Bioinformatics analysis on enrichment analysis of potential hub genes in breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35116555", "kind": "science-record", "revision": 5, "content_hash": "sha256:cb10995b177720bb31a3c58d7b082fb909524a62ddf95c38fadefa8270215979", "created_at": "2026-11-01T17:36:51Z", "updated_at": "2026-11-01T22:48:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analysis on enrichment analysis of potential hub genes in breast cancer.", "doi": "10.21037/tcr-21-749", "pmid": "35116555", "authors": ["Limin Wei", "Yukun Wang", "Dan Zhou", "Xinyang Li", "Ziming Wang", "Ge Yao", "Xinshuai Wang"], "journal": "Translational cancer research", "year": 2021, "abstract": "BACKGROUND: Despite recent advances in screening, treatment, and survival, breast cancer remains the most invasive cancer in women. The development of novel diagnostic and therapeutic markers for breast cancer may provide more information about its pathogenesis and progression.\nMETHODS: We obtained GSE86374 micro-expression matrix chip data from the Gene Expression Omnibus (GEO) database consisting of 159 samples (124 normal samples and 35 breast cancer samples). The language was then used to perform data processing and differential expression analysis. For all differentially expressed genes (DEGs), \"FDR <0.01 and |logFC| ≥1\" were selected as thresholds.\nRESULTS: In this study, 173 up-regulated genes and 143 down-regulated genes were selected for GO and KEGG enrichment analysis. These genes are also significantly enriched in the KEGG pathway, including phenylalanine metabolism, staphylococcus aureus infection, and the PPAR signaling pathway. The survival and prognosis of the selected eight key genes (DLGAP5, PRC1, TOP2A, CENPF, RACGAP1, RRM2, PLK1, and ASPM) were analyzed by the Kaplan-Meier plotter database.\nCONCLUSIONS: Eight hub genes and pathways closely related to the onset and progression of breast cancer were identified. We found that the PPAR signaling pathway, especially PPARγ, plays an important role in breast cancer and suggest this pathway be the subject of further research.", "claims": [{"id": "claim-method", "statement": "Based on the \"Limma\" package, we used empirical Bayesian method to screen for significantly different genes, then used the \"hugene10sttranscriptcluster\" package to annotate DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35116555", "locator": "sections[4] \"Identification of DEGs\" paragraph 2", "quote": "Based on the \"Limma\" package, we used empirical Bayesian method to screen for significantly different genes, then used the \"hugene10sttranscriptcluster\" package to annotate DEGs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded GSE86374, a breast cancer-related dataset submitted by Rebollar-Vega R and based on the GPL6244 platform ((HuGene-1_0-st) Affymetrix Human Gene 1.0 ST Array), from the GEO database, comprising 124 breast cancer samples and 35 normal samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35116555", "locator": "sections[3] \"Acquisition of microarray data\" paragraph 3", "quote": "We downloaded GSE86374, a breast cancer-related dataset submitted by Rebollar-Vega R and based on the GPL6244 platform ((HuGene-1_0-st) Affymetrix Human Gene 1.0 ST Array), from the GEO database, comprising 124 breast cancer samples and 35 normal samples."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:35116555"], "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:GSE86374"], "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:GSE86374"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35116555", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-21-749", "sha256": 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