The Immune-related ceRNA Network in Prognosis of Cervical Cancer.

BACKGROUND: Immunotherapy is gaining attention and it is being included as one of the treatment strategies for cancer patients. However, the molecular mechanisms of immune-related genes and their affinity for cervical cancer progression remain unclear. In this study, we have developed an immune-related competing endogenous RNA [ceRNA] network and assessed the tumour infiltrating immune cells towards the prognosis of cervical cancer. METHODS: Differential RNA expression pattern between stages I and II-IV of cervical cancer patients from The Cancer Genome Atlas [TCGA] was analyzed. Immune-related ceRNA network based on the immune gene signatures were retrieved and their targets were predicted using miRwalk 3.0. CIBERSORT was employed to identify the immune cell types based on their respective transcripts. The prognostic significance of RNAs in the ceRNA network and immune cell subsets was analyzed. RESULTS: Significant differences in 22 long non-coding RNAs [lncRNAs], 15 microRNAs [miRNAs], and 252 messenger RNAs [mRNAs] between stages I and II-IV of cervical cancer were observed. Further, we shortlisted the 49 immune-related mRNAs based on immune gene signature and predicted their target miRNAs and lncRNAs. A potential ceRNA network of 4 lncRNAs, 10 miRNAs, and 11 mRNAs had a strong correlation for prognosis. Out of 11 protein-coding immune mRNAs, IRF4 and AZGP1 had high degrees of interaction. In addition, the evaluation of immune cell subsets showed increased infiltration of M1 macrophages had better survival outcome. CONCLUSIONS: We have identified an immune-related ceRNA network based on differentially expressed transcripts between stages I and II-IV which may help predict the prognosis of cervical cancer.

Repurposing Drugs by In Silico Methods to Target BCR Kinase Domain in Chronic Myeloid Leukemia.

BACKGROUND: Targeted therapy in the form of highly selective tyrosine kinase inhibitors (TKIs) has transformed the treatment of chronic myeloid leukemia (CML). However, mutations in the kinase domain contribute to drug resistance against TKIs which compromises the treatment response. Our aim is to explore regions outside the BCR-ABL oncoprotein to identify potential therapeutic targets to curb drug resistance by targeting growth factor receptor-bound protein-2 (Grb-2) which binds to BCR-ABL at the phosphorylated tyrosine (Y177) thereby activating the Ras and PI3K/AKT signaling pathway. METHODS: We have used in silico methods to repurpose drugs for identifying their potential to inhibit the binding of Grb-2 with Y177 by occupying the active binding site of the BCR domain. RESULTS: Differentially expressed genes from GEO dataset were found to be associated with hematopoietic cell lineage, NK cell-mediated cytotoxicity, NF-κB and chemokine signaling, cytokine-cytokine receptor interaction, histidine metabolism and transcriptional misregulation in cancer. The fold recognition method of SPARKS-X tool was used to model the BCR domain (Z-score = 8.21). Connectivity Map generated a drug list based on the gene expression profile, which were docked with BCR. Schrodinger XP glide docking identified Diphosphopyridine nucleotide, Hesperidin, Butirosin, Ovoflavin, and Nor-dihydroguaiaretic acid to show strong interaction in close proximity to the active binding pocket containing Y177 of the target protein and was further validated using iGEMDOCK and Parallelized Open Babel and AutoDock suite Pipeline (POAP). CONCLUSION: Our study not only extends our current knowledge about repurposing drugs for newer indications but also provides a route towards combinatorial therapy with standard drugs used for CML treatment. However, the efficacy of these repurposed drugs needs to be further investigated using in vitro and in vivo studies.
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In silico and in vitro screening of small molecule Inhibitors against SYT-SSX1 fusion protein in synovial sarcoma.

Synovial sarcoma (SS) is characterized by a tumour specific chromosomal translocation t(X;18) (p11;q11) which results in the formation of SYT-SSX1 fusion protein. This fusion protein represents a clear therapeutic target and molecules specifically targeting SYT-SSX1 fusion protein are currently not available. In this study, SYT-SSX1 fusion protein sequence was retrieved from Uniprot and 3D structure was generated using I-TASSER modeling program. A structure based computational screening approach has been employed using Glide docking software to identify potential SYT-SSX1 small molecule inhibitors that bind to the junction region of the fusion protein. The obtained inhibitors were further filtered based on the docking score and ADME/T properties. Ten best fit compounds were chosen for in vitro studies. The anti-proliferative activities of these 10 compounds were screened in Yamato, ASKA (carries SYT-SSX1 fusion protein) and other sarcoma cell lines such as A673, 143B to understand the specificity of inhibition of the chosen compounds. The in vitro activity was compared against HEK293 cell lines. The compound 5-fluoro-3-(1-phenyl-1H-tetraazol-5-yl)-1H-indole (FPTI) was found to be selectively cytotoxic in synovial sarcoma cell lines (Yamato and ASKA) and this compound also showed insignificant anti proliferative activity on other cell lines. Further, target gene expression study confirmed that FPTI treatment down-regulated SYT-SSX1 and modulated its downstream target genes. Cell cycle analysis revealed the involvement of an apoptotic mechanism of cell death. Further experimental validations may elucidate the therapeutic potentials of FPTI against SYT-SSX1 fusion protein.

Molecular modeling and docking of small molecule inhibitors against NEK2.

Aberrant expression of NEK2 (NIMA-related kinase 2) is indicated in a wide variety of human cancers. NEK2 is highly correlated to multi drug resistance by activating drug efflux activity. Identification of new small molecule inhibitors targeted against NEK2 therefore, facilitates to increase drug sensitivity of cancer cells, by stabilizing drug influx and minimizes the dose of therapeutic drug. Our work investigates to screen for optimal small molecule inhibitors against NEK2. In this study, we used a computational approach by modeling NEK2 protein using I-TASSER (Iterative Threading ASSEmbly Refinement) software. The modeled structure was subjected to protein preparation wizard; to add hydrogens and to optimize the protonation states of His, Gln and Asn residues. Active site of the modeled protein was identified using SiteMap tool of Schrodinger package. We further carried out docking studies by means of Glide, with various ligands downloaded from EDULISS database. Based on glide score, potential ligands were screened and their interaction with NEK2 was identified. The best hits were further screened for Lipinski's rule for drug-likeliness, bioactivity scoring and ADME properties. Thus, we report two (didemethylchlorpromazine and 2-[5-fluoro-1Hindol- 3-yl] propan-1-amine) compounds that have successfully satisfied all in silico parameters, necessitating further in vitro and in vivo studies.