Targeting FCRLA to induce necrosis in lung adenocarcinoma: a novel strategy for prognosis and therapy via MPT-Driven pathways.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40881693", "kind": "science-record", "revision": 1, "content_hash": "sha256:977df69d7f0e87d4c92c97d7a13bf3958d23531eb53db8e717128c1709072665", "created_at": "2026-11-01T10:25:52Z", "updated_at": "2026-11-01T10:25:52Z", "generator": {"name": "corpus-assay-engine", "converter": "sep_to_sci_v1", "source_package_schema": "urn:bntrg:sep:1.0.0"}, "paper": {"state": "partial", "title": "Targeting FCRLA to induce necrosis in lung adenocarcinoma: a novel strategy for prognosis and therapy via MPT-Driven pathways.", "doi": "10.3389/fimmu.2025.1596179", "pmid": "40881693", "authors": null, "journal": "Frontiers in immunology", "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": 82.0, "unit": "candidate genes", "tolerance": null, "source": "chars 1069-1194 of source sha256 1fef0886b20a2ad5c3044325a71b884c423ed91a82dbade59bfd6a342b915052"}, "evidence": [{"asset": "source:pmid:40881693", "locator": "chars 1069-1194 of source sha256 1fef0886b20a2ad5c3044325a71b884c423ed91a82dbade59bfd6a342b915052", "quote": "A total of 82 candidate genes were identified by intersecting 3,231 differentially expressed genes with 566 key module genes."}]}], "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:40881693"], "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:40881693", "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:40881693' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40881693", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2025.1596179", "sha256": "sha256:1fef0886b20a2ad5c3044325a71b884c423ed91a82dbade59bfd6a342b915052", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2025.1596179", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}], "gaps": ["asset:source:pmid:40881693 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:40881693"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40881693"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "19d2f9eb313a7694b2d82919cc6466fc737e7adaf7fa139a768bffa444489105", "source_hash": "1fef0886b20a2ad5c3044325a71b884c423ed91a82dbade59bfd6a342b915052", "raw_response_sha256": "sha256:ec3eac51f2a8e678989c1aa3225272065c3fd20c5103b4fe091a9705cf182ea7", "raw_response_chars": 5965}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "e40961a78616b5d918456c421c2565a9fb5c4b866143ec8fa9f999706252dad0", "source_hash": "1fef0886b20a2ad5c3044325a71b884c423ed91a82dbade59bfd6a342b915052", "raw_response_sha256": "sha256:a70b556e77154b4800a0493c4adaf5e67b4fa6641721766aaf6b95c134e008d6", "raw_response_chars": 71660}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "a2631e9a93f9264347ae7f6f8428f92b79976e0bdb0dc28491b9d1ba0704a6b0", "source_hash": "1fef0886b20a2ad5c3044325a71b884c423ed91a82dbade59bfd6a342b915052", "raw_response": "{\"parent_value\":82,\"unit\":\"candidate genes\",\"evidence\":\"A total of 82 candidate genes were identified by intersecting 3,231 differentially expressed genes with 566 key module genes.\",\"target_result\":\"The paper identifies 82 candidate genes.\",\"number_stated\":true}", "reading": {"parent_value": 82, "unit": "candidate genes", "evidence": "A total of 82 candidate genes were identified by intersecting 3,231 differentially expressed genes with 566 key module genes.", "target_result": "The paper identifies 82 candidate genes.", "number_stated": true, "source_locator": "chars 1069-1194 of source sha256 1fef0886b20a2ad5c3044325a71b884c423ed91a82dbade59bfd6a342b915052"}, "recorded_at": "2026-10-31T15:33:46.790010+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "c7c47059be149652097dc169c9f8c29448f3b894258a8fed6b73c444279b45ca", "source_hash": "1fef0886b20a2ad5c3044325a71b884c423ed91a82dbade59bfd6a342b915052", "raw_response": "<think>We need answer JSON only. Need interpret target: headline numerical result paper states for main finding, single numerical result replication registered target reproduce. Source excerpt abstract/results. Numbers: 82 candidate genes; 3,231 DEGs; 566 module genes; three prognostic genes. What is \"headline numerical result\" for main finding? Need decide. The registered target vague: \"the headline numerical result the paper states for its main finding\". Need extract likely number 82 candidate genes. It says \"A total of 82 candidate genes were identified...\" Main quantitative result. Need report value and unit paper prints. Unit likely \"candidate genes\". Exact contiguous quote containing number, perhaps whole sentence. parent_value 82, unit \"candidate genes\", evidence exact sentence. target_result perhaps \"82 candidate genes\". number_stated true.\n\nBut inspect paper title / abstract. Main finding FCRLA mechanism qualitative. Headline numerical result maybe 82 candidate genes identified by intersecting 3,231 DEGs with 566 key module genes. User asks single numerical result that replication registered target has to reproduce. Could perhaps ask \"headline number\" from abstract, likely 82 candidate genes. Need use exact unit printed—\"candidate genes\". Evidence exact contiguous quote: \"A total of 82 candidate genes were identified by intersecting 3,231 differentially expressed genes with 566 key module genes.