Integrated bulk and single-cell transcriptomic analysis unveiled a novel cuproptosis-related lipid metabolism gene molecular pattern and a risk index for predicting prognosis and antitumor drug sensitivity in breast cancer.

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"raw_response_sha256": "sha256:c9b8888ce5846f3791636bb86fc65a505ffb4e3b5dd80a3d0375a677784417d4", "raw_response_chars": 3225}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "9c50db2246633de6b2f68e2858bdd7f242a023c0ce490954ec5facc2cbb1cdd2", "source_hash": "29fa246c656953dd22094b666a8487f1be03bf7b3e5994df0442745eef8ae6ff", "raw_response_sha256": "sha256:4e7e5d80913f98deb26b80b4e0ec08c848eaa9a0ac5bb0220b1cb2d1c14bed44", "raw_response_chars": 60102}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "c0fb62da9c22947814443cbb3fe413f59e35f08cd9cbf8ebdb8e6d913b78ab60", "source_hash": "29fa246c656953dd22094b666a8487f1be03bf7b3e5994df0442745eef8ae6ff", "raw_response": "{\"parent_value\":682,\"unit\":\"CLMGs\",\"evidence\":\"In summary, in this study, we identified 682 CLMGs, providing insights into the expression patterns and roles of these genes in breast cancer.\",\"target_result\":\"The study identified 682 cuproptosis-related lipid metabolism genes (CLMGs).\",\"number_stated\":true}", "reading": {"parent_value": 682, "unit": "CLMGs", "evidence": "In summary, in this study, we identified 682 CLMGs, providing insights into the expression patterns and roles of these genes in breast cancer.", "target_result": "The study identified 682 cuproptosis-related lipid metabolism genes (CLMGs).", "number_stated": true, "source_locator": "chars 14932-15074 of source sha256 29fa246c656953dd22094b666a8487f1be03bf7b3e5994df0442745eef8ae6ff"}, "recorded_at": "2026-10-31T15:34:46.224860+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "3bbb77f749757f86037331ebe0802cc4f90a2fe0d040e60124e68bd2f1b937f2", "source_hash": "29fa246c656953dd22094b666a8487f1be03bf7b3e5994df0442745eef8ae6ff", "raw_response": "<think>We need answer JSON only, extract single numerical result headline main finding. Need infer target registered is headline numerical result paper states for main finding. Need identify likely number. Source abstract/model: identified 682 CLMGs, risk model 8 genes. \"headline numerical result\" perhaps AUC values? Need determine what counts as registered target. We need find single numerical result replication must reproduce, value paper states for registered target, not calculated. Registered target vague: \"the headline numerical result the paper states for its main finding\". Need pick perhaps 682 CLMGs. They emphasize numerical result, headline number. Source starts abstract, states \"we identified 682 CLMGs\" repeatedly conclusion. Main finding may be 682 CLMGs. Yet there are multiple AUCs. They ask \"single numerical result that a replication ... has to reproduce.\" Need report headline number only. Target likely asks extraction from paper where main result might be number of genes identified. The title no numerical result. Abstract's only prominent number is 682 CLMGs; four clusters and 8 genes, but latter model genes. \"headline numerical result the paper states for its main finding\" likely 682.\n\nCould be headline number is AUC 0.868? But that's performance for one-year survival, while paper main finding is integrated bulk/single-cell transcriptomic analysis and risk index. Registered target phrasing deliberately generic maybe benchmark framework has targets like \"headline numerical result\". Need select one result. The source conclusion says \"we identified 682 CLMGs\". This is singular and repeated, likely target. Unit could be \"CLMGs\" or perhaps \"cuproptosis-related lipid metabolism genes\". Exact contiguous evidence containing number. Need quote whole clause for attribution. Could use: \"In conclusion, the results of this study provide new insights and strategies for assessing prognosis and implementing precision treatment for breast cancer through the lens of CLMGs.\" No number. Better exact: \"In summary, in this study, we identified 682 CLMGs, providing insights into the expression patterns and roles of these genes in breast cancer.\" Contains direct target. Parent_value 682, unit perhaps \"CLMGs\". User says unit must be unit paper prints. \"682 CLMGs\" printed, so unit \"CLMGs\". target_result should say \"The study identified 682 cuproptosis-related lipid metabolism genes.\" Evidence exact quote.\n\nBut let's ensure perhaps headline numerical result means AUC values and no single because three values. Their instructions mention attribution by parenthetical list / respectively likely target extraction sometimes numbers attached to labels. Here source has AUCs 0.868, .773, .739, respectively. Registered target broad could mean headline numerical result main finding perhaps \"AUC values for predicting 1-year, 3-year, 5-year survival\" but asks single numerical result, so likely select one target? Registered target wording singular may be interpreted as target itself broad category, but asks \"single numerical result\". Could identify main headline number from abstract, likely 682.