Identification of biomarkers and potential therapeutic targets of breast cancer using bioinformatics analysis.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40826700", "kind": "science-record", "revision": 2, "content_hash": "sha256:6e65052c7755ca1102f3e53383f1ae8d3b51e40fd9a214f1d9228aaa2f1aae42", "created_at": "2026-11-01T10:25:52Z", "updated_at": "2026-11-01T14:01:46Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Identification of biomarkers and potential therapeutic targets of breast cancer using bioinformatics analysis.", "doi": "10.1097/md.0000000000043973", "pmid": "40826700", "authors": null, "journal": "Medicine", "year": 2025, "abstract": null, "claims": [{"id": "claim-1", "statement": "the headline numerical result the paper states for its main finding", "kind": "quantitative", "state": "complete", "target": {"value": 4.0, "unit": "potential biomarkers", "tolerance": null, "source": "chars 4572-4672 of source sha256 f75f271b744a25d8c199261cfabab399c9ce4b7cec3527ec4bebd4617ad0600b"}, "evidence": [{"asset": "source:pmid:40826700", "locator": "chars 4572-4672 of source sha256 f75f271b744a25d8c199261cfabab399c9ce4b7cec3527ec4bebd4617ad0600b", "quote": "Our present study identifies 4 potential biomarkers for BRCA including AURKA, DTL, NUSAP1, and RRM2."}]}], "gaps": ["abstract", "authors"]}, "experiment": {"state": "partial", "objective": "the headline numerical result the paper states for its main finding", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate and its interval from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The number a replication must land on, and the sentence it came from.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40826700"], "outputs": ["the published estimate, its unit and its interval"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data behind the published result", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:not_downloaded", "description": "The published result cannot be recomputed without the data the paper analysed.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the analysis programme", "kind": "analysis", "state": "blocked", "blocked_on": "no owned computational capability is bound to this experiment", "description": "Re-run the paper's analysis on the source data and produce the registered result. | SEP input not carried into the asset list: materials:the source data behind the published result in pmid:40826700", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the registered result"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:40826700' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40826700", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1097/md.0000000000043973", "sha256": "sha256:f75f271b744a25d8c199261cfabab399c9ce4b7cec3527ec4bebd4617ad0600b", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1097/md.0000000000043973", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}, {"id": "asset:table:pmid:40826700:1", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:bc47dccc4d454b1a893d407548b2711c4bfd7e0a1ff7cec85fde790cf7d0e6e4", "bytes": 591, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[10]/table-wrap[1]#T1", "version": null, "note": "Primers used for RT-qPCR in this study.", "structure": {"pmcid": "PMC12366978", "section_index": 10, "section_title": "2.8. Hub genes validation by reverse transcription quantitative polymerase chain reaction (RT-qPCR)", "row_count": 7, "column_count": 3}, "table": {"label": "Table 1", "caption": "Primers used for RT-qPCR in this study.", "rows": [["Gene", "Forward primer (5′–3′)", "Reverse primer (5′–3′)"], ["GAPDH", "TGCACCACCAACTGCTTAGC", "GGCATGGACTGTGGTCATGAG"], ["AURKA", "TCTTCCCAGCGCATTCCTTT", "TGGTTGCCTGCAATTGCTTC"], ["DTL", "TCTCCTCCGTCTCTCCCAAG", "GCTTGGGATGACTTCTGGCT"], ["NUSAP1", "GTGTGGACCCTGACTCACAG", "CTTCTTGGTGCTCGTCTGGT"], ["RRM2", "AAGAAGAAGGCAGACTGGGC", "CCAGGCATCAGTCCTCGTTT"], ["FGF2", "GCTGTACTGCAAAAACGGGG", "AGCCAGGTAACGGTTAGCAC"]], "row_count": 7, "column_count": 3, "numeric_cells": 2, "header_rows": 1, "locator": "section[10]/table-wrap[1]#T1"}}, {"id": "asset:table:pmid:40826700:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:0288bfd01f244b2b0bf332f5dc619d65560aa16ed7f53e3d121064c38799f1bb", "bytes": 