invasion assays. Results Our study revealed that the high CLEC2B expression correlates with poor overall survival of melanoma patients. Moreover, a low expression of CLEC2B was found in the A375 cell line. In addition, CLEC2B has significant prognostic value in melanoma diagnosis, with an AUC of 0.896. Prognostic analysis showed the low expression of CLEC2B to be independently associated with melanoma patients. Moreover, the expression of CLEC2B was significantly correlated with B cells, eosinophils, macrophages, neutrophils, NK cells, T helper cells, Tregs, Th1 cells, Th17 cells, and Th2 cells. PCR and immunohistochemistry indicated CLEC2B to be significantly downregulated in melanoma. The cell colony formation assay showed CLEC2B knockout increased the proliferation of A375 cells. Conclusion Our study established low levels of CLEC2B to be poor prognostic markers, enabling immunosuppressive cell infiltration in melanoma.
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