Identification of Novel Genes and Pathways of Ovarian Cancer Using a Comprehensive Bioinformatic Framework.

Ovarian cancer (OC) is a significant contributor to gynecological cancer-related deaths worldwide, with a high mortality rate. Despite several advances in understanding the pathogenesis of OC, the molecular mechanisms underlying its development and prognosis remain poorly understood. Therefore, the current research study aimed to identify hub genes involved in the pathogenesis of OC that could serve as selective diagnostic and therapeutic targets. To achieve this, the dataset GEO2R was used to retrieve differentially expressed genes. The study identified a total of five genes (CDKN1A, DKK1, CYP1B1, NTS, and GDF15) that were differentially expressed in OC. Subsequently, a network analysis was performed using the STRING database, followed by the construction of a network using Cytoscape. The network analyzer tool in Cytoscape predicted 276 upregulated and 269 downregulated genes. Furthermore, KEGG analysis was conducted to identify different pathways related to OC. Subsequently, survival analysis was performed to validate gene expression alterations and predict hub genes, using a p-value of 0.05 as a threshold. Four genes (CDKN1A, DKK1, CYP1B1, and NTS) were predicted as significant hub genes, while one gene (GDF15) was predicted as non-significant. The adjusted P values of said predicted genes are 2.85E - 07, 5.49E - 06, 4.28E - 07, 1.43E - 07, and 3.70E - 07 for CDKN1A, DKK1, NTS, GDF15, and CYP1B1 respectively; additionally 6.08, 5.76, 5.74, 5.01, and 4.9 LogFc values of the said genes were predicted in GEO data set. In a boxplot analysis, the expression of these genes was analyzed in normal and tumor cells. The study found that three genes were highly expressed in tumor cells, while two genes (CDKN1A and DKK1) were more elevated in normal cells. According to the boxplot analysis for CDKN1A, 50% of tumor cells ranged between approx 3.8 and 5, while 50% of normal cells ranged between approx 6.9 and 7.9, which is greater than tumor cells. This shows that in normal cells, the CYP1B1 has a high expression level according to the GEPIA boxplot; addtionally the boxplot for DKK1 indicated that 50% of tumor cells ranged between approx 0 and 0.5, which was less than that of normal cells which ranged between approx 0.3 and 0.9. It shows that DKK1 is highly expressed in normal genes. Overall, the current study provides novel insights into the molecular mechanisms underlying OC. The identified hub genes and drug candidate targets could potentially serve as alternative diagnostic and therapeutic options for OC patients. Further research is needed to investigate the clinical significance of these findings and develop effective interventions that can improve the prognosis of patients with OC.

A novel therapeutic approach to prevent Helicobacter pylori induced gastric cancer using networking biology, molecular docking, and simulation approaches.

Helicobacter pylori infects 50% of the world population and in 80% of cases, the infection progresses to the point where an ulcer develops leading to gastric cancer (GC). This study aimed to prevent GC by predicting Hub genes that are inducing GC. Furthermore, the study objective was to screen inhibitory molecules that block the function of predicted genes through several biophysical approaches. These proteins, such as Mucin 4 (MUC4) and Baculoviral IAP repeat containing 3 (BIRC3), had LogFC values of 2.28 and 3.39, respectively, and were found to be substantially expressed in those who had H. pylori infection. The MUC4 and BIRC3 inhibit apoptosis of infected cells and promote cancerous cell survival. The proteins were examined for their Physico-chemical characteristics, 3D structure and secondary structure analysis, solvent assessable surface area (SASA), active site identification, and network analysis. The MUC4 and BIRC3 expression was inhibited by docking eighty different compounds collected from the ZINC database. Fifty-seven compounds were successfully docked into the active site resulting in the lowest binding energy scores. The ZINC585267910 and ZINC585268691 compounds showed the lowest binding energy of -8.5 kcal/mol for MUC4 and -7.1 kcal/mol for BIRC3, respectively, and were considered best-docked solutions for molecular dynamics simulations. The mean root mean square deviation (RMSD) value for the ZINC585267910-MUC4 complex was 0.86 Å and the ZINC585268691-BIRC3 complex was 1.01 Å. The net MM/GBSA energy value of the ZINC585267910-MUC4 complex estimated was -46.84 kcal/mol and that of the ZINC585268691-BIRC3 complex was -44.84 kcal/mol. In a nutshell, the compounds might be investigated further as an inhibitor of the said proteins to stop the progress of GC induced by H. pylori.Communicated by Ramaswamy H. Sarma.

Identification of potential drug molecules against fibroblast growth factor receptor 3 (FGFR3) by multi-stage computational-biophysics correlate.

The fibroblast growth factor receptor 3 (FGFR3) is warranted as a promising therapeutic target in bladder cancer as it is described in 75% of papillary bladder tumors. Considering this, the present study was conducted to use different approaches of computer-aided drug discovery (CADD) to identify the best binding compounds against the active pocket of FGFR3. Compared to control pyrimidine derivative, the study identified three promising lead structures; BDC_24037121, BDC_21200852, and BDC_21206757 with binding energy value of -14.80 kcal/mol, -12.22 kcal/mol, and -11.67 kcal/mol, respectively. The control molecule binding energy score was -9.85 kcal/mol. The compounds achieved deep pocket binding and produced balanced interactions of hydrogen bonds and van der Waals. The FGFR3 enzyme residues such as Leu478, Lys508, Glu556, Asn562, Asn622, and Asp635. The molecular dynamic (MD) simulation studies additionally validated the docked conformation stability with respect to FGFR3 with a mean root mean square deviation (RMSD) value of < 3 Å. The root mean square fluctuation (RMSF) complements the complexes structural stability and the residues showed less fluctuation in the presence of compounds. The Poisson-Boltzmann or generalized Born and surface area continuum solvation (MM/PBSA and MM/GBSA) methods revalidated compounds better binding and highlighted van der Waals energy to dominate the overall net energy. The docked stability was additionally confirmed by WaterSwap and AMBER normal mode entropy energy analyses. In a nutshell, the compounds shortlisted in this study are promising in term of theoretical binding affinity for FGFR3 but experimental validation is needed.Communicated by Ramaswamy H. Sarma.