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. 2021 Feb 18:12:624259.
doi: 10.3389/fgene.2021.624259. eCollection 2021.

Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy

Jorge Guerra Pires  1 , Gilberto Ferreira da Silva  1 , Thomas Weyssow  2 , Alessandra Jordano Conforte  1   3 , Dante Pagnoncelli  4 , Fabricio Alves Barbosa da Silva  3 , Nicolas Carels  1
Affiliations

Galaxy and MEAN Stack to Create a User-Friendly Workflow for the Rational Optimization of Cancer Chemotherapy

Jorge Guerra Pires et al. Front Genet. .

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.

Keywords: Galaxy; MEAN stack; Shannon entropy; angular; personalized medicine; protein–protein network; systems biology; translational oncology.

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Conflict of interest statement

The intellectual property of this research is protected by the Brazilian patent number BR1020150308191. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Process of Galaxy workflow validation.
FIGURE 2
FIGURE 2
Workflow for the validation of the correlation between the entropy of the subnetworks of up-regulated genes from different tumors and their respective 5-years OS. (A) TCGA. (B) GDC.
FIGURE 3
FIGURE 3
Correlation of subnetwork entropies vs. 5-years OS for p = 0.975. (A) GDC RPKMupper. (B) GDC RPKMupper + LogNorm. (C) GDC RPKMupper + Log2. (D) GDC FPKM-UQ.
FIGURE 4
FIGURE 4
Critical value calculation (red dot line) by CVC script in TCGA (A–C) and GDC (D–F) in LUSC (TCGA-22-4593 sample). (A,D) Histogram of observed differential gene expression distribution (tumor-control) of genes. (B,E) Function of density of probability. (C,F) Function of cumulated probability. The critical values were 3,633.8 and 17,042.9 for TCGA and GDC, respectively.
FIGURE 5
FIGURE 5
Workflow for collections of BLASTx output processing.
FIGURE 6
FIGURE 6
Workflow for top-n most relevant hub target diagnosis in up-regulated genes of solid tumors. (A) TCGA. (B) GDC.
FIGURE 7
FIGURE 7
Scaling of pipeline from Figure 2B (entropy) and Figure 6B (PTTCS) using GDC read counts (see data in Supplementary Table 1). (A) Linear scaling for 5–45 patients in LUSC and PRAD as well as 15 and 25 patients for STAD, LIHC, THCA, and KIRC. (B) Statistical analysis of scaling for high entropy (H) cancer (LUSC, LIHC, STAD) and low entropy (L) cancer (KIRC, PRAD, THCA) for entropy (gray) and PTTCS (white) pipelines. ∗∗Significant at α ≤ 0.01 for k = 4 degrees of freedom. The horizontal bars are for the standard error of the mean (SEM).
FIGURE 8
FIGURE 8
Dashboard on (A) a desktop and (B) an extra small devices (Galaxy S5).
FIGURE 9
FIGURE 9
Form header. A user is warned when leaving without saving the form. The patient password is encrypted and kept on the server using a specialized type of file; nonetheless, users can choose their own passwords.
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