Calcium/calmodulin-dependent protein kinase IV (CAMKIV) is associated with many diseases including cancer and neurodegenerative disorders and thus being considered as a potential drug target. Here, we have employed the knowledge of three-dimensional structure of CAMKIV to identify new inhibitors for possible therapeutic intervention. We have employed virtual high throughput screening of 12,500 natural compounds of Zinc database to screen the best possible inhibitors of CAMKIV. Subsequently, 40 compounds which showed significant docking scores (-11.6 to -10.0 kcal/mol) were selected and further filtered through Lipinski rule and drug likeness parameter to get best inhibitors of CAMKIV. Docking results are indicating that ligands are binding to the hydrophobic cavity of the kinase domain of CAMKIV and forming a significant number of non-covalent interactions. Four compounds, ZINC02098378, ZINC12866674, ZINC04293413, and ZINC13403020, showing excellent binding affinity and drug likeness were subjected to molecular dynamics simulation to evaluate their mechanism of interaction and stability of protein-ligand complex. Our observations clearly suggesting that these selected ligands may be further employed for therapeutic intervention to address CAMKIV associated diseases. Communicated by Ramaswamy H. Sarma.

Background Among many gynecological malignancies ovarian cancer is the most prominent and leading cause of female mortality worldwide. Despite extensive research, the underlying cause of disease progression and pathology is still unknown. In the progression of ovarian cancer different non-coding RNAs have been recognized as important regulators. The biology of ovarian cancer which includes cancer initiation, progression, and dissemination is found to be regulated by different ncRNA. Clinically ncRNA shows high prognostic and diagnostic importance. Results In this review, we prioritize the role of different non-coding RNA and their perspective in diagnosis as potential biomarkers in the case of ovarian cancer. Summary of some of the few miRNAs involved in epithelial ovarian cancer their expression and clinical features are being provided in the table. Also, in cancer cell proliferation, apoptosis, invasion, and migration abnormal expression of piRNAs are emerging as a crucial regulator hence the role of few piRNAs is being given. Both tRFs and tiRNAs play important roles in tumorigenesis and are promising diagnostic biomarkers and therapeutic targets for cancer. lncRNA has shown a leading role in malignant transformation and potential therapeutic value in ovarian cancer therapy. Conclusions Hence in this review we demonstrated the role of different ncRNA that play an important role in serving strong potential as a therapeutic approach for the treatment of ovarian cancer.

Ovarian cancer is the eighth-most common cancer in women and has the highest rate of death among all gynecological malignancies in the Western world. Increasing evidence shows that miRNAs are connected to the progression of ovarian cancer. In the current study, we focus on the identification of miRNA and its associated genes that are responsible for the early prognosis of patients with ovarian cancer. The microarray dataset GSE119055 used in this study was retrieved via the publicly available GEO database by NCBI for the analysis of DEGs. The miRNA GSE119055 dataset includes six ovarian carcinoma samples along with three healthy/primary samples. In our study, DEM analysis of ovarian carcinoma and healthy subjects was performed using R Software to transform and normalize all transcriptomic data along with packages from Bioconductor. Results: We identified miRNA and its associated hub genes from the samples of ovarian cancer. We discovered the top five upregulated miRNAs (hsa-miR-130b-3p, hsa-miR-18a-5p, hsa-miR-182-5p, hsa-miR-187-3p, and hsa-miR-378a-3p) and the top five downregulated miRNAs (hsa-miR-501-3p, hsa-miR-4324, hsa-miR-500a-3p, hsa-miR-1271-5p, and hsa-miR-660-5p) from the network and their associated genes, which include seven common genes (SCN2A, BCL2, MAF, ZNF532, CADM1, ELAVL2, and ESRRG) that were considered hub genes for the downregulated network. Similarly, for upregulated miRNAs we found two hub genes (PRKACB and TAOK1).

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