Behnaz Yazdani

Objective Glioblastoma Multiforme (GBM) is a devastating neoplastic growth affecting the brain, with a dismal prognosis. The underlying diagnostic and prognostic potential hypoxia-inducible-factors and BMP pathway in this devastating malignancy remains poorly understood, lacking compelling preventive strategies. Methods and materials: A bioinformatic study was conducted using integrative bioinformatics techniques for the analysis of GBM count data, which were obtained from the Cancer Genome Atlas (TCGA) database and underwent normalization and differential expression analysis (DEG). Gene Set Enrichment Analysis (GSEA), Differential gene expression analysis, and correlation analysis using Pearson method were conducted for the genes involved in the BMP pathway. Gene Ontology and Protein-protein interaction analyses were employed. Survival analysis and Receiver Operating test (ROC) were also performed to identify potential prognostic and diagnostic biomarkers. Results The results revealed that the expression levels of EPAS1, HIF3A, CHRDL1, NOG, BMP6, and AHSG genes did not exhibit a statistically significant difference between GBM cancer samples and normal tissue samples. Further DEG analysis indicated that the majority of genes from the BMP pathogenesis were significantly downregulated in GBM cancer samples and a positive correlation was observed between the expression levels of EPAS1, BMPR2, and MAPK1 genes. the Top DEGs were correlated with specific pathways, such as the TGF-beta signaling pathway, pathways in cancer, and the cell cycle. By ROC test we identified the best diagnostic biomarkers for GBM and SMURF1 gene is predicted to have significant prognostic capability. Conclusion These findings highlight the possible utility of these genes as promising diagnostic and prognostic biomarkers for the early detection of GBM.

Since the emergence of SARS-CoV-2, many genetic variations within its genome have been identified, but only a few mutations have been found in nonstructural proteins (NSPs). Among this class of viral proteins, NSP3 is a multidomain protein with 16 different domains, and its largest domain is known as the macrodomain or Mac1 domain. In this study, we present a virtual screening campaign in which we computationally evaluated the NCI anticancer library against the NSP3 Mac1 domain, using Molegro Virtual Docker. The top hits with the best MolDock and Re-Rank scores were selected. The physicochemical analysis and drug-like potential of the top hits were analyzed using the SwissADME data server. The binding stability and affinity of the top NSC compounds against the NSP3 Mac1 domain were analyzed using molecular dynamics (MD) simulation, using Desmond software, and their interaction energies were analyzed using the MM/GBSA method. In particular, by applying subsequent computational filters, we identified 10 compounds as possible NSP3 Mac1 domain inhibitors. Among them, after the assessment of binding energies (ΔG_bind) on the whole MD trajectories, we identified the four most interesting compounds that acted as strong binders of the NSP3 Mac1 domain (NSC-358078, NSC-287067, NSC-123472, and NSC-142843), and, remarkably, it could be further characterized for developing innovative antivirals against SARS-CoV-2.

Background: Hypoxia-inducible factors (HIFs) are transcription factors that get activated and stabilized in the heterodimerized form under hypoxic conditions. The three members of the HIF alpha factors share high structural similarity but have tissue-specific expression patterns. A majority of studies have reported the importance of the HIF1A and HIF2A activity in the survival, proliferation, metastatic potential, and metabolic regulation of hypoxic cancer cells. However, the importance of the expression pattern and activity of HIF3A in a variety of cancers remains unknown. Method and materials: The expression profile of 13 different types of The Cancer Genome Atlas (TCGA) cancer samples were downloaded, normalized and differential gene expression analysis (DGE) was performed to compare the expression pattern of HIF alpha family members in cancer and adjacent normal tissues, as well as at different stages and tumor-sizes. Receiver operating characteristic (ROC) test and survival analysis were carried out to estimate the diagnostic potential of HIF alpha isomers in different cancers, as well as the survival rate of patients with the varying expression levels of HIF alpha factors. Results: The expression status of HIF3A was notably less in all cancer samples in contrast to their adjacent normal tissues. The expression degree of HIF1A varied among distinct types of cancer and the expression degree of HIF2A was lower in nearly all types of cancers. The expression level of HIF alpha isomers did not significantly correlate with different sizes of tumor samples and stages of different tumor tissue samples. HIF3A had very weak diagnostic potential, while HIF2A had better diagnostic potential in most types of cancers compared to HIF1A. Patients who had a higher level of HIF3A had better survival, while the higher expression levels of HIF1A and HIF2A were associated with worse survival in many types of cancers. Conclusion: Our study shows the heterogenous expression pattern of HIF alpha subunits in distinctive kinds of cancers and the influence of HIF3A expression level in the survival of patients with varying types of cancers.