Unveiling Novel miRNA-mRNA Interactions and Their Prognostic Roles in Triple-Negative Breast Cancer: Insights into miR-210, miR-183, miR-21, and miR-181b.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40076546", "kind": "science-record", "revision": 1, "content_hash": "sha256:035e5b5b11a322e324fe09d24c93cf6fd47a93109f9c94df6df298d2ac0fde2f", "created_at": "2026-11-01T10:25:49Z", "updated_at": "2026-11-01T10:25:49Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Unveiling Novel miRNA-mRNA Interactions and Their Prognostic Roles in Triple-Negative Breast Cancer: Insights into miR-210, miR-183, miR-21, and miR-181b.", "doi": "10.3390/ijms26051916", "pmid": "40076546", "authors": null, "journal": "International journal of molecular sciences", "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": 19.0, "unit": "validated mirna–mrna pairs", "tolerance": null, "source": "chars 918-1414 of source sha256 a48e1fd8848f457da3744ad11787733601df83841ae97c3dfebe011e3b951f0a"}, "evidence": [{"asset": "source:pmid:40076546", "locator": "chars 918-1414 of source sha256 a48e1fd8848f457da3744ad11787733601df83841ae97c3dfebe011e3b951f0a", "quote": "A prognostic model constructed based on the top 10 significant MTI pairs using random forest combination effectively classified patient survival outcomes in both TCGA and independent dataset GSE19783 cohorts, demonstrating good predictive accuracy and valuable prognostic insights for TNBC patients. Further analysis uncovered a complex network of 71 coherent feed-forward loops involving transcription factors, miRNAs, and target genes, shedding light on the mechanisms driving TNBC progression."}]}], "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:40076546"], "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:40076546", "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:40076546' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40076546", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/ijms26051916", "sha256": "sha256:a48e1fd8848f457da3744ad11787733601df83841ae97c3dfebe011e3b951f0a", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/ijms26051916", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}], "gaps": ["asset:source:pmid:40076546 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:40076546"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40076546"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "passed", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "b83113ef75d871d89ec1ba171693d46e60d9d2270564ff210852af6a8401d585", "source_hash": "a48e1fd8848f457da3744ad11787733601df83841ae97c3dfebe011e3b951f0a", "raw_response_sha256": "sha256:70df5b849baae4a9f85a7fe1922a2fd25150f41b1612b96573c8d6ffeef9d25d", "raw_response_chars": 5332}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "ba13777d17c1b83da41f5e7f1f155ff261ad3754721484225f9d248480adcf69", "source_hash": "a48e1fd8848f457da3744ad11787733601df83841ae97c3dfebe011e3b951f0a", "raw_response_sha256": "sha256:7200daaffaff479beb0c967b2f1993afbc32b34d1e463e0709e2d9c6fe107138", "raw_response_chars": 50699}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "34f8c159ddd5f87635fecce0f1cb4f675a9de2f6142578c5662a306277d294d6", "source_hash": "a48e1fd8848f457da3744ad11787733601df83841ae97c3dfebe011e3b951f0a", "raw_response": "{\"parent_value\":19,\"unit\":\"validated miRNA–mRNA pairs\",\"evidence\":\"A prognostic model constructed based on the top 10 significant MTI pairs using random forest combination effectively classified patient survival outcomes in both TCGA and independent dataset GSE19783 cohorts, demonstrating good predictive accuracy and valuable prognostic insights for TNBC patients. Further analysis uncovered a complex network of 71 coherent feed-forward loops involving transcription factors, miRNAs, and target genes, shedding light on the mechanisms driving TNBC progression.\",\"target_result\":\"The paper states that four key miRNAs targeting eight downstream genes formed a novel regulatory network of 19 validated miRNA–mRNA pairs.\",\"number_stated\":true}", "reading": {"parent_value": 19, "unit": "validated miRNA–mRNA pairs", "evidence": "A prognostic model constructed based on the top 10 significant MTI pairs using random forest combination effectively classified patient survival outcomes in both TCGA and independent dataset GSE19783 cohorts, demonstrating good predictive accuracy and valuable prognostic insights for TNBC patients. Further analysis uncovered a complex network of 71 coherent feed-forward loops involving transcription factors, miRNAs, and target genes, shedding light on the mechanisms driving TNBC progression.", "target_result": "The paper states that four key miRNAs targeting eight downstream genes formed a novel regulatory network of 19 validated miRNA–mRNA pairs.", "number_stated": true, "source_locator": "chars 