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

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

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

Open access
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

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/pharmaceutics15092303

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Figures

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 45

  1. Smyth, 2020, Gastric Cancer, Lancet, 396, 635, 10.1016/S0140-6736(20)31288-5 · DOI
  2. Sung, 2021, Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA Cancer J. Clin., 71, 209, 10.3322/caac.21660 · DOI · Paper page
  3. Liu, 2022, Long Non-Coding RNA in Gastric Cancer: Mechanisms and Clinical Implications for Drug Resistance, Front. Oncol., 12, 841411, 10.3389/fonc.2022.841411 · DOI
  4. Lauren, 1965, The two histological main types of gastric carcinoma: Diffuse and so-called intestinal-type carcinoma. an attempt at a histo-clinical classification, Acta Pathol. Microbiol. Scand., 64, 31, 10.1111/apm.1965.64.1.31 · DOI
  5. Sasako, 2011, Five-Year Outcomes of a Randomized Phase III Trial Comparing Adjuvant Chemotherapy with S-1 versus Surgery Alone in Stage II or III Gastric Cancer, J. Clin. Oncol., 29, 4387, 10.1200/JCO.2011.36.5908 · DOI
  6. Leichman, 1992, Preoperative Systemic Chemotherapy Followed by Adjuvant Postoperative Intraperitoneal Therapy for Gastric Cancer: A University of Southern California Pilot Program, J. Clin. Oncol., 10, 1933, 10.1200/JCO.1992.10.12.1933 · DOI
  7. Ajani, 1999, Enhanced Staging and All Chemotherapy Preoperatively in Patients with Potentially Resectable Gastric Carcinoma, J. Clin. Oncol., 17, 2403, 10.1200/JCO.1999.17.8.2403 · DOI
  8. Guan, 2023, Gastric Cancer Treatment: Recent Progress and Future Perspectives, J. Hematol. Oncol., 16, 57, 10.1186/s13045-023-01451-3 · DOI
  9. Joshi, 2021, Current Treatment and Recent Progress in Gastric Cancer, CA Cancer J. Clin., 71, 264, 10.3322/caac.21657 · DOI
  10. Miller, 2015, Proteasome Inhibitors with Pyrazole Scaffolds from Structure-Based Virtual Screening, J. Med. Chem., 58, 2036, 10.1021/jm501344n · DOI
  11. Rohr, 2021, A Merged Microarray Meta-Dataset for Transcriptionally Profiling Colorectal Neoplasm Formation and Progression, Sci. Data, 8, 214, 10.1038/s41597-021-00998-5 · DOI
  12. Liberzon, 2011, Molecular Signatures Database (MSigDB) 3.0, Bioinformatics, 27, 1739, 10.1093/bioinformatics/btr260 · DOI · Paper page
  13. 2023, Discovery and Ranking of the Most Robust Prognostic Biomarkers in Serous Ovarian Cancer, GeroScience, 45, 1889, 10.1007/s11357-023-00742-4 · DOI
  14. Ribeiro, 2018, Gastric Cancer Cell Lines Have Different MYC-Regulated Expression Patterns but Share a Common Core of Altered Genes, Can. J. Gastroenterol. Hepatol., 2018, 5804376
  15. Riquelme, 2016, The Gene Expression Status of the PI3K/AKT/MTOR Pathway in Gastric Cancer Tissues and Cell Lines, Pathol. Oncol. Res., 22, 797, 10.1007/s12253-016-0066-5 · DOI
  16. Mesquita, 2021, Kinase Inhibitor Screening Reveals Aurora-a Kinase Is a Potential Therapeutic and Prognostic Biomarker of Gastric Cancer, J. Cell. Biochem., 122, 1376, 10.1002/jcb.30015 · DOI
  17. Bustin, S.A., Beaulieu, J.-F., Huggett, J., Jaggi, R., Kibenge, F.S.B., Olsvik, P.A., Penning, L.C., and Toegel, S. (2010). MIQE Precis: Practical Implementation of Minimum Standard Guidelines for Fluorescence-Based Quantitative Real-Time PCR Experiments. BMC Mol. Biol., 11. · DOI
  18. Varadi, 2022, AlphaFold Protein Structure Database: Massively Expanding the Structural Coverage of Protein-Sequence Space with High-Accuracy Models, Nucleic Acids Res., 50, D439, 10.1093/nar/gkab1061 · DOI · Paper page
  19. Kiss, 2012, http://Mcule.com: A Public Web Service for Drug Discovery, J. Cheminform., 4, 2012, 10.1186/1758-2946-4-S1-P17 · DOI
  20. Muteeb, G., Rehman, M.T., AlAjmi, M.F., Aatif, M., Farhan, M., and Shafi, S. (2022). Identification of a Potential Inhibitor (MCULE-8777613195-0-12) of New Delhi Metallo-β-Lactamase-1 (NDM-1) Using In Silico and In Vitro Approaches. Molecules, 27. · DOI
  21. Lipinski, 2012, Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings, Adv. Drug Deliv. Rev., 64, 4, 10.1016/j.addr.2012.09.019 · DOI
