Comprehensively analyzing the genetic alterations, and identifying key genes in ovarian cancer.

Though significant improvements have been made in the treatment methods for ovarian cancer (OC), the prognosis for OC patients is still poor. Exploring hub genes associated with the development of OC and utilizing them as appropriate potential biomarkers or therapeutic targets is highly valuable. In this study, the differentially expressed genes (DEGs) were identified from an independent GSE69428 Gene Expression Omnibus (GEO) dataset between OC and control samples. The DEGs were processed to construct the protein-protein interaction (PPI) network using STRING. Later, hub genes were identified through Cytohubba analysis of the Cytoscape. Expression and survival profiling of the hub genes were validated using GEPIA, OncoDB, and GENT2. For exploring promoter methylation levels and genetic alterations in hub genes, MEXPRESS and cBioPortal were utilized, respectively. Moreover, DAVID, HPA, TIMER, CancerSEA, ENCORI, DrugBank, and GSCAlite were used for gene enrichment analysis, subcellular localization analysis, immune cell infiltration analysis, exploring correlations between hub genes and different diverse states, lncRNA-miRNA-mRNA co-regulatory network analysis, predicting hub gene-associated drugs, and conducting drug sensitivity analysis, respectively. In total, 8947 DEGs were found between OC and normal samples in GSE69428. After STRING and Cytohubba analysis, 4 hub genes including TTK (TTK Protein Kinase), (BUB1 mitotic checkpoint serine/threonine kinase B) BUB1B, (Nucleolar and spindle-associated protein 1) NUSAP1, and (ZW10 interacting kinetochore protein) ZWINT were selected as the hub genes. Further, it was validated that these 4 hub genes were significantly up-regulated in OC samples compared to normal controls, but overexpression of these genes was not associated with overall survival (OS). However, genetic alterations in those genes were found to be linked with OS and disease-free (DFS) survival. Moreover, this study also revealed some novel links between TTK, BUB1B, NUSAP1, and ZWINT overexpression and promoter methylation status, immune cell infiltration, miRNAs, gene enrichment terms, and various chemotherapeutic drugs. Four hub genes, including TTK, BUB1B, NUSAP1, and ZWINT, were revealed as tumor-promotive factors in OC, having the potential to be utilized as novel biomarkers and therapeutic targets for OC management.

Screening a novel six critical gene-based system of diagnostic and prognostic biomarkers in prostate adenocarcinoma patients with different clinical variables.

The mechanisms behind prostate adenocarcinoma (PRAD) pathogenicity remain to be understood due to tumor heterogeneity. In the current study, we identified by microarray technology six eligible real hub genes from already identified hub genes through a systematic in silico approach that could be useful to lower the heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment. For this purpose, microarray technology-based, already-identified PRAD-associated hub genes were initially explored through extensive literature mining; then, a protein-protein interaction (PPI) network construction of those hub genes and its analysis helped us to identify six most critical genes (real hub genes). Various online available expression databases were then used to explore the tumor driving, diagnostic, and prognostic roles of real hub genes in PRAD patients with different clinicopathologic variables. In total, 124 hub genes were extracted from the literature, and among those genes, six, including CDC20, HMMR, AURKA, CDK1, ASF1B, and CCNB1 were identified as real hub genes by the degree method. Further expression analysis revealed the significant up-regulation of real hub genes in PRAD patients of different races, age groups, and nodal metastasis status relative to controls. Moreover, through correlational analyses, different valuable correlations between treal hub genes expression and different other data (promoter methylation status, genetic alterations, overall survival (OS), tumor purity, CD4+ T, CD8+ T immune cells infiltration, and different other mutant genes and a few more) across PRAD samples were also documented. Ultimately, from this study, a few important transcription factors (TFS), miRNAs, and chemotherapeutic drugs showing a great therapeutic potential were also identified. In conclusion, we have discovered a set of six real hub genes that might be utilized as new biomarkers for lowering heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment.

Decoding signal transducer and activator of transcription 1 across various cancers through data mining and integrative analysis.

OBJECTIVES: Using different online available databases and Bioinformatics tools, we extensively studied the role STAT1 across different cancers. METHODS: STAT1 mRNA, protein expression, and promoter methylation were analyzed and validated using UALCAN, GENT2, Human Protein Atlas (HPA), and MEXPRESS. Furthermore, the potential prognostic values were evaluated through KM plotter. Then, cBioPortal was utilized to examine the STAT1-related genetic mutations, while pathway enrichment analysis was performed using DAVID. To identify STAT1 targeted microRNAs (miRNAs) and transcription factors (TFs) we used Enricher. Moreover, a correlational analysis between STAT1 expression tumor purity and CD8+ T immune cells and a gene-drug interaction network analysis was performed using TIMER, CTD, and Cytoscape. RESULTS: In 23 major human cancers, STAT1 expression was notably up-regulated relative to corresponding controls. As well, the elevated expression of STAT1 was exclusively found to be associated with the reduced overall survival (OS) of Esophageal Carcinoma (ESCA), Kidney Renal Clear Cell Carcinoma (KIRC), and Lung adenocarcinoma (LUAD) patients. This implies that STAT1 plays a significant role in the development and progression of these three cancers. Further pathway analysis indicated that STAT1 enriched genes were involved in six critical pathways, while a few interesting correlations were also documented between STAT1 expression and promoter methylation level, tumor purity, CD8+ T immune cells infiltration, and genetic alteration. In addition, we have also predicted a few miRNAs, TFs, and chemotherapeutic drugs that could regulate the STAT1 expression. CONCLUSION: The current study revealed the shared oncogenic, diagnostic, and prognostic role of STAT1 in ESCA, KIRC, and LUAD.