Heterogeneous response of endothelial cells to insulin-like growth factor 1 treatment is explained by spatially clustered sub-populations

Pulmonary Vascular Remodeling in Pulmonary Hypertension

Pulmonary vascular remodeling is the critical structural alteration and pathological feature in pulmonary hypertension (PH) and involves changes in the intima, media and adventitia. Pulmonary vascular remodeling consists of the proliferation and phenotypic transformation of pulmonary artery endothelial cells (PAECs) and pulmonary artery smooth muscle cells (PASMCs) of the middle membranous pulmonary artery, as well as complex interactions involving external layer pulmonary artery fibroblasts (PAFs) and extracellular matrix (ECM). Inflammatory mechanisms, apoptosis and other factors in the vascular wall are influenced by different mechanisms that likely act in concert to drive disease progression. This article reviews these pathological changes and highlights some pathogenetic mechanisms involved in the remodeling process.

Endothelial cells in the pathogenesis of pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a devastating disease that involves pulmonary vasoconstriction, small vessel obliteration, large vessel thickening and obstruction, and development of plexiform lesions. PAH vasculopathy leads to progressive increases in pulmonary vascular resistance, right heart failure and, ultimately, premature death. Besides other cell types that are known to be involved in PAH pathogenesis (e.g. smooth muscle cells, fibroblasts and leukocytes), recent studies have demonstrated that endothelial cells (ECs) have a crucial role in the initiation and progression of PAH. The EC-specific role in PAH is multi-faceted and affects numerous pathophysiological processes, including vasoconstriction, inflammation, coagulation, metabolism and oxidative/nitrative stress, as well as cell viability, growth and differentiation. In this review, we describe how EC dysfunction and cell signalling regulate the pathogenesis of PAH. We also highlight areas of research that warrant attention in future studies, and discuss potential molecular signalling pathways in ECs that could be targeted therapeutically in the prevention and treatment of PAH.

Emerging machine learning approaches to phenotyping cellular motility and morphodynamics

Cells respond heterogeneously to molecular and environmental perturbations. Phenotypic heterogeneity, wherein multiple phenotypes coexist in the same conditions, presents challenges when interpreting the observed heterogeneity. Advances in live cell microscopy allow researchers to acquire an unprecedented amount of live cell image data at high spatiotemporal resolutions. Phenotyping cellular dynamics, however, is a nontrivial task and requires machine learning (ML) approaches to discern phenotypic heterogeneity from live cell images. In recent years, ML has proven instrumental in biomedical research, allowing scientists to implement sophisticated computation in which computers learn and effectively perform specific analyses with minimal human instruction or intervention. In this review, we discuss how ML has been recently employed in the study of cell motility and morphodynamics to identify phenotypes from computer vision analysis. We focus on new approaches to extract and learn meaningful spatiotemporal features from complex live cell images for cellular and subcellular phenotyping.

Identification of Aurora Kinase A as a Biomarker for Prognosis in Obesity Patients with Early Breast Cancer

BACKGROUND

Obesity is associated both with a higher risk of developing breast cancer, particularly in postmenopausal women, and with worse disease outcome for women of all ages. Previous investigation suggested Aurora A kinase was able to partially restore the functionalities of obese adipose-derived mesenchymal stem cells by stabilizing their primary cilia and reestablishing a balance of multiple stemness-associated genes. The association between Aurora A and obesity breast cancer is still unclear. We hypothesized that overexpression of Aurora A was associated with poor survival in obesity breast cancer and the related axis mechanism was involved.

METHODS

A total of 517 primary breast cancer specimens were collected from the First Affiliated Hospital of China Medical University between January 2011 and November 2016. Our independent variable was BMI at baseline, categorized as overweight (BMI ≥25 kg/m2, as obesity cohort), and normal (18.5 ≤ BMI <25 kg/m2, as non-obesity cohort). The immunohistochemical (IHC) staining was performed with Aurora A, Survivin, MMP11, Cyclin B1, and Cathepsin L. Kaplan-Meier curve was used to analyze overall survival in our cohorts and TCGA-BRCA data (GSE3494). Log rank test was used to calculate P values. Protein-protein interaction (PPI) network analysis and MCODE model were used to analyze the Aurora-altered signal pathway from GSE78958.

RESULTS

Among 517 breast patients, Aurora A-positive (staining scores ≥4) was significantly higher in obesity breast carcinoma compared with non-obesity cancer carcinoma (χ 2=9.79, P=0.002), with more frequency in hormone receptor-negative (68.4% vs 77.9%, P=0.015) and HER2-positive patients (28.7% vs 17.9%, P=0.003). High Aurora A expression was remarkably and significantly associated with overall survival (OS) (8-year OS ratio: 69.5% vs 81.1%, OR=1.76, 95% CI: 1.03~3.02, P=0.041) in obesity cohort. Interestingly, higher expression of Aurora A was not associated with a shorter overall survival time among the non-obesity breast cancer (8-year OS ratio: 81.4% vs 85.8%, OR=1.40, 95% CI: 0.79~2.45, P=0.229). As for RFS, the expression levels of Aurora A expression genes have no significance with RFS statistically in non-obesity and obesity patients. Aurora A and lymph node metastases were significantly poor prognostic factors for OS, and borderline significance was noted for high BMI. Kaplan-Meier survival analysis from TCGA database confirmed that the high Aurora A expression group had worse prognosis (HR=1.47, 95% CI: 1.14-1.90, P=0.003). The KEGG pathway enrichment results were consistent with GO biological process term analysis, in which CCNB1 was enriched for upregulated Aurora A. In our samples, Aurora A level on tumor cytoplasm had broad connections with Cyclin B1 by IHC correlation analysis (correlation coefficient = 0.227, P=0.001).

CONCLUSION

Our finding demonstrates here for the first time that high expression of Aurora A was notably correlated with early recurrence and poor overall survival in obesity patients with early breast cancer. The Aurora A-Cyclin B1 axis could be a potential promising therapeutic target for cancer intervention and therapy.