  22. Lipinski, 2000, Drug-like Properties and the Causes of Poor Solubility and Poor Permeability, J. Pharmacol. Toxicol. Methods, 44, 235, 10.1016/S1056-8719(00)00107-6 · DOI
  23. Banerjee, 2018, ProTox-II: A Webserver for the Prediction of Toxicity of Chemicals, Nucleic Acids Res., 46, W257, 10.1093/nar/gky318 · DOI
  24. Daina, 2016, A BOILED-Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules, ChemMedChem, 11, 1117, 10.1002/cmdc.201600182 · DOI
  25. Daina, 2017, SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules, Sci. Rep., 7, 42717, 10.1038/srep42717 · DOI · Paper page
  26. Almeida, 2014, A Dynamic Niching Genetic Algorithm Strategy for Docking Highly Flexible Ligands, Inf. Sci., 289, 206, 10.1016/j.ins.2014.08.002 · DOI
  27. Guedes, 2021, New Machine Learning and Physics-Based Scoring Functions for Drug Discovery, Sci. Rep., 11, 3198, 10.1038/s41598-021-82410-1 · DOI
  28. Inbar, 2005, Approaching the CAPRI Challenge with an Efficient Geometry-Based Docking, Proteins Struct. Funct. Bioinform., 60, 217, 10.1002/prot.20561 · DOI
  29. Housman, 2014, Drug Resistance in Cancer: An Overview, Cancers, 6, 1769, 10.3390/cancers6031769 · DOI · Paper page
  30. Borriello, A., Caldarelli, I., Basile, M.A., Bencivenga, D., Tramontano, A., Perrotta, S., della Ragione, F., and Oliva, A. (2011). The Tyrosine Kinase Inhibitor Dasatinib Induces a Marked Adipogenic Differentiation of Human Multipotent Mesenchymal Stromal Cells. PLoS ONE, 6. · DOI
  31. Yi, 2022, CCDC69 Is a Prognostic Marker of Breast Cancer and Correlates with Tumor Immune Cell Infiltration, Front. Surg., 9, 879921, 10.3389/fsurg.2022.879921 · DOI
  32. Prieto-Martínez, F.D., López-López, E., Eurídice Juárez-Mercado, K., and Medina-Franco, J.L. (2019). Silico Drug Design: Repurposing Techniques and Methodologies, Elsevier.
  33. Anthony, L.F.W., Kanding, B., and Selvan, R. (2020). Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models. arXiv.
  34. Song, 2022, Therapeutic Exosomes Loaded with SERPINA5 Attenuated Endometrial Cancer Cell Migration via the Integrin Β1/FAK Signaling Pathway, Cell. Oncol., 45, 861, 10.1007/s13402-022-00687-4 · DOI
  35. Wu, 2021, Kaempferol Reverses Aerobic Glycolysis via MiR-339-5p-Mediated PKM Alternative Splicing in Colon Cancer Cells, J. Agric. Food Chem., 69, 3060, 10.1021/acs.jafc.0c07640 · DOI
  36. Jia, 2017, The LIM Protein AJUBA Promotes Colorectal Cancer Cell Survival through Suppression of JAK1/STAT1/IFIT2 Network, Oncogene, 36, 2655, 10.1038/onc.2016.418 · DOI
  37. Dommann, N., Sánchez-Taltavull, D., Eggs, L., Birrer, F., Brodie, T., Salm, L., Baier, F.A., Medova, M., Humbert, M., and Tschan, M.P. (2020). The LIM Protein AJUBA Augments Tumor Metastasis in Colon Cancer. Cancers, 12. · DOI
  38. Li, 1996, The Expression of Costimulatory Molecules CD80 and CD86 in Human Carcinoma Cell Lines: Its Regulation by Interferon Gamma and Interleukin-10, Cancer Immunol. Immunother., 43, 213, 10.1007/s002620050324 · DOI
  39. Horn, 2018, Soluble CD80 Protein Delays Tumor Growth and Promotes Tumor-Infiltrating Lymphocytes, Cancer Immunol. Res., 6, 59, 10.1158/2326-6066.CIR-17-0026 · DOI
  40. Feng, 2019, Low Expression of CD80 Predicts for Poor Prognosis in Patients with Gastric Adenocarcinoma, Future Oncol., 15, 473, 10.2217/fon-2018-0420 · DOI
  41. Peggs, 2010, Ipilimumab: Attenuation of an Inhibitory Immune Checkpoint Improves Survival in Metastatic Melanoma, Expert Rev. Anticancer Ther., 10, 1697, 10.1586/era.10.144 · DOI
  42. Zhai, 2023, Roles of NOLC1 in Cancers and Viral Infection, J. Cancer Res. Clin. Oncol., 149, 10593, 10.1007/s00432-023-04934-5 · DOI
  43. Kong, 2021, Knockdown of NOLC1 Inhibits PI3K-AKT Pathway to Improve the Poor Prognosis of Esophageal Carcinoma, J. Oncol., 2021, 9944132, 10.1155/2021/9944132 · DOI
  44. 2010, Protein-Protein Interactions and Cancer: Targeting the Central Dogma, Curr. Top. Med. Chem., 11, 258
  45. Bahmanyar, 2008, β-Catenin Is a Nek2 Substrate Involved in Centrosome Separation, Genes Dev., 22, 91, 10.1101/gad.1596308 · DOI

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review