scholarly journals Chitosan nanoparticle-mediated effect of antimiRNA-324-5p on decreasing the ovarian cancer cell proliferation by regulation of GLI1 expression

Bioimpacts ◽  
2021 ◽  
Author(s):  
Ysrafil Ysrafil ◽  
Indwiani Astuti
Keyword(s):  
Ovarian Cancer ◽  
Cell Proliferation ◽  
Mtt Assay ◽  
In Silico ◽  
Quantitative Polymerase Chain Reaction ◽  
Chitosan Nanoparticles ◽  
In Silico Analysis ◽  
Skov3 Cell ◽  
Gli1 Expression ◽  
Silico Analysis

Introduction: MicroRNAs (miRNAs) are short-sequence RNAs that regulate gene expression by targeting messenger RNAs (mRNAs). Recent studies reveal that miRNA-324-5p plays an important role in worsening the ovarian cancer prognosis when the expression is very high. This study aimed to develop a miRNA targeted therapy by targeting the miRNA-324-5p function as a miRNA-324-5p inhibitor. Methods: Chitosan nanoparticles were used for antimiRNA-324-5p delivery into SKOV3 cell lines formulated by ionic gelation method. Antiproliferative effect of CS-NPs-antimiRNA was assessed by the MTT Assay. A mechanism study assessed the anticancer effect of the formula. In silico analysis used miRTar.Human and StarmiRDB combined with Genecard to predict the target genes of antimiR. Hawkdock web server was used to analyze protein-protein interactions that were further validated by quantitative polymerase chain reaction (qPCR). Results: The results of qPCR analysis showed endogenous miRNA-324-5p decreased after 24-hour transfection of antagonist miRNA. Furthermore, the MTT assay results showed that antimiRNA was able to inhibit SKOV3 cell proliferation (80 nM 68.13%, P<0.05). In silico analysis found miRNA-324-5p can regulate MEN1 and indirectly repress Gli1 mRNA. Validation results confirmed antimiR can decrease GLI1 mRNA expression. Conclusion: Our results showed antimiRNA-324-5p can act as a microRNA-based therapy to inhibit ovarian cancer proliferation by the reduction of GLI1 expression.

Targeting CD44, ABCG2 and CD133 markers using aptamers: in silico analysis of CD133 extracellular domain 2 and its aptamer

RSC Advances ◽  
2016 ◽  
Vol 6 (38) ◽  
pp. 32115-32123 ◽  
Author(s):  
Nithya Subramanian ◽  
Balachandran Akilandeswari ◽  
Anjali Bhutra ◽  
Mohamed Alameen ◽  
Umashankar Vetrivel ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
In Silico ◽  
In Silico Analysis ◽  
Extracellular Domain ◽  
Structural Interactions ◽  
Silico Analysis

Truncated CSC marker aptamers penetrate tumor spheres and inhibits cell proliferation; a bioinformatics approach to decipher their structural interactions.

scholarly journals mRNA-Expression of KRT5 and KRT20 Defines Distinct Prognostic Subgroups of Muscle-Invasive Urothelial Bladder Cancer Correlating with Histological Variants

2018 ◽  
Vol 19 (11) ◽  
pp. 3396 ◽  
Author(s):  
Markus Eckstein ◽  
Ralph Wirtz ◽  
Matthias Gross-Weege ◽  
Johannes Breyer ◽  
Wolfgang Otto ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bladder Cancer ◽  
In Silico ◽  
Quantitative Polymerase Chain Reaction ◽  
In Silico Analysis ◽  
Disease Specific Survival ◽  
Substantial Impact ◽  
Muscle Invasive ◽  
Silico Analysis ◽  
Disease Specific

Recently, muscle-invasive bladder cancer (MIBC) has been subclassified by gene expression profiling, with a substantial impact on therapy response and patient outcome. We tested whether these complex molecular subtypes of MIBC can be determined by mRNA detection of keratin 5 (KRT5) and keratin 20 (KRT20). Reverse transcriptase quantitative polymerase chain reaction (RT-qPCR) was applied to quantify gene expression of KRT5 and KRT20 using TaqMan®-based assays in 122 curatively treated MIBC patients (median age 68.0 years). Furthermore, in silico analysis of the MD Anderson Cancer Center (MDACC) cohort (GSE48277 + GSE47993) was performed. High expression of KRT5 and low expression of KRT20 were associated with significantly improved recurrence-free survival (RFS) and disease-specific survival disease specific survival (DSS: 5-year DSS for KRT5 high: 58%; 5-year DSS for KRT20 high: 29%). KRT5 and KRT20 were associated with rates of lymphovascular invasion and lymphonodal metastasis. The combination of KRT5 and KRT20 allowed identification of patients with a very poor prognosis (KRT20+/KRT5−, 5-year DSS 0%, p < 0.0001). In silico analysis of the independent MDACC cohorts revealed congruent results (5-year DSS for KRT20 low vs. high: 84% vs. 40%, p = 0.042). High KRT20-expressing tumors as well as KRT20+/KRT− tumors were significantly enriched with aggressive urothelial carcinoma variants (micropapillary, plasmacytoid, nested).

A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

2020 ◽  
Vol 47 (6) ◽  
pp. 398-408
Author(s):  
Sonam Tulsyan ◽  
Showket Hussain ◽  
Balraj Mittal ◽  
Sundeep Singh Saluja ◽  
Pranay Tanwar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gallbladder Carcinoma ◽  
In Silico ◽  
In Silico Analysis ◽  
Transcriptomic Profile ◽  
Silico Analysis

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

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