Suppression of PTTG1 inhibits cell angiogenesis, migration and invasion in glioma cells

Pituitary tumor-transforming gene 1 (PTTG1) has been identified as an oncogene and is overexpressed in many tumor types. However, the role of PTTG1 in glioblastoma (GBM) has not been well characterized, especially in relation to angiogenesis, migration, and invasion. In the present study, our results showed that the expression of PTTG1 was significantly higher in patients with GBM. Bioinformatic analysis showed that angiogenesis and the cell migration-related process were increased in patients with high PTTG1 expression levels; meanwhile, PTTG1 was positively correlated with marker genes of angiogenesis, migration and the evasion of apoptosis. In vitro assays showed that PTTG1 knockdown dramatically suppressed angiogenesis, migration and invasion, and increased the apoptosis of GBM cells. Moreover, our results also showed that silencing PTTG1 suppressed the activity of the TGF-β/PI3K-AKT-mTOR pathway, which induced tumor deterioration in multiple organs. Overall, our findings indicate that PTTG1 is a glioma malignant factor that promotes angiogenesis, migration, invasion, and the evasion of apoptosis, and these roles may be related to the TGF-β/PI3K-AKT-mTOR pathway. Thus, the targeted inhibition of PTTG1 might be a novel therapeutic strategy and a potential diagnostic biomarker for GBM-targeted therapies.

Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis

Lung cancer (LC), with its high morbidity and mortality rates, is one of the most widespread and malignant neoplasms. Mediastinal lymph node metastasis (MLNM) severely affects postoperative survival of patients with LC. Additionally, the molecular mechanisms of LC with MLNM (MM LC) remain not well understood. To identify the key biomarkers in its carcinogenesis and development, the datasets GSE23822 and GSE13213 were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform functional annotations of DEGs. Search Tool for the Retrieval of Interacting Genes and Cytoscape were utilized to obtain the protein-protein interaction (PPI) network, and to analyze the most significant module. Subsequently, a Kaplan-Meier plotter was used to analyze overall survival (OS). Additionally, one co-expression network of the hub genes was obtained from cBioPortal. A total of 308 DEGs were identified in the two microarray datasets, which were mainly enriched during cellular processes, including the Gene Ontology terms ‘cell’, ‘catalytic activity’, ‘molecular function regulator’, ‘signal transducer activity’ and ‘binding’. The PPI network was composed of 315 edges and 167 nodes. Its significant module had 11 hub genes, and high expression of actin β, MYC, arginine vasopressin, vesicle associated membrane protein 2 and integrin subunit β1, and low expression of NOTCH1, synaptojanin 2 and intersectin 2 were significantly associated with poor OS. In summary, hub genes and DEGs presented in the present study may help identify underlying targets for diagnostic and therapeutic methods for MM LC.