Abstract
One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.
Highlights
The worldwide estimate of people diagnosed with cancer was 18.1 million in 20171 and it is predicted by the World Health Organization (WHO) to be 27 million new cases worldwide by 2030
The gene symbols are transformed into UniprotKB accession numbers (GS2UP) to perform the subtraction of the control RNA sequencing (RNA-seq) expression data from that of the tumor (DEGL)
The connection count at each vertex is necessary for computing the Shannon entropy of the tumor subnetwork of up-regulated genes by the Entropy Calculation (ETP) script
Summary
The worldwide estimate of people diagnosed with cancer was 18.1 million in 20171 and it is predicted by the World Health Organization (WHO) to be 27 million new cases worldwide by 2030. The well-known heterogeneity of breast cancer has justified the genomic study of tumors on a large scale in search for tumor subtypes that could allow a better understanding of the tumor biology and could serve as support for the establishment of genetic signatures, which, when validated in clinical trials, could pave the way for an increasingly specific and more precise treatment than the clinical parameters currently in use It is a more in-depth knowledge of tumor biology that has allowed for greater individualization of available treatments and has made it possible to overcome the relapse and resistance eventually observed with traditional treatments (Naito and Urasaki, 2018). Clinical experience has shown that knowledge of the individual characteristics of each tumor may contribute to better therapeutic results with less toxicity
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
