scholarly journals In silico analysis of the prognostic value of FAS mRNA in malignancies

2020 ◽  
Vol 11 (3) ◽  
pp. 542-550
Author(s):  
Zhigang Chen ◽  
Jun Wu ◽  
Hailin Xu ◽  
Xiuyan Yu ◽  
Ke Wang
2021 ◽  
Vol 11 (3) ◽  
pp. 232
Author(s):  
Antonio Fernando Leis-Filho ◽  
Patrícia de Faria Lainetti ◽  
Mayara Simão Franzoni ◽  
Chiara Palmieri ◽  
Priscila Emiko Kobayshi ◽  
...  

CD24 is a cell surface molecule anchored by glycosyl-phosphatidyl-inositol and expressed by different human cancers, including prostate cancer (PC). Some studies have demonstrated that CD24 expression is associated with poor patient outcome; however, few studies have investigated CD24 expression in spontaneous animal models of human PC, such as canine PC. This study aimed to evaluate the expression of CD24 in human PC using the in silico analysis of the data obtained from The Cancer Genome Atlas (TCGA) and comparing it with the previously published prostatic canine transcriptome data. In addition, CD24 expression was confirmed by immunohistochemistry in an independent cohort of canine prostatic samples and its prognostic significance assessed. The systematic review identified 10 publications fitting with the inclusion criteria of this study. Of the 10 manuscripts, 5 demonstrated a direct correlation between CD24 overexpression and patient prognoses. CD24 expression was also associated with PSA relapse (2/5) and tumor progression (1/5). However, the in silico analysis did not validate CD24 as a prognostic factor of human PC. Regarding canine PC, 10 out of 30 normal prostates and 27 out of 40 PC samples were positive for CD24. As in humans, there was no association with overall survival. Overall, our results demonstrated a significant CD24 overexpression in human and canine prostate cancer, although its prognostic value may be questionable. However, tumors overexpressing CD24 may be a reliable model for new target therapies and dogs could be used of a unique preclinical model for these studies.


2020 ◽  
Vol 47 (6) ◽  
pp. 398-408
Author(s):  
Sonam Tulsyan ◽  
Showket Hussain ◽  
Balraj Mittal ◽  
Sundeep Singh Saluja ◽  
Pranay Tanwar ◽  
...  

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki

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.


2013 ◽  
Vol 9 (4) ◽  
pp. 608-616 ◽  
Author(s):  
Zaheer Ul-Haq ◽  
Saman Usmani ◽  
Uzma Mahmood ◽  
Mariya al-Rashida ◽  
Ghulam Abbas

2019 ◽  
Vol 13 (2) ◽  
pp. 159-170 ◽  
Author(s):  
Vishal Ahuja ◽  
Aashima Sharma ◽  
Ranju Kumari Rathour ◽  
Vaishali Sharma ◽  
Nidhi Rana ◽  
...  

Background: Lignocellulosic residues generated by various anthropogenic activities can be a potential raw material for many commercial products such as biofuels, organic acids and nutraceuticals including xylitol. Xylitol is a low-calorie nutritive sweetener for diabetic patients. Microbial production of xylitol can be helpful in overcoming the drawbacks of traditional chemical production process and lowring cost of production. Objective: Designing efficient production process needs the characterization of required enzyme/s. Hence current work was focused on in-vitro and in-silico characterization of xylose reductase from Emericella nidulans. Methods: Xylose reductase from one of the hyper-producer isolates, Emericella nidulans Xlt-11 was used for in-vitro characterization. For in-silico characterization, XR sequence (Accession No: Q5BGA7) was used. Results: Xylose reductase from various microorganisms has been studied but the quest for better enzymes, their stability at higher temperature and pH still continues. Xylose reductase from Emericella nidulans Xlt-11 was found NADH dependent and utilizes xylose as its sole substrate for xylitol production. In comparison to whole cells, enzyme exhibited higher enzyme activity at lower cofactor concentration and could tolerate higher substrate concentration. Thermal deactivation profile showed that whole cell catalysts were more stable than enzyme at higher temperature. In-silico analysis of XR sequence from Emericella nidulans (Accession No: Q5BGA7) suggested that the structure was dominated by random coiling. Enzyme sequences have conserved active site with net negative charge and PI value in acidic pH range. Conclusion: Current investigation supported the enzyme’s specific application i.e. bioconversion of xylose to xylitol due to its higher selectivity. In-silico analysis may provide significant structural and physiological information for modifications and improved stability.


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