Advanced stage, high-grade primary tumor ovarian cancer: a multi-omics dissection and biomarker prediction process.

Ovarian cancer (OC) incidence and mortality rates continue to escalate globally. Early detection of OC is challenging due to extensive metastases and the ambiguity of biomarkers in advanced High-Grade Primary Tumors (HGPTs). In the present study, we conducted an in-depth in silico analysis in OC cell lines using the Gene Expression Omnibus (GEO) microarray dataset with 53 HGPT and 10 normal samples. Differentially-Expressed Genes (DEGs) were also identified by GEO2r. A variety of analyses, including gene set enrichment analysis (GSEA), ChIP enrichment analysis (ChEA), eXpression2Kinases (X2K) and Human Protein Atlas (HPA), elucidated signaling pathways, transcription factors (TFs), kinases, and proteome, respectively. Protein-Protein Interaction (PPI) networks were generated using STRING and Cytoscape, in which co-expression and hub genes were pinpointed by the cytoHubba plug-in. Validity of DEG analysis was achieved via Gene Expression Profiling Interactive Analysis (GEPIA). Of note, KIAA0101, RAD51AP1, FAM83D, CEP55, PRC1, CKS2, CDCA5, NUSAP1, ECT2, and TRIP13 were found as top 10 hub genes; SIN3A, VDR, TCF7L2, NFYA, and FOXM1 were detected as predominant TFs in HGPTs; CEP55, PRC1, CKS2, CDCA5, and NUSAP1 were identified as potential biomarkers from hub gene clustering. Further analysis indicated hsa-miR-215-5p, hsa-miR-193b-3p, and hsa-miR-192-5p as key miRNAs targeting HGPT genes. Collectively, our findings spotlighted HGPT-associated genes, TFs, miRNAs, and pathways as prospective biomarkers, offering new avenues for OC diagnostic and therapeutic approaches.

In silico prediction and in vitro validation of the effect of pH on adhesive behaviour of the fused CsgA-MFP3 protein.

Recombinant production of mussel foot proteins among marine-inspired proteinaceous adhesive materials has been attracted high attention for medical applications, due to their exceptional versatility potential of hierarchically arranged nanostructures. Various biochemical and proteinous factors such as amyloid CsgA curli protein have been used as a synergistic factor to enhance the constancy of obtained bio-adhesion but their mechanistic interactions have not yet been deeply investigated widely in different pH conditions. To this end, the present study has first sought to assess molecular simulation and prediction by using RosettaFold to predict the 3-dimensional structure of the fused CsgA subunit and the MFP3 protein followed by in vitro verification. It was developed an ensemble of quantitative structure-activity relationship models relying on simulations according to the surface area and molecular weight values of the fused proteins in acidic to basic situations using PlayMolecule (protein preparation app for MD simulations) online databases followed by molecular dynamic simulation at different pHs. It was found that acidic conditions positively affect adhesive strength throughout the chimeric structure based on comparative structure-based analyses along with those obtained in prevailing literature. Atomic force microscopy analysis was confirmed obtained in silico data which showed enhanced adhesive properties of fused protein after self-assembly in low pH conditions. In conclusion, the augmented model for reactivity predictions not only unravels the performance and explain ability of the adhesive proteins but in turn paves the way for the decision-making process for chimeric subunits modifications needed for future industrial production.