Bioinformatics investigation of the prognostic value and mechanistic role of CD9 in glioma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39424846", "kind": "science-record", "revision": 3, "content_hash": "sha256:32c0e5776d355156fce4be1586f3c8da0f657fe38bf4c05b781a1b3a807b8770", "created_at": "2026-11-01T20:29:40Z", "updated_at": "2026-11-01T22:12:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics investigation of the prognostic value and mechanistic role of CD9 in glioma.", "doi": "10.1038/s41598-024-74109-w", "pmid": "39424846", "authors": ["Jing Jiang", "Bo Jiang", "Wen-Bin Li"], "journal": "Scientific reports", "year": 2024, "abstract": "In recent years, CD9 has been extensively studied as a potential biomarker for cancer. However, the biological role of CD9 in gliomas remains unclear. This study investigates the function of CD9 in gliomas and its molecular mechanisms. Utilizing pan-cancer analysis with TCGA, CGGA, and GEO databases, differential expression of CD9 was observed in 11 tumor types within the TCGA cohort, and it was associated with patient survival rates. Analysis of the CGGA glioma database revealed that patients with high CD9 expression had lower survival rates. The area under the ROC curve (AUC) for GSE16011 was greater than 0.7, indicating a high discriminative ability. Through gene set enrichment analysis (GSEA), immune-related analysis, and CD9 mutation detection, CD9 was found to have the strongest correlation with neutrophil involvement (cor = 0.30, P < 0.05), and the high CD9 expression group exhibited higher rejection responses and TIDE scores, suggesting a lower likelihood of successful immunotherapy. The high CD9 expression group was more sensitive to 81 drugs, indicating potential therapeutic effects for gliomas. Furthermore, overexpression of CD9 in gliomas may be associated with gene mutations. Down-regulation or up-regulation of CD9 expression in the glioblastoma cell line LN229 showed that CD9 could positively regulate the migratory ability of LN229 cells. Further, several marker genes, such as VEGFR-2, TGF-β1, CASP1 and PI3K, were down regulated in CD9 knockdown cell lines and up regulated in CD9 overexpression cell lines, compared with control cell line. This study preliminarily explores the role of CD9 in gliomas and its prognostic value, providing new insights for personalized treatment strategies in glioma therapy.", "claims": [{"id": "claim-method", "statement": "Moreover, CD9 expression differences in various clinical characteristics (age, gender, IDH mutation, and radiation) of TCGA-glioma and in different clinical characteristics (grade, IDH mutation, and X1p19q codeletion status) in CGGA-glioma were compared (P < 0.05).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39424846", "locator": "sections[5] \"CD9 expression analysis in glioma\" paragraph 2", "quote": "Moreover, CD9 expression differences in various clinical characteristics (age, gender, IDH mutation, and radiation) of TCGA-glioma and in different clinical characteristics (grade, IDH mutation, and X1p19q codeletion status) in CGGA-glioma were compared (P < 0.05)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The TCGA-glioma (GBMLGG) dataset utilized in this study was sourced from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), while the CGGA-glioma dataset was acquired from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/). Additionally, GSE16011 and validation set CSE68848 were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39424846", "locator": "sections[32] \"Data availability\" paragraph 2", "quote": "The TCGA-glioma (GBMLGG) dataset utilized in this study was sourced from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), while the CGGA-glioma dataset was acquired from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/). Additionally, GSE16011 and validation set CSE68848 were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The area under the ROC curve (AUC) for GSE16011 was greater than 0.7, indicating a high discriminative ability.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39424846", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The area under the ROC curve (AUC) for GSE16011 was greater than 0.7, indicating a high discriminative ability."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The area under the ROC curve (AUC) for GSE16011 was greater than 0.7, indicating a high discriminative ability.", "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:39424846"], "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:GSE16011"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE16011"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39424846", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-024-74109-w", "sha256": "sha256:bb6f05e24801248027c4605f81a818278ed852c9f70d10a9ad9b8760c5b2924b", "bytes": 49485, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-024-74109-w", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11489571", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE16011", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE16011", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE16011", "version": null, "note": "The TCGA-glioma (GBMLGG) dataset utilized in this study was sourced from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), while the CGGA-glioma dataset was acquired from the Chinese Glioma Genome Atlas (CGGA) database (http://www.cgga.org.cn/). 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