Abstract
Introduction
Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy.
Objectives
Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways.
Methods
OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM.
Results
Our analysis identified 447 genes common to both the “OM” and “wound healing” text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success.
Conclusions
Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.
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Acknowledgements
The authors are thankful to the research team in the department of Oral Medicine and to Dr. Cory Brouwer for their helpful discussions at the beginning of this project.
Funding
This work was supported by Carolinas HealthCare System Research fund.
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Electronic supplementary material
Supplementary Figure 1.
Targetable PTGS2/BCL2 subnetwork. Connecting line color indicates the type of information used to infer the association.Items were arranged manually for optimal viewing.(GIF 9kb)
Supplementary Figure 2.
KEGG diagram for the Nuclear Factor kappa-B signaling pathway, with relevant genes highlighted in red. The gene CFLAR is referred to here by its alias ‘c-FLIP.’ (GIF 166kb)
Supplementary Figure 3.
KEGG diagram for the apoptosis pathway, with relevant genes highlighted in red. CFLAR is referred to here as ‘FLIP’; TP53 is referred to as ‘p53’; AKT1 is referred to as Akt/PKB. (GIF 166kb)
Supplementary Table 1.
Total 48 genes identified by protein interaction analysis. KEGG enriched 89 significant genes yielded 48 genes with potential interactions using STRING.(DOCX 16kb)
Supplementary Table 2.
OM drug candidates targeting genes belonging to KEGG pathways associated with oral mucositis and cancer therapy. The table contains 50 most highly enriched KEGG pathways in the gene set intersecting the text mining search results for 'oral mucositis,' and the search results for genes affected by drugs that have been tested or are being tested in clinical trials for cancer therapy associated OM (ClinicalTrials.gov). The p-value is calculated using the hypergeometric distribution, which describes the probability of a number of a given type of genes appearing in the query set given the total number of those genes in the reference set (genome). (DOCX 26kb)
Supplementary Table 3.
OM drug candidates targeting genes belonging to KEGG pathways associated with wound healing and cancer therapy. The table contains the 52 most highly enriched KEGG pathways in the gene set intersecting the text mining search results for 'wound healing,' and the search results for genes affected by clinical trial drugs. The p-value is calculated using the hypergeometric distribution as described in the Supplementary Table 2 note above. (DOCX 27kb)
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Kirk, J., Shah, N., Noll, B. et al. Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy. Support Care Cancer 26, 2695–2705 (2018). https://doi.org/10.1007/s00520-018-4096-2
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DOI: https://doi.org/10.1007/s00520-018-4096-2