A Computational Text Mining-Guided Meta-Analysis Approach to Identify Potential Xerostomia Drug Targets

Abstract

Xerostomia (subjective complaint of dry mouth) is commonly associated with salivary gland hypofunction. Molecular mechanisms associated with xerostomia pathobiology are poorly understood, thus hampering drug development. Our objectives were to (i) use text-mining tools to investigate xerostomia and dry mouth concepts, (ii) identify associated molecular interactions involving genes as candidate drug targets, and (iii) determine how drugs currently used in clinical trials may impact these genes and associated pathways. PubMed and PubMed Central were used to identify search terms associated with xerostomia and/or dry mouth. Search terms were queried in pubmed2ensembl. Protein–protein interaction (PPI) networks were determined using the gene/protein network visualization program search tool for recurring instances of neighboring genes (STRING). A similar program, Cytoscape, was used to determine PPIs of overlapping gene sets. The drug–gene interaction database (DGIdb) and the clinicaltrials.gov database were used to identify potential drug targets from the xerostomia/dry mouth PPI gene set. We identified 64 search terms in common between xerostomia and dry mouth. STRING confirmed PPIs between identified genes (CL = 0.90). Cytoscape analysis determined 58 shared genes, with cytokine–cytokine receptor interaction representing the most significant pathway (p = 1.29 × 10−23) found in the Kyoto encyclopedia of genes and genomes (KEGG). Fifty-four genes in common had drug interactions, per DGIdb analysis. Eighteen drugs, targeting the xerostomia/dry mouth PPI network, have been evaluated for xerostomia, head and neck cancer oral complications, and Sjögren’s Syndrome. The PPI network genes IL6R, EGFR, NFKB1, MPO, and TNFSF13B constitute a possible biomarker signature of xerostomia. Validation of the candidate biomarkers is necessary to better stratify patients at the genetic and molecular levels to facilitate drug development or to monitor response to treatment.

Comparative Topic Modeling for Determinants of Divergent Report Results Applied to Macular Degeneration Studies

“…In another domain, the Neurosynth Corpus [7] is created via text mining, namely by tying keyword mentions to fMRI images from a large sample of studies to assemble a library of neuroimaging representations associated with cognitive, physical, and emotional states. Additionally, text mining is leveraged to gather data for MA to identify xerostomia drug targets [8], evaluate the efficacy of educational software [9], and analyze treatment plans for addiction recovery [10] as other examples.…”

Insights into the nutritional prevention of macular degeneration based on a comparative topic modeling approach

“…In another domain, the Neurosynth Corpus ( Neurosynth, 2023 ) is created via text mining, namely by tying keyword mentions to fMRI images from a large sample of studies to assemble a library of neuroimaging representations associated with cognitive, physical, and emotional states. Additionally, text mining is leveraged to gather data for MA to identify xerostomia drug targets ( Beckman et al, 2022 ), evaluate the efficacy of educational software ( Costa et al, 2020 ), and augment biomarker discovery ( Bhatnagar et al, 2022 ).…”

Text-Mining Approach to Identify Hub Genes of Cancer Metastasis and Potential Drug Repurposing to Target Them

“…In the past, we have used PubMed citation counts to identify trends in hallmark-based drug discovery in breast cancer [35]. Recently, the text-mining approach was also used to identify Xerostomia-associated genes and candidate drug targets to improve clinical outcomes [36]. Currently, cancer remains the major health problem accounting for approximately 19.3 million new cases and approximately 10 million deaths worldwide, and more than 2,569,626 PubMed citations related to cancer can be used to gain biological insight into processes related to the metastasis of cancer [37].…”