1006, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[12]/table-wrap[1]#T2", "version": null, "note": "The information of the overlapping differentially expressed genes (DEGs).", "structure": {"pmcid": "PMC12366978", "section_index": 12, "section_title": "3.1. Identification of DEGs", "row_count": 11, "column_count": 11}, "table": {"label": "Table 2", "caption": "The information of the overlapping differentially expressed genes (DEGs).", "rows": [["Category", "Gene symbol"], ["Upregulated DEGs", "COL1A1", "FN1", "POSTN", "BGN", "GPRC5A", "MMP11", "CEACAM6", "COL11A1", "TOP2A", "COL10A1"], ["COMP", "ISG15", "HN1", "SQLE", "INHBA", "PAFAH1B3", "RRM2", "S100P", "BIRC5", "CDK1"], ["NUSAP1", "CDC20", "MMP1", "ASPM", "DTL", "KIF20A", "NEK2", "AURKA", "CEP55", "MELK"], ["HMMR", "SOX11", "CDCA3", "", "", "", "", "", "", ""], ["Downregulated DEGs", "SFRP1", "CD36", "ANXA1", "FABP4", "ADH1B", "CAV1", "FHL1", "LPL", "PLIN1", "CRYAB"], ["FOSB", "SORBS1", "NTRK2", "TGFBR3", "ACACB", "MME", "CFD", "ITIH5", "ENPP2", "CHRDL1"], ["MAOA", "CAV2", "SEMA3G", "LMOD1", "LEP", "TF", "ITM2A", "ITGA7", "DMD", "RBP4"], ["PPP1R1A", "AKR1C3", "CSN1S1", "LDB2", "IL33", "ABCA8", "SPRY2", "PPARG", "FGF2", "S100B"], ["EDNRB", "CHL1", "HLF", "ATP1A2", "TIMP4", "PCK1", "MEOX2", "CXCL2", "MT1M", "TNMD"], ["HSPB2", "", "", "", "", "", "", "", "", ""]], "row_count": 11, "column_count": 11, "numeric_cells": 12, "header_rows": 1, "locator": "section[12]/table-wrap[1]#T2"}}, {"id": "asset:table:pmid:40826700:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:13db5716e375cc8fadee5700dd176e3dd89fc2404f86f342e105cae0b78d9f13", "bytes": 2783, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[13]/table-wrap[1]#T3", "version": null, "note": "Gene ontology analysis of overlapping DEGs in breast cancer.", "structure": {"pmcid": "PMC12366978", "section_index": 13, "section_title": "3.2. Functional enrichment", "row_count": 34, "column_count": 5}, "table": {"label": "Table 3", "caption": "Gene ontology analysis of overlapping DEGs in breast cancer.", "rows": [["GO analysis", "Term", "Gene count", "%", "P value"], ["GOTERM_BP_DIRECT", "GO:0051301~cell division", "7", "28", "5.30E−06"], ["GOTERM_BP_DIRECT", "GO:0090307~mitotic spindle assembly", "4", "16", "2.64E−05"], ["GOTERM_BP_DIRECT", "GO:0000086~G2/M transition of mitotic cell cycle", "4", "16", "3.17E−05"], ["GOTERM_BP_DIRECT", "GO:0000281~mitotic cytokinesis", "4", "16", "8.56E−05"], ["GOTERM_BP_DIRECT", "GO:0042593~glucose homeostasis", "4", "16", "3.48E−04"], ["GOTERM_BP_DIRECT", "GO:0009617~response to bacterium", "4", "16", "3.95E−04"], ["GOTERM_BP_DIRECT", "GO:0010629~negative regulation of gene expression", "5", "20", "4.95E−04"], ["GOTERM_BP_DIRECT", "GO:0000278~mitotic cell cycle", "4", "16", "7.45E−04"], ["GOTERM_BP_DIRECT", "GO:0006468~protein phosphorylation", "5", "20", "0.001019056"], ["GOTERM_BP_DIRECT", "GO:0014823~response to activity", "3", "12", "0.001675663"], ["GOTERM_BP_DIRECT", "GO:0006629~lipid metabolic process", "4", "16", "0.00201127"], ["GOTERM_BP_DIRECT", "GO:0071300~cellular response to retinoic acid", "3", "12", "0.003254841"], ["GOTERM_BP_DIRECT", "GO:0048511~rhythmic process", "3", "12", "0.004124865"], ["GOTERM_CC_DIRECT", "GO:0030496~midbody", "7", "28", "6.54E−08"], ["GOTERM_CC_DIRECT", "GO:0005813~centrosome", "8", "32", "7.35E−06"], ["GOTERM_CC_DIRECT", "GO:0005876~spindle microtubule", "4", "16", "1.60E−05"], ["GOTERM_CC_DIRECT", "GO:0005819~spindle", "5", "20", "2.62E−05"], ["GOTERM_CC_DIRECT", "GO:0072686~mitotic spindle", "4", "16", "6.33E−04"], ["GOTERM_CC_DIRECT", "GO:0000776~kinetochore", "4", "16", "8.98E−04"], ["GOTERM_CC_DIRECT", "GO:0005829~cytosol", "15", "60", "0.001220631"], ["GOTERM_CC_DIRECT", "GO:0005737~cytoplasm", "14", "56", "0.005263969"], ["GOTERM_CC_DIRECT", "GO:0005874~microtubule", "4", "16", "0.005979865"], ["GOTERM_CC_DIRECT", "GO:0005811~lipid droplet", "3", "12", "0.006787893"], ["GOTERM_CC_DIRECT", "GO:0032133~chromosome passenger complex", "2", "8", "0.008016755"], ["GOTERM_CC_DIRECT", "GO:0000922~spindle