The interplay between estrogen receptor beta and protein kinase C, a crucial collaboration for medulloblastoma cell proliferation and invasion

Identification and validation of a metabolism-related gene signature for predicting the prognosis of paediatric medulloblastoma

Medulloblastoma (MB) is a malignant brain tumour that is highly common in children and has a tendency to spread to the brain and spinal cord. MB is thought to be a metabolically driven brain tumour. Understanding tumour cell metabolic patterns and characteristics can provide a promising foundation for understanding MB pathogenesis and developing treatments. Here, by analysing RNA-seq data of MB samples from the Gene Expression Omnibus (GEO) database, 12 differentially expressed metabolic-related genes (DE-MRGs) were chosen for the construction of a predictive risk score model for MB. This model demonstrated outstanding accuracy in predicting the outcomes of MB patients and served as a standalone predictor. An evaluation of functional enrichment revealed that the risk score showed enrichment in pathways related to cancer promotion and the immune response. In addition, a high risk score was an independent poor prognostic factor for MB in patients with different ages, sexes, metastasis stages and subgroups (SHH and Group 4). Consistently, the metabolic enzyme ornithine decarboxylase (ODC1) was upregulated in MB patients with poor survival time. Inhibition of ODC1 in primary and metastatic MB cell lines decreased cell proliferation, migration and invasion but increased immune infiltration. This study could aid in identifying metabolic targets for MB as well as optimizing risk stratification systems and individual treatment plans for MB patients via the use of a metabolism-related gene prognostic risk score signature.

AURKA Enhances the Glycolysis and Development of Ovarian Endometriosis Through ERβ

Ovarian endometriosis (EMs) is a benign, estrogen-dependent gynecological disorder. Estrogen receptor beta (ERβ), a nuclear receptor for estradiol, plays an important role in the development of ovarian EMs. Here, we investigated the biological significance of aurora kinase A (AURKA) in ovarian EMs and the mechanism by which it regulates ERβ. We used immunohistochemical assays to verify that AURKA and ERβ were highly expressed in ectopic endometrial tissues. Cell proliferation and colony formation assays were used to demonstrate that AURKA promoted the proliferation of EMs cells. Wound-healing assay, Transwell migration assay, and Matrigel invasion assay further showed that AURKA enhanced the ability of EMs cells to migrate and invade. In addition, AURKA was shown to stimulate glycolysis in EMs cells by measuring the concentration of glucose and lactate in the cell supernatants. Moreover, the AURKA inhibitor alisertib was found to inhibit the progression of ovarian EMs and glycolysis in a mouse model of EMs by measuring ectopic tissues as well as by testing the peritoneal fluid of mice. Furthermore, coimmunoprecipitation assay showed that AURKA interacted with ERβ. The rescue experiments confirmed that AURKA regulated the development and glycolysis of ovarian EMs in an ERβ-dependent manner. AURKA contributed to the development of ovarian EMs by upregulating of ERβ. AURKA may represent a new target for the treatment of ovarian EMs.

Comprehensive analysis of gene expression profiles to identify differential prognostic factors of primary and metastatic breast cancer

Breast cancer accounts for nearly half of all cancer-related deaths in women worldwide. However, the molecular mechanisms that lead to tumour development and progression remain poorly understood and there is a need to identify candidate genes associated with primary and metastatic breast cancer progression and prognosis. In this study, candidate genes associated with prognosis of primary and metastatic breast cancer were explored through a novel bioinformatics approach. Primary and metastatic breast cancer tissues and adjacent normal breast tissues were evaluated to identify biomarkers characteristic of primary and metastatic breast cancer. The Cancer Genome Atlas-breast invasive carcinoma (TCGA-BRCA) dataset (ID: HS-01619) was downloaded using the mRNASeq platform. Genevestigator 8.3.2 was used to analyse TCGA-BRCA gene expression profiles between the sample groups and identify the differentially-expressed genes (DEGs) in each group. For each group, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were used to determine the function of DEGs. Networks of protein-protein interactions were constructed to identify the top hub genes with the highest degree of interaction. Additionally, the top hub genes were validated based on overall survival and immunohistochemistry using The Human Protein Atlas. Of the top 20 hub genes identified, four (KRT14, KIT, RAD51, and TTK) were considered as prognostic risk factors based on overall survival. KRT14 and KIT expression levels were upregulated while those of RAD51 and TTK were downregulated in patients with breast cancer. The four proposed candidate hub genes might aid in further understanding the molecular changes that distinguish primary breast tumours from metastatic tumours as well as help in developing novel therapeutics. Furthermore, they may serve as effective prognostic risk markers based on the strong correlation between their expression and patient overall survival.