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Cited by 6Computational Drug Discovery MethodsGastric Cancer Management and OutcomesGene expression and cancer classification

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A Shortcut from Genome to Drug: The Employment of Bioinformatic Tools to Find New Targets for Gastric Cancer Treatment

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Brito, Daiane M. S., Lima, Odnan G., Mesquita, Felipe P., da Silva, Emerson L., de Moraes, Maria E. A., Burbano, Rommel M. R., Montenegro, Raquel C., Souza, Pedro F. N.·2023·Pharmaceutics
DOI 10.3390/pharmaceutics15092303PMC PMC10535099

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Abstract

Gastric cancer (GC) is a highly heterogeneous, complex disease and the fifth most common cancer worldwide (about 1 million cases and 784,000 deaths worldwide in 2018). GC has a poor prognosis (the 5-year survival rate is less than 20%), but there is an effort to find genes highly expressed during tumor establishment and use the related proteins as targets to find new anticancer molecules. Data were collected from the Gene Expression Omnibus (GEO) bank to obtain three dataset matrices analyzing gastric tumor tissue versus normal gastric tissue and involving microarray analysis performed using the GPL570 platform and different sources. The data were analyzed using the GEPIA tool for differential expression and KMPlot for survival analysis. For more robustness, GC data from the TCGA database were used to corroborate the analysis of data from GEO. The genes found in in silico analysis in both GEO and TCGA were confirmed in several lines of GC cells by RT-qPCR. The AlphaFold Protein Structure Database was used to find the corresponding proteins. Then, a structure-based virtual screening was performed to find molecules, and docking analysis was performed using the DockThor server. Our in silico and RT-qPCR analysis results confirmed the high expression of the AJUBA, CD80 and NOLC1 genes in GC lines. Thus, the corresponding proteins were used in SBVS analysis. There were three molecules, one molecule for each target, MCULE-2386589557-0-6, MCULE-9178344200-0-1 and MCULE-5881513100-0-29. All molecules had favorable pharmacokinetic, pharmacodynamic and toxicological properties. Molecular docking analysis revealed that the molecules interact with proteins in critical sites for their activity. Using a virtual screening approach, a molecular docking study was performed for proteins encoded by genes that play important roles in cellular functions for carcinogenesis. Combining a systematic collection of public microarray data with a comparative meta-profiling, RT-qPCR, SBVS and molecular docking analysis provided a suitable approach for finding genes involved in GC and working with the corresponding proteins to search for new molecules with anticancer properties.

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Figure 1 (A) Batch effect corrected among the studies. (B) Volcano plot of differentially expressed genes (DEGs) in gastric tumors. Red dots represent significant expressed genes.
Figure 1 (A) Batch effect corrected among the studies. (B) Volcano plot of differentially expressed genes (DEGs) in gastric tumors. Red dots represent significant expressed genes.CC BY · PubMed Central
Figure 2 Differentially expressed genes were found using Gene Set Enrichment Analysis (GSEA).
Figure 2 Differentially expressed genes were found using Gene Set Enrichment Analysis (GSEA).CC BY · PubMed Central
Figure 3 TCGA (RNA-seq) expression data for selected genes using Gene Expression Profiling Interactive Analysis (GEPIA). Bars indicate the difference between treatments. * p < 0.01 by one-away (ANOVA) followed by Bonferroni’s test.
Figure 3 TCGA (RNA-seq) expression data for selected genes using Gene Expression Profiling Interactive Analysis (GEPIA). Bars indicate the difference between treatments. * p < 0.01 by one-away (ANOVA) followed by Bonferroni’s test.CC BY · PubMed Central
Figure 4 Survival analysis for the genes (A) AJUBA, (B) GPNMB, (C) CD80, (D) FBXL13, (E) PDILT, (F) CCDC69, (G) KNL1, (H) IL13RA2 and (I) NOLC1 using Kaplan–Meier plotter (KM plotter).
Figure 4 Survival analysis for the genes (A) AJUBA, (B) GPNMB, (C) CD80, (D) FBXL13, (E) PDILT, (F) CCDC69, (G) KNL1, (H) IL13RA2 and (I) NOLC1 using Kaplan–Meier plotter (KM plotter).CC BY · PubMed Central
Figure 5 The relative expression level of AJUBA, CD80, NOLC1 and KNL1 genes in samples of gastric tumor cell lines (A) ACP-02, (B) ACP-03, (C) AGP-01 and (D) AGS is related to normal gastric cells, MNP-01. ** p < 0.01; *** p < 0.001 by one-away (ANOVA) followed by Bonferroni´s test.
Figure 5 The relative expression level of AJUBA, CD80, NOLC1 and KNL1 genes in samples of gastric tumor cell lines (A) ACP-02, (B) ACP-03, (C) AGP-01 and (D) AGS is related to normal gastric cells, MNP-01. ** p < 0.01; *** p < 0.001 by one-away (ANOVA) followed by Bonferroni´s test.CC BY · PubMed Central
Figure 6 (A) Molecular docking analysis of the AJUBA protein and molecule MCULE-2386589557-0-6. (B) Molecular docking analysis of the CD80 protein and molecule MCULE-9178344200-0-1. (C) Molecular docking analysis of the NOLC1 protein and molecule MCULE-5881513100-0-29. Figure 6 shows the 2D and 3D analysis of the interaction. In the 3D representation, the three compounds are in stick format; proteins are represented in cartoon format, while the residues involved in hydrogen bonding are shown in line format. Black dotted lines represent hydrogen bonds; the bond length is expressed in the Angstrom (Å) measurement unit. The 2D diagram shows the amino acid residues involved in the interactions between AJUBA, CD80 and NOLC1 proteins and the selected MCULE compound for each protein. The present type of interaction is represented by colors, which are identified by the legend in the figure.
Figure 6 (A) Molecular docking analysis of the AJUBA protein and molecule MCULE-2386589557-0-6. (B) Molecular docking analysis of the CD80 protein and molecule MCULE-9178344200-0-1. (C) Molecular docking analysis of the NOLC1 protein and molecule MCULE-5881513100-0-29. Figure 6 shows the 2D and 3D analysis of the interaction. In the 3D representation, the three compounds are in stick format; proteins are represented in cartoon format, while the residues involved in hydrogen bonding are shown in line format. Black dotted lines represent hydrogen bonds; the bond length is expressed in the Angstrom (Å) measurement unit. The 2D diagram shows the amino acid residues involved in the interactions between AJUBA, CD80 and NOLC1 proteins and the selected MCULE compound for each protein. The present type of interaction is represented by colors, which are identified by the legend in the figure.CC BY · PubMed Central
Figure 7 Graphic representation of the network of protein interactions for proteins (A) AJUBA, (B) CD80 and (C) NOLC1, identified using STRING v11.5. Each node represents a protein, and each edge represents an interaction. Colored lines between the proteins indicate the various types of interaction evidence.
Figure 7 Graphic representation of the network of protein interactions for proteins (A) AJUBA, (B) CD80 and (C) NOLC1, identified using STRING v11.5. Each node represents a protein, and each edge represents an interaction. Colored lines between the proteins indicate the various types of interaction evidence.CC BY · PubMed Central
Figure 8 ① Protein; ② interaction between the targeted protein and the protein in the respective pathway selected on STRING; ③ analysis of complex formed by the molecule and targeted protein with the protein in the respective pathway.
Figure 8 ① Protein; ② interaction between the targeted protein and the protein in the respective pathway selected on STRING; ③ analysis of complex formed by the molecule and targeted protein with the protein in the respective pathway.CC BY · PubMed Central