pole", "3", "12", "0.01210966"], ["GOTERM_CC_DIRECT", "GO:0072687~meiotic spindle", "2", "8", "0.017103345"], ["GOTERM_CC_DIRECT", "GO:0005814~centriole", "3", "12", "0.017624847"], ["GOTERM_CC_DIRECT", "GO:0015630~microtubule cytoskeleton", "3", "12", "0.022337357"], ["GOTERM_MF_DIRECT", "GO:0106310~protein serine kinase activity", "4", "16", "0.010182819"], ["GOTERM_MF_DIRECT", "GO:0004674~protein serine/threonine kinase activity", "4", "16", "0.011432112"], ["GOTERM_MF_DIRECT", "GO:0005524~ATP binding", "6", "24", "0.038167489"], ["GOTERM_MF_DIRECT", "GO:0004672~protein kinase activity", "3", "12", "0.046022875"], ["GOTERM_MF_DIRECT", "GO:0008017~microtubule binding", "3", "12", "0.046022875"]], "row_count": 34, "column_count": 5, "numeric_cells": 132, "header_rows": 1, "locator": "section[13]/table-wrap[1]#T3"}}, {"id": "asset:table:pmid:40826700:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:6595dc97000e0282e8c52302df3a014efd06023860daf8813ab334b2e22943df", "bytes": 505, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[13]/table-wrap[2]#T4", "version": null, "note": "KEGG pathway analysis of overlapping DEGs in breast cancer.", "structure": {"pmcid": "PMC12366978", "section_index": 13, "section_title": "3.2. Functional enrichment", "row_count": 5, "column_count": 5}, "table": {"label": "Table 4", "caption": "KEGG pathway analysis of overlapping DEGs in breast cancer.", "rows": [["Category", "Term", "Gene count", "%", "P value"], ["KEGG pathway", "hsa03320:PPAR signaling pathway", "5", "20", "8.49E−06"], ["hsa04152:AMPK signaling pathway", "4", "16", "0.001259508"], ["hsa04920:Adipocytokine signaling pathway", "3", "12", "0.006904311"], ["hsa04114:Oocyte meiosis", "3", "12", "0.025501685"]], "row_count": 5, "column_count": 5, "numeric_cells": 16, "header_rows": 1, "locator": "section[13]/table-wrap[2]#T4"}}, {"id": "asset:table:pmid:40826700:5", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:90d14b1db013428b50fc3f037f69e48753d681e14e016bef45cc1ed19e1773ec", "bytes": 379, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[14]/table-wrap[1]#T5", "version": null, "note": "Top 20 hub genes with degree >10.", "structure": {"pmcid": "PMC12366978", "section_index": 14, "section_title": "3.3. PPI network construction", "row_count": 5, "column_count": 2}, "table": {"label": "Table 5", "caption": "Top 20 hub genes with degree >10.", "rows": [["Degree of connectivity", "Hub genes"], ["28", "NUSAP1, NEK2"], ["26", "CDK1, CDC20, KIF20A, CEP55, HMMR, RRM2, BIRC5, MELK, AURKA, ASPM, PPARG"], ["24", "FGF2, CDCA3, DTL, LEP"], ["18", "CAV1, LPL, FABP4"]], "row_count": 5, "column_count": 2, "numeric_cells": 5, "header_rows": 1, "locator": "section[14]/table-wrap[1]#T5"}}], "gaps": ["asset:source:pmid:40826700 not verified"]}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "targets"]}, "capability": {"state": "absent", "requirements": [], "gaps": ["capability"]}, "readiness": {"state": "partial", "tier": null, "blocking_axis": null, "blocked_on": null, "axes": {}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:40826700", "asset:table:pmid:40826700:1", "asset:table:pmid:40826700:2", "asset:table:pmid:40826700:3", "asset:table:pmid:40826700:4", "asset:table:pmid:40826700:5"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40826700"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "2b7ba834a6b80ca9d613c6f5b08a1211e17b4410a3fb7b5225bced2e6e76a2e1", "source_hash": "f75f271b744a25d8c199261cfabab399c9ce4b7cec3527ec4bebd4617ad0600b", "raw_response_sha256": "sha256:01a7ecc0829610805763271b9f0949772aea462992f90eb09cef8c4ff45e2e61", "raw_response_chars": 68944}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "c8a444f319a13e960e712ef3416856c883c3204edd89f0c4313a3c3b41d12a79", "source_hash": "f75f271b744a25d8c199261cfabab399c9ce4b7cec3527ec4bebd4617ad0600b", "raw_response": "{\"parent_value\":4,\"unit\":\"potential biomarkers\",\"evidence\":\"Our present study identifies 4 potential biomarkers for BRCA including AURKA, DTL, NUSAP1, and RRM2.\",\"target_result\":\"The paper identifies 4 potential biomarkers for BRCA: AURKA, DTL, NUSAP1, and RRM2.