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pmid:39458974

The Identification of New Pharmacological Targets for the Treatment of Glaucoma: A Network Pharmacology Approach.

Erika Giuffrida · Chiara Bianca Maria Platania · Francesca Lazzara · Federica Conti · Nicoletta Marcantonio · Filippo Drago · Claudio Bucolo

Pharmaceuticals (Basel, Switzerland) · 2024 doi:10.3390/ph17101333

Paper

complete

Experiment

complete

Data

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Analysis

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:39458974
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science-record
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https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json
created_at
2026-11-01T20:29:43Z
updated_at
2026-11-01T22:13:01Z
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paper object · 10 fields
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title
The Identification of New Pharmacological Targets for the Treatment of Glaucoma: A Network Pharmacology Approach.
doi
10.3390/ph17101333
pmid
39458974
authors array · 7 items
[0]
Erika Giuffrida
[1]
Chiara Bianca Maria Platania
[2]
Francesca Lazzara
[3]
Federica Conti
[4]
Nicoletta Marcantonio
[5]
Filippo Drago
[6]
Claudio Bucolo
journal
Pharmaceuticals (Basel, Switzerland)
year
2024
abstract
Background: Glaucoma is a progressive optic neuropathy characterized by the neurodegeneration and death of retinal ganglion cells (RGCs), leading to blindness. Current glaucoma interventions reduce intraocular pressure but do not address retinal neurodegeneration. In this effort, to identify new pharmacological targets for glaucoma management, we employed a network pharmacology approach. Methods: We first retrieved transcriptomic data from GEO, an NCBI database, and carried out GEO2R (an interactive web tool aimed at comparing two or more groups of samples in a GEO dataset). The GEO2R statistical analysis aimed at identifying the top differentially expressed genes (DEGs) and used these as input of STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) app within Cytoscape software, which builds networks of proteins starting from input DEGs. Analyses of centrality metrics using Cytoscape were carried out to identify nodes (genes or proteins) involved in network stability. We also employed the web-server software MIRNET 2.0 to build miRNA-target interaction networks for a re-analysis of the GSE105269 dataset, which reports analyses of microRNA expressions. Results: The pharmacological targets, identified in silico through analyses of the centrality metrics carried out with Cytoscape, were rescored based on correlations with entries in the PubMed and clinicaltrials.gov databases. When there was no match (82 out of 135 identified central nodes, in 8 analyzed networks), targets were considered "potential innovative" targets for the treatment of glaucoma, after further validation studies. Conclusions: Several druggable targets, such as GPCRs (e.g., 5-hydroxytryptamine 5A (5-HT5A) and adenosine A2B receptors) and enzymes (e.g., lactate dehydrogenase A or monoamine oxidase B), were found to be rescored as "potential innovative" pharmacological targets for glaucoma treatment.
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DEGs (p < 0.05) for each sample of the GEO datasets (e.g., glaucoma vs. healthy control) were identified using the GEO2R statistical analysis (https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html accessed on 1 March 2022), applying the Benjamini and Hochberg (false discovery rate) method for the adjustment of p-values using standard parameters in GEO2R [23,123,124].
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sections[14] "4.1. GEO Dataset" paragraph 2
quote
DEGs (p < 0.05) for each sample of the GEO datasets (e.g., glaucoma vs. healthy control) were identified using the GEO2R statistical analysis (https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html accessed on 1 March 2022), applying the Benjamini and Hochberg (false discovery rate) method for the adjustment of p-values using standard parameters in GEO2R [23,123,124].
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sections[23] "notes" paragraph 1
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All data generated or analyzed in this study are included in this published article.
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Across six GEO datasets, 82 of 135 central genes had no match in PubMed or clinicaltrials.gov and were classified as potential innovative pharmacological targets for glaucoma treatment.
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When there was no match (82 out of 135 identified central nodes, in 8 analyzed networks), targets were considered “potential innovative” targets for the treatment of glaucoma, after further validation studies.
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sections[0] "Abstract" paragraph 1
quote
Several druggable targets, such as GPCRs (e.g., 5-hydroxytryptamine 5A (5-HT5A) and adenosine A2B receptors) and enzymes (e.g., lactate dehydrogenase A or monoamine oxidase B), were found to be rescored as “potential innovative” pharmacological targets for glaucoma treatment.
