Evaluation of FGFR1 as a diagnostic biomarker for ovarian cancer using TCGA and GEO datasets.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33604191", "kind": "science-record", "revision": 8, "content_hash": "sha256:e25bdf978ee94085f80d98d17379aa5076457a218fc47ec2db1dd70293e23998", "created_at": "2026-11-01T15:41:25Z", "updated_at": "2026-11-01T22:46:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Evaluation of FGFR1 as a diagnostic biomarker for ovarian cancer using TCGA and GEO datasets.", "doi": "10.7717/peerj.10817", "pmid": "33604191", "authors": ["Huiting Xiao", "Kun Wang", "Dan Li", "Ke Wang", "Min Yu"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND: Malignant ovarian cancer is associated with the highest mortality of all gynecological tumors. Designing therapeutic targets that are specific to OC tissue is important for optimizing OC therapies. This study aims to identify different expression patterns of genes related to FGFR1 and the usefulness of FGFR1 as diagnostic biomarker for OC.\nMETHODS: We collected data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. In the TCGA cohort we analyzed clinical information according to patient characteristics, including age, stage, grade, longest dimension of the tumor and the presence of a residual tumor. GEO data served as a validation set. We obtained data on differentially expressed genes (DEGs) from the two microarray datasets. We then used gene set enrichment analysis (GSEA) to analyze the DEG data in order to identify enriched pathways related to FGFR1.\nRESULTS: Differential expression analysis revealed that FGFR1 was significantly downregulated in OC specimens. 303 patients were included in the TCGA cohort. The GEO dataset confirmed these findings using information on 75 Asian patients. The GSE105437 and GSE12470 database highlighted the significant diagnostic value of FGFR1 in identifying OC (AUC = 1, p = 0.0009 and AUC = 0.8256, p = 0.0015 respectively).\nCONCLUSIONS: Our study examined existing TCGA and GEO datasets for novel factors associated with OC and identified FGFR1 as a potential diagnostic factor. Further investigation is warranted to characterize the role played by FGFR1 in OC.", "claims": [{"id": "claim-method", "statement": "The two ovarian cancer gene expression microarray datasets GSE105437 and GSE12470 were analyzed using the R limma package and were sorted according to logfold-change values with corrected p-values <0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33604191", "locator": "sections[10] \"Identification of DEGs in ovarian cancer using integrated bioinformatics\" paragraph 2", "quote": "The two ovarian cancer gene expression microarray datasets GSE105437 and GSE12470 were analyzed using the R limma package and were sorted according to logfold-change values with corrected p-values <0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data is available at NCBI GEO: GSE105437, GSE66957, GSE66387, GSE40595, GSE29450, GSE29156, GSE27651, GSE26712, GSE18521, GSE18520, GSE17308, and GSE12470.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33604191", "locator": "sections[16] \"Additional Information and Declarations\" paragraph 4", "quote": "Data is available at NCBI GEO: GSE105437, GSE66957, GSE66387, GSE40595, GSE29450, GSE29156, GSE27651, GSE26712, GSE18521, GSE18520, GSE17308, and GSE12470."}], "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:33604191"], "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:GSE105437"], "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:GSE105437"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33604191", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.10817", "sha256": 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