A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40715243", "kind": "science-record", "revision": 1, "content_hash": "sha256:974be838cc872a47c01092bcb04f2983a6cc257783356d770ddc73f34773ae35", "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": "A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness.", "doi": "10.1038/s41598-025-11704-5", "pmid": "40715243", "authors": null, "journal": "Scientific reports", "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": 5.0, "unit": "protein kinases", "tolerance": null, "source": "chars 0-104 of source sha256 6a43fc52837af72578bbb8a4a8044a8f80871fd1b5d68513fe6cc49b7262d795"}, "evidence": [{"asset": "source:pmid:40715243", "locator": "chars 0-104 of source sha256 6a43fc52837af72578bbb8a4a8044a8f80871fd1b5d68513fe6cc49b7262d795", "quote": "A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness"}]}], "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:40715243"], "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:40715243", "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:40715243' does not resolve to a declared asset"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40715243", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-025-11704-5", "sha256": "sha256:6a43fc52837af72578bbb8a4a8044a8f80871fd1b5d68513fe6cc49b7262d795", "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-025-11704-5", "version": null, "note": "Source read by the package builder: full_text.", "structure": {"access_state": "full_text"}}], "gaps": ["asset:source:pmid:40715243 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:40715243"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:40715243"]}, "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": "5df9614e4a8dcc9ded0c7df0086c3802685fbdd499fedbdee34eac6318520e12", "source_hash": "6a43fc52837af72578bbb8a4a8044a8f80871fd1b5d68513fe6cc49b7262d795", "raw_response_sha256": "sha256:0102b427a09d1913053cf9a2e8fa103795b38e4990cb91e69781a304470af344", "raw_response_chars": 5071}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "69254dafb97966665d9afa734f2be552a9f51241bd162b76de5a9f38f1cdc115", "source_hash": "6a43fc52837af72578bbb8a4a8044a8f80871fd1b5d68513fe6cc49b7262d795", "raw_response_sha256": "sha256:4dd7d682349061e259304553c2e54307e42664fee3332e172dcc3e28212e7b42", "raw_response_chars": 64067}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "012081f3d75d04ad2e06337de9fedd3700555dcdd2ef1cceeb2213d727ea1226", "source_hash": "6a43fc52837af72578bbb8a4a8044a8f80871fd1b5d68513fe6cc49b7262d795", "raw_response": "{\"parent_value\":5,\"unit\":\"protein kinases\",\"evidence\":\"A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness\",\"target_result\":\"A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness\",\"number_stated\":true}", "reading": {"parent_value": 5, "unit": "protein kinases", "evidence": "A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness", "target_result": "A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness", "number_stated": true, "source_locator": "chars 0-104 of source sha256 6a43fc52837af72578bbb8a4a8044a8f80871fd1b5d68513fe6cc49b7262d795"}, "recorded_at": "2026-10-31T15:23:17.912406+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "ebd1a7b0cff9088ddaef1e5b7a15fc4f58651e7f22d75c4cf41d59eda125cb9d", "source_hash": "6a43fc52837af72578bbb8a4a8044a8f80871fd1b5d68513fe6cc49b7262d795", "raw_response": "<think>We need answer JSON only. Need identify single numerical result replication registered target headline numerical result paper states for main finding. Source supplied limited abstract/methods/results start. Main finding likely 5-protein kinase risk signature? Headline number perhaps 5 proteins? They ask \"value paper itself states for registered target, not calculated...\" Registered target headline numerical result for main finding. In title/abstract: \"novel 5-protein kinase risk signature\". Is that the numerical result? Maybe main finding headline numerical result could be 29 dysregulated kinases? Need infer expected extraction. Need report headline number only, unit paper prints, exact contiguous quote. They emphasize target main finding. Source text title says \"A novel 5-protein kinase risk signature...\" and abstract concludes signature. This likely registered target numerical result = 5 proteins. Unit \"protein kinases\"? Need evidence exact quote containing number, perhaps \"we developed a novel protein kinase prognostic risk signature comprising CAMKK2, PAK1, AK5, WEE1, and CDK4\" no number explicit. Title has \"5-protein kinase risk signature\". Exact quote. parent_value 5, unit perhaps \"protein kinases\" but title prints \"5-protein kinase\" (unit could \"protein kinases\" or \"protein kinase risk signature\"? Prompt unit must unit paper prints. Numerical headline result maybe five-protein signature. Evidence \"A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness\" exact contiguous. target_result perhaps \"A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness\". Number stated true.\n\nBut let's carefully parse request. \"Read supplied paper text and find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed.\" REGISTERED TARGET: \"the headline numerical result the paper states for its main finding\". Likely ask extract headline result perhaps signature consists 5 proteins. Main numerical finding could be 29 differentially expressed kinases, but headline paper finding is novel 5-protein kinase risk signature. Need choose one. They want \"headline number only\". So 5 likely.