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Across six GEO datasets, 82 of 135 central genes had no match in PubMed or clinicaltrials.gov and were classified as potential innovative pharmacological targets for glaucoma treatment.
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All data generated or analyzed in this study are included in this published article.
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Differentially expressed microRNAs in the aqueous humor of patients with exfoliation glaucoma or primary open-angle glaucoma
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All data generated or analyzed in this study are included in this published article.
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14745
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Rattus norvegicus
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Transcriptional profiling of the rat retina after optic nerve crush uncovers sustained activation of the complement cascade and Delta-Notch signaling pathways
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2026-11-01T20:16:08Z
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2507
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Genome-wide Expression of Trabecular Meshwork in POAG
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2026-11-01T18:06:43Z
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All data generated or analyzed in this study are included in this published article.
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All data generated or analyzed in this study are included in this published article.
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1261
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Mus musculus
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Microarray Analysis of Retinal Gene Expression in the DBA/2J Model of Glaucoma
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2026-11-01T20:16:08Z
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All data generated or analyzed in this study are included in this published article.
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All data generated or analyzed in this study are included in this published article.
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570
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Homo sapiens
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Genome-wide expression profile of human trabecular meshwork cultured cells, non-glaucomatous and POAG tissue
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2026-11-01T20:16:08Z
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5175
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Homo sapiens
title
An Investigation of Global Gene Expression Patterns in Glaucoma and Ocular Hypertension Derived Optic Nerve Heads
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DEGs (p < 0.05) for each sample of the GEO datasets (e.g., glaucoma vs. healthy control) were identified using the GEO2R statistical analysis (https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html accessed on 1 March 2022), applying the Benjamini and Hochberg (false discovery rate) method for the adjustment of p-values using standard parameters in GEO2R [23,123,124].
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GEO GSE105269
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rotoninergic agonist) decreased the IOP in a mouse model of steroid-induced ocular hypertension [87].
[1]
ady been investigated as a modulator of IOPs in vivo [50,51]. With respect to adrenergic receptor
[2]
olvement of lipocalin 2 was confirmed in two in vivo models of glaucoma, spontaneous glaucomatous
[3]
erimental animal models of glaucoma, such as in mice [37] and rats [38]. 2.2. GSE105269: Analysis
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genes related to the AKT and Bcl-2-regulated apoptosis signaling pathways were identified as gene t
[1]
T1, and GRB2); the “p53 signaling pathway”; “Apoptosis”; and “Axon guidance”. Pathways such as “Cho
[2]
in which plays key roles in innate immunity, apoptosis, and cell proliferation. Moreover, the invol
[3]
kine promotes caspase activation, leading to apoptosis, and IOP elevation in a rat model of ocular
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paper_text_order:7030
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bosentan is currently under evaluation in a randomized controlled trial (NCT02377271) including patients with nonart
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rovascular dysfunctions [76]. Furthermore, a phase I clinical trial was carried out on POAG subjects treated wit
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], validated, with a quantitative RT-PCR the microarray results for ceruloplasmin, chitinase 3-like