\",\"number_stated\":true}", "reading": {"parent_value": 4, "unit": "potential biomarkers", "evidence": "Our present study identifies 4 potential biomarkers for BRCA including AURKA, DTL, NUSAP1, and RRM2.", "target_result": "The paper identifies 4 potential biomarkers for BRCA: AURKA, DTL, NUSAP1, and RRM2.", "number_stated": true, "source_locator": "chars 4572-4672 of source sha256 f75f271b744a25d8c199261cfabab399c9ce4b7cec3527ec4bebd4617ad0600b"}, "recorded_at": "2026-10-31T15:31:17.539022+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "f6257a4d266590702813d52b3c807b411a6bf701b594040050ec744f5a4e5296", "source_hash": "f75f271b744a25d8c199261cfabab399c9ce4b7cec3527ec4bebd4617ad0600b", "raw_response": "<think>We need answer JSON object only. Need determine single numerical result replication registered target headline numerical result paper states for main finding. Need parse task. Need choose likely 4 potential biomarkers? \"headline numerical result\" perhaps paper's main finding: identifies 4 potential biomarkers for BRCA. Need report headline number only, value paper itself states for registered target, unit paper prints. Evidence exact contiguous quote containing number; attribution via wording. Registered target: \"the headline numerical result the paper states for its main finding\". Source conclusion: \"Our present study identifies 4 potential biomarkers for BRCA including AURKA, DTL, NUSAP1, and RRM2.\" Likely parent_value 4, unit maybe \"potential biomarkers\" (unit must unit paper prints). Need formulate JSON. Could evidence quote exact sentence or clause. Unit likely \"potential biomarkers\". target_result perhaps \"4 potential biomarkers\" or sentence. Need infer expected extraction schema conventions. They ask \"Report the headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed. The unit must be the unit the paper prints. The evidence must be an exact, contiguous quotation from SOURCE TEXT that contains the number; do not shorten it, do not add an ellipsis and do not paraphrase it. Attribution may be carried by the text's own wording: a number may be attached to the target by a parenthetical list, by the word 'respectively', or by words that name the target directly. When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves. If the text states no number for the registered target, set number_stated to false, leave parent_value at 0, leave unit and evidence empty, and say in target_result what the text does state instead. Never invent a source.\"\n\nWe need decide whether headline numerical result could be \"4\" biomarkers, versus 84 DEGs, 20 hub genes, 5 etc. Registered target deliberately generic perhaps expected headline main finding from conclusion = 4 potential biomarkers. They likely want number 4 and unit \"potential biomarkers\". Evidence exact quote: \"Our present study identifies 4 potential biomarkers for BRCA including AURKA, DTL, NUSAP1, and RRM2.\" This contains number and target. Need perhaps quote whole clause. JSON:\n{\n\"parent_value\":4,\n\"unit\":\"potential biomarkers\",\n\"evidence\":\"Our present study identifies 4 potential biomarkers for BRCA including AURKA, DTL, NUSAP1, and RRM2.\",\n\"target_result\":\"4 potential biomarkers\",\n\"number_stated\":true\n}\n\"Report headline number only\" means parent_value numerical, unit. target_result likely describe. Could unit be \"biomarkers\" rather than \"potential biomarkers\"? The unit paper prints perhaps \"potential biomarkers for BRCA\". They may want unit \"potential biomarkers\". We need ensure exact quote no citation markers. Yes.\n\nCould target be 5 hub genes expression levels significantly different among subtypes? Main abstract says \"results showed that expression levels of these 5 hub genes...\" But conclusion explicitly \"identifies 4 potential biomarkers\". \"single numerical result that a replication of registered target has to reproduce.\" The registered target phrase main finding. Headline numerical result likely count final validated biomarkers = 4. Unit must be unit. Let's analyze source and wording deeply.