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Metadata

Authors
Brito, D. M. S., Lima, O. G., Mesquita, F. P., da Silva, E. L., de Moraes, M. E. A., Burbano, R. M. R., Montenegro, R. C., & Souza, P. F. N.
Affiliations
  • Department of Biochemistry and Molecular Biology, Federal University of Ceará, Fortaleza 60020-181, Brazil
  • Universidade Federal do Ceará (BR)
  • Universidade Federal do Pará (BR)
Corresponding author
Souza, Pedro F. N.
ORCID
Venue
Pharmaceutics
Publisher
MDPI AG
Published
2023-09-12
Volume
15
Issue
9
Pages
2303
ISSN
1999-4923 (ISSN-L)
DOI
10.3390/pharmaceutics15092303
PMC
PMC10535099
OpenAlex
W4386638888
Cited by
6
References
3
Citations by year
2026: 1 · 2025: 1 · 2024: 3 · 2023: 1
FWCI
0.86
Type
journal article
Language
en
Open access
Licence
Locations
  • Pharmaceutics (publishedVersion, cc-by)
  • Preprints.org (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Council for Scientific and Technological Development (CNPq) (403493/2021-8, 305459/2019-8, 305003/2022-4, 151388/2022-9)
  • CNPq (403493/2021-8, 305459/2019-8, 305003/2022-4, 151388/2022-9)
  • Red Latinoamericana de Implementación y Validación de guias clínicas Farmacogenomicas (RELIVAF) (403493/2021-8, 305459/2019-8, 305003/2022-4, 151388/2022-9)
  • Pró-Reitoria de Pesquisa e Pós-Graduação (PROPESP) from the Federal University of Pará (UFPA) (403493/2021-8, 305459/2019-8, 305003/2022-4, 151388/2022-9)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Health Sciences › Medicine › Oncology › Gastric Cancer Management and Outcomes
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
gastric cancer, drug target identification, differentially expressed genes, Ajuba, CD80, NOLC1, structure-based virtual screening, molecular docking, anticancer drug discovery, gene expression profiling, prognostic factors

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Brito, D. M. S., Lima, O. G., Mesquita, F. P., da Silva, E. L., de Moraes, M. E. A., Burbano, R. M. R., Montenegro, R. C., & Souza, P. F. N. (2023). A Shortcut from Genome to Drug: The Employment of Bioinformatic Tools to Find New Targets for Gastric Cancer Treatment. Pharmaceutics. https://doi.org/10.3390/pharmaceutics15092303