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39458974", "kind": "science-record", "revision": 3, "content_hash": "sha256:0616ddcc0f0ea819bbe3c7e91caa03424e34356d0117c8a60df4eb47268ba36c", "created_at": "2026-11-01T20:29:43Z", "updated_at": "2026-11-01T22:13:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "The Identification of New Pharmacological Targets for the Treatment of Glaucoma: A Network Pharmacology Approach.", "doi": "10.3390/ph17101333", "pmid": "39458974", "authors": ["Erika Giuffrida", "Chiara Bianca Maria Platania", "Francesca Lazzara", "Federica Conti", "Nicoletta Marcantonio", "Filippo Drago", "Claudio Bucolo"], "journal": "Pharmaceuticals (Basel, Switzerland)", "year": 2024, "abstract": "Background: Glaucoma is a progressive optic neuropathy characterized by the neurodegeneration and death of retinal ganglion cells (RGCs), leading to blindness. Current glaucoma interventions reduce intraocular pressure but do not address retinal neurodegeneration. In this effort, to identify new pharmacological targets for glaucoma management, we employed a network pharmacology approach. Methods: We first retrieved transcriptomic data from GEO, an NCBI database, and carried out GEO2R (an interactive web tool aimed at comparing two or more groups of samples in a GEO dataset). The GEO2R statistical analysis aimed at identifying the top differentially expressed genes (DEGs) and used these as input of STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) app within Cytoscape software, which builds networks of proteins starting from input DEGs. Analyses of centrality metrics using Cytoscape were carried out to identify nodes (genes or proteins) involved in network stability. We also employed the web-server software MIRNET 2.0 to build miRNA-target interaction networks for a re-analysis of the GSE105269 dataset, which reports analyses of microRNA expressions. Results: The pharmacological targets, identified in silico through analyses of the centrality metrics carried out with Cytoscape, were rescored based on correlations with entries in the PubMed and clinicaltrials.gov databases. When there was no match (82 out of 135 identified central nodes, in 8 analyzed networks), targets were considered \"potential innovative\" targets for the treatment of glaucoma, after further validation studies. Conclusions: Several druggable targets, such as GPCRs (e.g., 5-hydroxytryptamine 5A (5-HT5A) and adenosine A2B receptors) and enzymes (e.g., lactate dehydrogenase A or monoamine oxidase B), were found to be rescored as \"potential innovative\" pharmacological targets for glaucoma treatment.", "claims": [{"id": "claim-method", "statement": "DEGs (p < 0.05) for each sample of the GEO datasets (e.g., glaucoma vs. healthy control) were identified using the GEO2R statistical analysis (https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html accessed on 1 March 2022), applying the Benjamini and Hochberg (false discovery rate) method for the adjustment of p-values using standard parameters in GEO2R [23,123,124].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39458974", "locator": "sections[14] \"4.1. GEO Dataset\" paragraph 2", "quote": "DEGs (p < 0.05) for each sample of the GEO datasets (e.g., glaucoma vs. healthy control) were identified using the GEO2R statistical analysis (https://www.ncbi.nlm.nih.gov/geo/info/geo2r.html accessed on 1 March 2022), applying the Benjamini and Hochberg (false discovery rate) method for the adjustment of p-values using standard parameters in GEO2R [23,123,124]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed in this study are included in this published article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39458974", "locator": "sections[23] \"notes\" paragraph 1", "quote": "All data generated or analyzed in this study are included in this published article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Across six GEO datasets, 82 of 135 central genes had no match in PubMed or clinicaltrials.gov and were classified as potential innovative pharmacological targets for glaucoma treatment.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39458974", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "When there was no match (82 out of 135 identified central nodes, in 8 analyzed networks), targets were considered “potential innovative” targets for the treatment of glaucoma, after further validation studies."}, {"asset": "source:pmid:39458974", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Several druggable targets, such as GPCRs (e.g., 5-hydroxytryptamine 5A (5-HT5A) and adenosine A2B receptors) and enzymes (e.g., lactate dehydrogenase A or monoamine oxidase B), were found to be rescored as “potential innovative” pharmacological targets for glaucoma treatment."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Across six GEO datasets, 82 of 135 central genes had no match in PubMed or clinicaltrials.gov and were classified as potential innovative pharmacological targets for glaucoma treatment.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39458974"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE105269"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE105269"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39458974", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/ph17101333", "sha256": 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