\n\nNeed JSON fields:\nparent_value: 5\nunit: maybe \"protein kinases\" (paper prints \"5-protein kinase risk signature\"; if unit asked unit paper prints, could be \"protein kinase risk signature\"). Usually extraction task has numeric result with unit. A count of proteins has unit \"protein kinases\" perhaps inferred wording from title. Exact quote contains 5. target_result can explain. They said report headline number only perhaps target_result succinct. Evidence exact contiguous quotation, no shortening. We can quote title entire. Need valid JSON no markdown.\n\nCould evidence be: \"we developed a novel protein kinase prognostic risk signature comprising CAMKK2, PAK1, AK5, WEE1, and CDK4\" lacks number and no shorten. Better title.\n\nDoes \"do not shorten it\" refer evidence exact quote, perhaps quote whole clause. The title itself exact. \"Attribution may be carried by text's own wording ... words that name target directly. When that is how text links them, quote whole clause\". Title names result directly. Evidence whole title.\n\nUnit should perhaps be \"protein kinase risk signature\" as count of proteins in signature. But they insist unit paper prints. The printed unit adjacent to 5 is \"protein kinase risk signature\", grammatically 5-protein kinase risk signature. Could set unit \"protein kinases\". Is \"protein kinases\" exact contiguous phrase? In title: \"5-protein kinase risk signature\", singular modifier \"protein kinase\", but body: \"novel protein kinase prognostic risk signature\". If unit is \"protein kinases\", paper does use phrase \"five protein kinases\" in abstract: \"Finally, we confirmed the protein expression of five protein kinases...\" That's spelled five, not numeric. Yet unit concept okay.\n\nMaybe target result is \"29\" dysregulated kinases? Let's investigate possibility based on typical benchmark prompts. They supply paper excerpts, registered target generic \"headline numerical result the paper states for its main finding.\" Could be asking identify one numerical result from text. There are many: 29, 1078, 1217, etc. Main finding headline numerical likely title \"novel 5-protein kinase risk signature\". This is explicitly title and core output. The abstract says identified 29 and developed signature comprising 5. \"headline numerical result\" means likely 5. They ask \"single numerical result that a replication ... has to reproduce\". Replication of main finding would reproduce the 5-protein signature, not count DEGs. Yes.\n\nNeed target_result maybe \"The paper states a novel 5-protein kinase risk signature.\" But \"report headline number only\" perhaps target_result should just characterize. Number field separate. Could set target_result \"A 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness\". Unit \"protein kinases\". Evidence exact title.\n\nEnsure JSON number. Let's formulate.</think>\n\n{\"parent_value\":5,\"unit\":\"protein kinases\",\"evidence\":\"A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness\",\"target_result\":\"A 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness\",\"number_stated\":true}", "reading": {"parent_value": 5, "unit": "protein kinases", "evidence": "A novel 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness", "target_result": "A 5-protein kinase risk signature for predicting glioma prognosis and immunotherapy responsiveness", "number_stated": true, "source_locator": "chars 0-104 of source sha256 6a43fc52837af72578bbb8a4a8044a8f80871fd1b5d68513fe6cc49b7262d795"}, "recorded_at": "2026-10-31T15:23:23.456305+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "29 differentially expressed protein kinases were identified, and a 5-protein kinase prognostic risk signature comprising CAMKK2, PAK1, AK5, WEE1, and CDK4 was developed.", "minimax_alt": "A novel 5-protein kinase prognostic risk signature comprising CAMKK2, PAK1, AK5, WEE1, and CDK4, selected after identification of 29 differentially expressed protein kinases.", "decisive": false}, "physical_methods": {"minimax": [], "minimax_alt": ["Confirmation of protein expression of five protein kinases in glioma samples and normal samples; the specific assay is not stated in the supplied text."], "decisive": false}, "analysis_methods": {"minimax": ["Differential gene expression analysis", "GO enrichment analysis", "KEGG enrichment analysis", "Spearman correlation analysis", "STRING interaction analysis", "Univariate Cox regression", "LASSO regression", "Multivariate Cox regression", "Kaplan–Meier analysis", "Nomogram construction", "Gene set enrichment analysis", "Immune cell infiltration analysis"], "minimax_alt": ["Differential gene expression analysis", "Univariate Cox regression", "LASSO regression", "Multivariate Cox regression", "Kaplan–Meier analysis", "Nomogram construction", "Spearman correlation analysis", "GO enrichment analysis", "KEG enrichment analysis", "Gene set enrichment analysis (GSEA)", "Student’s t-test", "Welch t’ test", "Wilcoxon rank sum test", "One-way ANOVA", "Kruskal–Wallis test"], "decisive": false}, "statistical_method": {"minimax": "Univariate Cox regression, LASSO regression, and multivariate Cox regression", "minimax_alt": "univariate Cox, LASSO, and multivariate Cox regression", "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:69d5584c41833de037f328c85a1c6185e33a24137daf9dc085f66ffd62a4b87d", "type": "sep_package", "label": "SEP v1 package for pmid:40715243"}], "activities": [{"id": "activity:conversion:pmid:40715243", "type": "conversion", "used": ["package:69d5584c41833de037f328c85a1c6185e33a24137daf9dc085f66ffd62a4b87d"], "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": "bc1f0804-f6cf-4776-b42e-e4078a7c089c", "parent_hash": "69d5584c41833de037f328c85a1c6185e33a24137daf9dc085f66ffd62a4b87d", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40715243 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:40715243' does not resolve to a declared asset", "targets"]}