\n\nTitle: Identification of biomarkers and potential therapeutic targets of breast cancer using bioinformatics analysis. Abstract gives several numbers:\n- 43,780 deaths, 15% mortality rate\n- across 3 datasets\n- total 84 DEGs, 33 up, 51 down\n- top 20 key genes\n- expression levels of these 5 hub genes significantly different among subtypes\n- four hub genes finally identified via RT-qPCR\nConclusion: identifies 4 potential biomarkers.\n\nQuestion likely generated benchmark where target is semantic \"the headline numerical result the paper states for its main finding\", requiring choose count of final biomarkers. The explicit conclusion is strongest. Could \"headline numerical result\" refer to abstract's headline: 4 hub genes finally identified. Yes.\n\nNeed only JSON, valid. Unit perhaps \"potential biomarkers\" and target_result perhaps \"4 potential biomarkers for BRCA\". \"headline number only\" could mean don't include gene list in target_result? Evidence must exact full clause perhaps sentence. They ask report headline number only likely no other values. We can give evidence full sentence but includes no other numeric except 4. Good.\n\nCould evidence quote only clause: \"Our present study identifies 4 potential biomarkers for BRCA including AURKA, DTL, NUSAP1, and RRM2.\" exact. \"do not shorten it\" perhaps quote whole clause. That's exact sentence. Unit \"potential biomarkers\" is printed. target_result perhaps \"The paper identifies 4 potential biomarkers for BRCA.\" But they said report headline number only, perhaps target_result should be \"4 potential biomarkers for BRCA\".</think>\n\n{\"parent_value\":4,\"unit\":\"potential biomarkers\",\"evidence\":\"Our present study identifies 4 potential biomarkers for BRCA including AURKA, DTL, NUSAP1, and RRM2.\",\"target_result\":\"4 potential biomarkers for BRCA\",\"number_stated\":true}", "reading": {"parent_value": 4, "unit": "potential biomarkers", "evidence": "Our present study identifies 4 potential biomarkers for BRCA including AURKA, DTL, NUSAP1, and RRM2.", "target_result": "4 potential biomarkers for BRCA", "number_stated": true, "source_locator": "chars 4572-4672 of source sha256 f75f271b744a25d8c199261cfabab399c9ce4b7cec3527ec4bebd4617ad0600b"}, "recorded_at": "2026-10-31T15:31:23.746345+00:00"}], "agreement": false, "disagreements": [], "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "blocked", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "reason": "a quoted statement is absent from the supplied text", "raw_response": "{\"target_result\":\"4 potential biomarkers for BRCA: AURKA, DTL, NUSAP1, and RRM2\",\"work_type\":\"analysis_plus_physical_validation\",\"physical_methods\":[\"reverse transcription quantitative polymerase chain reaction\"],\"analysis_methods\":[\"Venn diagram analysis\",\"Gene Ontology enrichment analysis\",\"KEGG pathway enrichment analysis\",\"STRING protein-protein interaction network analysis\",\"Molecular Complex Detection\",\"CytoHubba\",\"Kaplan–Meier survival analysis\",\"GEPIA validation\",\"UALCAN validation\"],\"statistical_method\":\"Kaplan–Meier survival analysis\",\"method_evidence\":\"The survival analysis for the selected genes was conducted using the Kaplan–Meier Plotter online tool (https://kmplot.com/analysis/).\",\"repositories\":[{\"repository\":\"Gene Expression Omnibus\",\"accession\":\"GSE42568 and GSE5364\",\"data_description\":\"Gene expression datasets for BRCA used to identify differentially expressed genes.\",\"evidence\":\"The GSE42568 and GSE5364 datasets were obtained from the Gene Expression Omnibus database, and the Cancer Genome Atlas BRCA dataset was sourced from the Xena functional genomics platform.\",\"publicly_downloadable\":false},{\"repository\":\"Xena functional genomics platform\",\"accession\":\"Cancer Genome Atlas BRCA dataset\",\"data_description\":\"BRCA gene expression data from the Cancer Genome Atlas used in the differential-expression analysis.\",\"evidence\":\"The GSE42568 and GSE5364 datasets were obtained from the Gene Expression Omnibus database, and the Cancer Genome Atlas BRCA dataset was sourced from the Xena functional genomics platform.