\" This contains other numbers, but parent headline 82. They insist exact quote, do not shorten, whole clause attribution. That's fine. Could quote full sentence. target_result maybe \"A total of 82 candidate genes were identified.\" Yet evidence sufficient. They ask headline number only, unit must paper prints. JSON target_result likely perhaps concise \"82 candidate genes\". Need no extra.\n\nCould unit be \"candidate genes\" vs \"genes\"? Exact printed unit. Probably \"candidate genes\". The quote includes 3,231 etc though \"headline number only\" refers output, evidence naturally can include others. Maybe exact quote just \"A total of 82 candidate genes were identified\" is contiguous and contains attribution. It is exact contiguous quotation, not necessarily whole sentence? User says \"When that is how the text links them, quote the whole clause so a reader can check attribution.\" \"A total of 82 candidate genes were identified\" is clause (no explicit target? It states candidate genes). Could quote full sentence to be safe. target_result perhaps \"82 candidate genes\". Let's ensure JSON valid, no markdown.</think>\n\n{\"parent_value\":82,\"unit\":\"candidate genes\",\"evidence\":\"A total of 82 candidate genes were identified by intersecting 3,231 differentially expressed genes with 566 key module genes.\",\"target_result\":\"82 candidate genes\",\"number_stated\":true}", "reading": {"parent_value": 82, "unit": "candidate genes", "evidence": "A total of 82 candidate genes were identified by intersecting 3,231 differentially expressed genes with 566 key module genes.", "target_result": "82 candidate genes", "number_stated": true, "source_locator": "chars 1069-1194 of source sha256 1fef0886b20a2ad5c3044325a71b884c423ed91a82dbade59bfd6a342b915052"}, "recorded_at": "2026-10-31T15:33:49.981481+00:00"}], "agreement": false, "disagreements": {"physical_methods": {"minimax": ["An FCRLA knockdown cell model was constructed in vitro using LUAD cells.", "Cell-related phenotypic experiments, including proliferation and apoptosis, were performed.", "The integrity of the mitochondrial structure was observed using electron microscopy.", "The mitochondrial membrane potential was detected using a JC-1 probe."], "minimax_alt": ["In vitro FCRLA knockdown cell model constructed in LUAD cells", "Cell proliferation and apoptosis phenotypic experiments", "Electron microscopy to assess mitochondrial structural integrity", "JC-1 probe assay of mitochondrial membrane potential"], "decisive": false}, "analysis_methods": {"minimax": ["Differential expression analysis using DESeq2", "Weighted gene co-expression network analysis (WGCNA)", "Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses", "Protein–protein interaction network analysis using STRING", "Univariate and multivariate Cox regression", "Kaplan–Meier survival analysis", "Gene Set Enrichment Analysis (GSEA)", "Gene Set Variation Analysis (GSVA)", "Wilcoxon test", "Kruskal–Wallis test", "Spearman's correlation analysis", "One-way analysis of variance with Tukey's post-hoc significant difference test"], "minimax_alt": ["DESeq2 (version 1.40.2) differential expression analysis", "Weighted gene co-expression network analysis (WGCNA)", "Eulerr (version 7.0.0) intersection of DEGs and key module genes", "Single-sample gene set enrichment analysis (GSVA)", "Wilcoxon test", "Pearson’s correlation analysis", "Kaplan–Meier (KM) survival analysis", "Univariate Cox analysis and multivariate Cox analysis", "Nomogram model with ROC, calibration, and decision curve analysis (DCA)", "Kruskal–Wallis test", "Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses", "Gene Set Enrichment Analysis (GSEA)", "limma differential analysis", "Spearman’s correlation analysis", "One-way analysis of variance with Tukey’s post-hoc significant difference test"], "decisive": false}, "statistical_method": {"minimax": "Differential expression analysis and weighted gene co-expression network analysis (WGCNA)", "minimax_alt": "DESeq2-based differential expression analysis and weighted gene co-expression network analysis (WGCNA), followed by intersection of DEGs and key module genes", "decisive": true}}, "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": []}}, {"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:cb6507144d79e76fad625eb25abc9548d9bccf0cf0e0ab9090b7db25aa0c16de", "type": "sep_package", "label": "SEP v1 package for pmid:40881693"}], "activities": [{"id": "activity:conversion:pmid:40881693", "type": "conversion", "used": ["package:cb6507144d79e76fad625eb25abc9548d9bccf0cf0e0ab9090b7db25aa0c16de"], "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": "c7c74b74-0ddc-4b4b-b66e-1dae77a98232", "parent_hash": "cb6507144d79e76fad625eb25abc9548d9bccf0cf0e0ab9090b7db25aa0c16de", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40881693 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:40881693' does not resolve to a declared asset", "targets"]}