918-1414 of source sha256 a48e1fd8848f457da3744ad11787733601df83841ae97c3dfebe011e3b951f0a"}, "recorded_at": "2026-10-31T15:33:52.051323+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "04b829eee5df25129c7723fee9a9f80be3497a98e44cf6ec41387c8e2a09aee1", "source_hash": "a48e1fd8848f457da3744ad11787733601df83841ae97c3dfebe011e3b951f0a", "raw_response": "<think>We need answer JSON object only. Need determine single numerical result replication registered target headline numerical result main finding. Source abstract contains several numbers: four key miRNAs, eight genes, network 19 validated pairs, model top 10 MTI, 71 loops. Headline likely 19 validated miRNA–mRNA pairs? Asked \"single numerical result ... headline numerical result paper states for its main finding\" likely number is 19, unit \"validated miRNA–mRNA pairs\". Need exact contiguous quote containing number, likely sentence: \"Using this method, we identified four key miRNAs ... targeting eight downstream target genes, forming a novel regulatory network of 19 validated miRNA–mRNA pairs.\" This clause attaches 19 directly to pairs. Parent_value 19, unit paper prints perhaps \"validated miRNA–mRNA pairs\". Evidence exact clause or sentence. They say attribution may be carried by parenthetical list, respectively, or words naming target directly; quote whole clause so reader check. We can quote full sentence. target_result likely \"19 validated miRNA–mRNA pairs\". number_stated true.\n\nNeed think if headline numerical result might be 71 coherent feed-forward loops? Title/main finding perhaps \"novel network of 19 validated...\" Main abstract's result includes 19 pairs, then 71 loops. \"headline numerical result the paper states for its main finding\" Singular likely 19. Replication target perhaps registered target vague. Need select.\n\nCould target_result explain. Unit must unit paper prints. \"validated miRNA–mRNA pairs\" isn't a conventional unit but directly printed. parent_value 19.\n\nEvidence exact contiguous quotation. Full sentence exactly, Unicode characters. Ensure valid JSON escapes only quote. Could use en dashes. No markdown.\n\nLet's inspect wording: \"Using this method, we identified four key miRNAs (miR-181b-5p, miR-21-5p, miR-210-3p, miR-183-5p) targeting eight downstream target genes, forming a novel regulatory network of 19 validated miRNA–mRNA pairs.\" Exact. Quote whole clause perhaps this sentence. It includes competing numbers but attribution clear. They request \"single numerical result\" perhaps evidence can contain other nums. Maybe quote clause only: \"forming a novel regulatory network of 19 validated miRNA–mRNA pairs.\" That's exact contiguous and contains number, with target directly. They say do not shorten it perhaps means evidence exact quote not truncated. \"When that is how text links them, quote the whole clause so a reader can check attribution.\" Whole clause \"forming...\" works. But clause references prior. Yet direct. Better full sentence, though includes four/eight. Does that violate single result? No, evidence can. \"do not shorten it\" means don't alter quote likely. Exact full sentence safest.\n\ntarget_result string could state \"The paper reports a novel regulatory network of 19 validated miRNA–mRNA pairs.\" Unit \"validated miRNA–mRNA pairs.\" Should unit include count? No.\n\nLet's ensure output only object and valid JSON.</think>\n\n{\"parent_value\":19,\"unit\":\"validated miRNA–mRNA pairs\",\"evidence\":\"Using this method, we identified four key miRNAs (miR-181b-5p, miR-21-5p, miR-210-3p, miR-183-5p) targeting eight downstream target genes, forming a novel regulatory network of 19 validated miRNA–mRNA pairs.\",\"target_result\":\"The paper reports a novel regulatory network of 19 validated miRNA–mRNA pairs.\",\"number_stated\":true}", "reading": {"parent_value": 19, "unit": "validated miRNA–mRNA pairs", "evidence": "Using this method, we identified four key miRNAs (miR-181b-5p, miR-21-5p, miR-210-3p, miR-183-5p) targeting eight downstream target genes, forming a novel regulatory network of 19 validated miRNA–mRNA pairs.", "target_result": "The paper reports a novel regulatory network of 19 validated miRNA–mRNA pairs.", "number_stated": true, "source_locator": "chars 710-917 of source sha256 a48e1fd8848f457da3744ad11787733601df83841ae97c3dfebe011e3b951f0a"}, "recorded_at": "2026-10-31T15:33:56.935900+00:00"}], "agreement": true, "disagreements": {"target_result": {"minimax": "A prognostic model based on the top 10 significant miRNA–target interaction pairs, using random forest combination, classified patient survival outcomes in the TCGA and GSE19783 cohorts.", "minimax_alt": "The main finding was a network of 19 validated miRNA–mRNA pairs involving four key miRNAs targeting eight downstream target genes; 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