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pmid:32443297

Screening and identification of key biomarkers for retinoblastoma: Evidence from bioinformatics analysis.

Jing Huang · Lingrui Zhang · Zimeng Li · Xuejing Lu

Medicine · 2020 doi:10.1097/MD.0000000000019952

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

Execution

absent

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title
Screening and identification of key biomarkers for retinoblastoma: Evidence from bioinformatics analysis.
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10.1097/MD.0000000000019952
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32443297
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Jing Huang
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Lingrui Zhang
[2]
Zimeng Li
[3]
Xuejing Lu
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Medicine
year
2020
abstract
BACKGROUND: Retinoblastoma (RB) is one of the most common malignant tumors in pediatrics; to clarify the cause of RB, a lot of manpower and material resources have been invested but have not been well explained. METHODS: To identify the candidate genes in the occurrence and development of the disease, we downloaded the microarray datasets GSE97508, GSE92987, and GSE24673 from the gene expression database (GEO). The differentially expressed gene (DEG) was identified and functional enrichment analysis was performed. The protein-protein interaction network was constructed and analyzed by String and Cytoscape. RESULTS: A total of 74 DEGs were identified, including 40 up-regulated genes and 34 down-regulated genes. The rich functions and pathways of DEG include regulating mitosis, cell cycle, DNA transcription process, promoting protein phosphorylation, regulating energy metabolism in vivo, promoting the binding of some macromolecular complexes, and regulating the cell cycle. Twenty-four HUB genes were identified. Biological process analysis showed that these genes were mainly enriched in regulating energy metabolism in vivo, promoting the binding of some small molecules and regulating the cell cycle. Survival analysis showed that DGPDC1, NDC80, SHCBP, TOP2A, and DLGAP5 may be involved in the occurrence, invasion, or recurrence of RB. CONCLUSION: In conclusion, screening DEGs and HUB genes in RB can help us to better understand the mechanism of the occurrence and development of RB at the molecular level, and provide candidate targets for the diagnosis and treatment of RB.
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Characterization and molecular mechanistic study of a therapeutic peptide functionalized nanobioconjugate inhibiting the p53-HDM2 interaction in Retinoblastoma
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32443297", "kind": "science-record", "revision": 7, "content_hash": "sha256:40ca171d5edc9fc0fd6a7b41982d5ecac785fc31d068fb121df0c88260a2bdad", "created_at": "2026-11-01T15:41:02Z", "updated_at": "2026-11-01T22:01:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening and identification of key biomarkers for retinoblastoma: Evidence from bioinformatics analysis.", "doi": "10.1097/MD.0000000000019952", "pmid": "32443297", "authors": ["Jing Huang", "Lingrui Zhang", "Zimeng Li", "Xuejing Lu"], "journal": "Medicine", "year": 2020, "abstract": "BACKGROUND: Retinoblastoma (RB) is one of the most common malignant tumors in pediatrics; to clarify the cause of RB, a lot of manpower and material resources have been invested but have not been well explained.\nMETHODS: To identify the candidate genes in the occurrence and development of the disease, we downloaded the microarray datasets GSE97508, GSE92987, and GSE24673 from the gene expression database (GEO). The differentially expressed gene (DEG) was identified and functional enrichment analysis was performed. The protein-protein interaction network was constructed and analyzed by String and Cytoscape.\nRESULTS: A total of 74 DEGs were identified, including 40 up-regulated genes and 34 down-regulated genes. The rich functions and pathways of DEG include regulating mitosis, cell cycle, DNA transcription process, promoting protein phosphorylation, regulating energy metabolism in vivo, promoting the binding of some macromolecular complexes, and regulating the cell cycle. Twenty-four HUB genes were identified. Biological process analysis showed that these genes were mainly enriched in regulating energy metabolism in vivo, promoting the binding of some small molecules and regulating the cell cycle. Survival analysis showed that DGPDC1, NDC80, SHCBP, TOP2A, and DLGAP5 may be involved in the occurrence, invasion, or recurrence of RB.\nCONCLUSION: In conclusion, screening DEGs and HUB genes in RB can help us to better understand the mechanism of the occurrence and development of RB at the molecular level, and provide candidate targets for the diagnosis and treatment of RB.", "claims": [{"id": "claim-method", "statement": "The identification of DEGs using GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r, GEO2R) is an interactive network tool in GEO, which can be used to identify the genetic differences between normal and cancerous cells in RB under experimental conditions.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32443297", "locator": "sections[4] \"Identification of DEGs\" paragraph 2", "quote": "The identification of DEGs using GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r, GEO2R) is an interactive network tool in GEO, which can be used to identify the genetic differences between normal and cancerous cells in RB under experimental conditions."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are publicly available.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32443297", "locator": "sections[15] \"Footnotes\" paragraph 2", "quote": "The datasets generated during and/or analyzed during the current study are publicly available."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:32443297"], "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:GSE92987"], "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:GSE92987"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32443297", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1097/MD.0000000000019952", "sha256": 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