Identification of potential therapeutic targets for gliomas by bioinformatics analysis

Exploration of the radiosensitivity-related prognostic risk signature in patients with glioma: evidence from microarray data

BACKGROUND

Gene expression signatures can be used as prognostic biomarkers in various types of cancers. We aim to develop a gene signature for predicting the response to radiotherapy in glioma patients.

METHODS

Radio-sensitive and radio-resistant glioma cell lines (M059J and M059K) were subjected to microarray analysis to screen for differentially expressed mRNAs. Additionally, we obtained 169 glioblastomas (GBM) samples and 5 normal samples from The Cancer Genome Atlas (TCGA) database, as well as 80 GBM samples and 4 normal samples from the GSE7696 set. The "DESeq2" R package was employed to identify differentially expressed genes (DEGs) between the normal brain samples and GBM samples. Combining the prognostic-related molecules identified from the TCGA, we developed a radiosensitivity-related prognostic risk signature (RRPRS) in the training set, which includes 152 patients with glioblastoma. Subsequently, we validated the reliability of the RRPRS in a validation set containing 616 patients with glioma from the TCGA database, as well as an internal validation set consisting of 31 glioblastoma patients from the Nanfang Hospital, Southern Medical University.

RESULTS

Based on the microarray and LASSO COX regression analysis, we developed a nine-gene radiosensitivity-related prognostic risk signature. Patients with glioma were divided into high- or low-risk groups based on the median risk score. The Kaplan-Meier survival analysis showed that the progression-free survival (PFS) of the high-risk group was significantly shorter. The signature accurately predicted PFS as assessed by time-dependent receiver operating characteristic curve (ROC) analyses. Stratified analysis demonstrated that the signature is specific to predict the outcome of patients who were treated using radiotherapy. Univariate and multivariate Cox regression analysis revealed that the predictor was an independent predictor for the prognosis of patients with glioma. The prognostic nomograms accompanied by calibration curves displayed the 1-, 2-, and 3-year PFS and OS in patients with glioma.

CONCLUSION

Our study established a new nine-gene radiosensitivity-related prognostic risk signature that can predict the prognosis of patients with glioma who received radiotherapy. The nomogram showed great potential to predict the prognosis of patients with glioma treated using radiotherapy.

ITPKA induces cell senescence, inhibits ovarian cancer tumorigenesis and can be downregulated by miR-203

Overcoming senescence is a feature of ovarian cancer cells; however, the mechanisms underlying senescence regulation in ovarian cancer cells remain largely unknown. In this study, we found that ITPKA was downregulated in ovarian cancer samples, and the lower expression correlated with poor survival. Overexpression of ITPKA inhibited the anchorage-independent growth of ovarian cancer cells and induced senescence. However, knockdown of ITPKA promoted the anchorage-independent growth of ovarian cancer cells and inhibited senescence. Mechanistically, ITPKA was found to interact with MDM2, which stabilized P53, an essential regulator of senescence. Moreover, ITPKA was negatively regulated by miR-203, a microRNA that has been previously reported to be upregulated in ovarian cancer. Taken together, the results of this study demonstrated the tumor suppressive roles of ITPKA in ovarian cancer and provided a good explanation for the oncogenic roles of miR-203.

Identification of hub genes and pathways in glioblastoma by bioinformatics analysis

Glioblastoma (GBM) is the most common type of malignant brain tumor, and is associated with poor patient prognosis. A comprehensive understanding of the molecular mechanism underlying GBM may help to guide the identification of novel diagnoses and treatment targets. The gene expression profile of the GSE4290 GBM dataset was analyzed in order to identify differentially expressed genes (DEGs). Enriched pathways were identified through Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses. A protein-protein interaction network was constructed in order to identify hub genes and for module analysis. Expression and survival analyses were conducted in order to screen and validate critical genes. A total of 1,801 DEGs were recorded, including 620 upregulated and 1,181 downregulated genes. Upregulated DEGs were enriched in the terms ‘mitotic cell cycle process’, ‘mitotic cell cycle’ and ‘cell cycle process’. Downregulated genes were enriched in ‘transsynaptic signaling’, ‘anterograde transsynaptic signaling’ and ‘synaptic signaling’. A total of 15 hub genes, which displayed a high degree of connectivity, were selected. These genes included vascular endothelial growth factor A, cyclin-dependent kinase 1 (CDK1), cell-division cycle protein 20 (CDC20), aurora kinase A (AURKA), and budding uninhibited by benzimidazoles 1 (BUB1). The identified DEGs and hub genes may help guide investigations on the mechanisms underlying the development and progression of GBM. CDK1, CDC20, AURKA and BUB1, which are involved in cell cycle pathways, may be potential targets in the diagnosis and therapy of GBM.

Bioinformatics analysis of microarray data to reveal the pathogenesis of diffuse intrinsic pontine glioma

Background

Diffuse intrinsic pontine glioma (DIPG) is the main cause of pediatric brain tumor death. This study was designed to identify key genes associated with DIPG.

Methods

The gene expression profile GSE50021, which consisted of 35 pediatric DIPG samples and 10 normal brain samples, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by limma package. Functional and pathway enrichment analyses were performed by the DAVID tool. Protein–protein interaction (PPI) network, and transcription factor (TF)–microRNA (miRNA)–target gene network were constructed using Cytoscape. Moreover, the expression levels of several genes were validated in human glioma cell line U251 and normal glia HEB cells through real-time polymerase chain reaction (PCR).

Results

A total of 378 DEGs were screened (74 up-regulated and 304 down-regulated genes). In the PPI network, GRM1, HTR2A, GRM7 and GRM2 had higher degrees. Besides, GRM1 and HTR2A were significantly enriched in the neuroactive ligand–receptor interaction pathway, and calcium signaling pathway. In addition, TFAP2C was a significant down-regulated functional gene and hsa-miR-26b-5p had a higher degree in the TF-miRNA-target gene network. PCR analysis revealed that GRM7 and HTR2A were significantly downregulated while TFAP2C was upregulated in U251 cells compared with that in HEB cells (p < 0.001). GRM2 was not detected in cells.

Conclusions

GRM1 and HTR2A might function in DIPG through the neuroactive ligand–receptor interaction pathway and the calcium signaling pathway. Furthermore, the TFAP2C and hsa-miR-26b-5p might play important roles in the development and progression mechanisms of DIPG.