\",\"publicly_downloadable\":false}],\"availability_statement\":\"The paper states that GSE42568 and GSE5364 were obtained from the Gene Expression Omnibus database and that the Cancer Genome Atlas BRCA dataset was sourced from the Xena functional genomics platform, but it does not explicitly state whether the underlying data can be downloaded without an application, embargo, or request.\",\"data_on_request\":false,\"source_claims\":[\"A total of 84 overlapping differentially expressed genes were preliminarily screened, including 33 upregulated and 51 downregulated genes.\",\"The top 20 highly connected key genes were identified using Cytoscape software.\",\"Four hub genes (AURKA, DTL, NUSAP1, and RRM2) were identified through reverse transcription quantitative polymerase chain reaction.\",\"All four genes exhibited high expression levels in BRCA tissues compared with normal tissues.\",\"Patients with higher expression of AURKA, DTL, NUSAP1, or RRM2 had shorter overall survival, whereas patients with higher FGF2 expression had longer overall survival.\",\"The study identifies four potential biomarkers for BRCA and possible new treatment targets.\"],\"missing_information\":[\"The paper does not explicitly state that the repository data can be downloaded without an application, embargo, or request.\",\"The paper does not explicitly state that the repository data are available on request.\",\"The source text does not provide a separate statistical test for the tissue-expression comparison of the four genes.\"],\"evidence_spans\":[\"The GSE42568 and GSE5364 datasets were obtained from the Gene Expression Omnibus database, and the Cancer Genome Atlas BRCA dataset was sourced from the Xena functional genomics platform.\",\"A total of 84 overlapping differentially expressed genes were preliminarily screened at the initial stage, including 33 upregulated and 51 downregulated genes.\",\"The top 20 highly connected key genes were identified subsequently using Cytoscape software.\",\"Four hub genes (AURKA, DTL, NUSAP1, and RRM2) were finally identified through reverse transcription quantitative polymerase chain reaction.\",\"When compared to normal tissues, all of these genes exhibit high expression levels in BRCA tissues.\",\"The results indicated that patients with higher expression levels of AURKA (HR [hazard ratio] = 1.65, P < .001), DTL (HR = 1.52, P < .001), NUSAP1 (HR = 1.65, P < .001), and RRM2 (HR = 1.57, P < .001) had shorter overall survival, whereas patients with higher Fibroblast Growth Factor 2 (FGF2) expression (HR = 0.8, P = .019) had longer overall survival (Fig. 6).\",\"Our present study identifies 4 potential biomarkers for BRCA including AURKA, DTL, NUSAP1, and RRM2.\"]}"}]}}, {"key": "source_resolution", "status": "pending", "reason": null, "evidence": null}, {"key": "protocol_reconstruction", "status": "pending", "reason": null, "evidence": null}, {"key": "control_execution", "status": "pending", "reason": null, "evidence": null}, {"key": "control_comparison", "status": "pending", "reason": null, "evidence": null}, {"key": "publication", "status": "pending", "reason": null, "evidence": null}], "gaps": ["review.human_critique", "review.machine_critique.blocked"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:307aa9a2ce955d64b7cba6ac161d2a087cd7c1e97131ece96be0448f73bc2865", "type": "sep_package", "label": "SEP v1 package for pmid:40826700"}], "activities": [{"id": "activity:conversion:pmid:40826700", "type": "conversion", "used": ["package:307aa9a2ce955d64b7cba6ac161d2a087cd7c1e97131ece96be0448f73bc2865"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:minimax", "type": "model", "label": "minimax"}, {"id": "agent:minimax_alt", "type": "model", "label": "minimax_alt"}, {"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}], "source_run_id": "run-20261023T122449232913", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40826700", "parent_hash": "sha256:1207e5621b4003205bee6c87d5a22a645ece1e82bea6c9fd08ccb53fae18e434", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:40826700 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.blocked", "step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:40826700' does not resolve to a declared asset", "targets"]}