\n\nLet's parse wording: \"Read the 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. 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.\nREGISTERED TARGET: the headline numerical result the paper states for its main finding\"\n\nThis looks generated task with registered target perhaps a description. We need identify \"headline numerical result\" from paper. Could be challenge to choose primary numeric finding. The abstract highlights:\n- 682 CLMGs\n- four clusters\n- risk model 8 genes.\nConclusion repeats 682.\nAUC 0.868 etc in results.\nWhat is \"main finding\"? \"novel ... molecular pattern and a risk index.\" Number 682 supports novel gene identification. Headline likely 682.\n\nCould they expect \"8\" genes in risk model? Replication of registered target main finding needs reproduce gene model perhaps eight model genes. But numerical headline maybe \"identified 682 CLMGs\". Let's assess wording singular numerical result. If target is \"the headline numerical result the paper states for its main finding\", main finding is perhaps \"682 CLMGs\" because explicit count, whereas 8 genes are enumerated and could count but source doesn't say \"8\" there except single-cell paragraph says 8 CLMG-related risk model genes. Abstract says model incorporates eight names without number. The 682 appears twice. Thus strongest.\n\nUnit must be paper's printed unit. Could \"CLMGs\" be unit? Counts aren't often units but source prints \"682 CLMGs\". They may expect \"CLMGs\". target_result concise.\n\nNeed output exact JSON, no markdown. Ensure quote exact contiguous. \"In summary, in this study, we identified 682 CLMGs, providing insights into the expression patterns and roles of these genes in breast cancer.\" Exact. Could use abstract clause \"By utilizing transcriptomic data from The Cancer Genome Atlas (TCGA) for breast cancer, we identified 682 CLMGs and applied the nonnegative matrix factorization (NMF) method to categorize breast cancer patients into four distinct clusters: cluster 1, ‘‘immune-cold and stroma-poor’’; cluster 2, ‘‘immune-infiltrated’’; cluster 3, ‘‘stroma-rich’’; and cluster 4, ‘‘moderate infiltration’’.\" Longer but exact. Conclusion clause is enough and directly says finding. Use unit \"CLMGs\". parent_value number 682. target_result maybe \"682 cuproptosis-related lipid metabolism genes were identified.\" But asks report headline number only perhaps target_result should only say number? Schema asks target_result string. Maybe \"The study identified 682 CLMGs.\" Evidence has number. Number only in parent_value. Fine.\n\nCould unit be \"genes\" rather than CLMGs? The paper prints \"682 CLMGs\", so unit \"CLMGs.\" They explicitly say unit must be unit paper prints. Yes.\n\nNeed not include extra keys.</think>\n\n{\"parent_value\":682,\"unit\":\"CLMGs\",\"evidence\":\"In summary, in this study, we identified 682 CLMGs, providing insights into the expression patterns and roles of these genes in breast cancer.\",\"target_result\":\"The study identified 682 cuproptosis-related lipid metabolism genes.\",\"number_stated\":true}", "reading": {"parent_value": 682, "unit": "CLMGs", "evidence": "In summary, in this study, we identified 682 CLMGs, providing insights into the expression patterns and roles of these genes in breast cancer.", "target_result": "The study identified 682 cuproptosis-related lipid metabolism genes.", "number_stated": true, "source_locator": "chars 14932-15074 of source sha256 29fa246c656953dd22094b666a8487f1be03bf7b3e5994df0442745eef8ae6ff"}, "recorded_at": "2026-10-31T15:34:51.269406+00:00"}], "agreement": true, "disagreements": {"target_result": {"minimax": "682 cuproptosis-related lipid metabolism genes (CLMGs)", "minimax_alt": "682 cuproptosis-related lipid metabolism genes (CLMGs) were identified.", "decisive": false}, "analysis_methods": {"minimax": ["Pearson correlation analysis", "nonnegative matrix factorization (NMF)"], "minimax_alt": ["Pearson correlation analysis", "Consistent clustering analysis using nonnegative matrix factorization (NMF)", "Kaplan–Meier survival analysis", "Univariate and multivariate Cox regression", "Limma differential gene expression analysis", "Gene Ontology (GO) enrichment analysis", "Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis", "Gene set enrichment analysis (GSEA)", "Spearman correlation analysis", "ESTIMATE analysis", "CIBERSORT", "Single-sample GSEA (ssGSEA)", "Maftools somatic-mutation annotation analysis", "Uniform manifold approximation and projection (UMAP)", "Wilcoxon test", "ROC curve analysis", "Nomogram and calibration curve analysis"], "decisive": false}}, "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": "passed", "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.passed"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:2c7e682401b4c36e5cacdb0fa7e9c284995d0a45ceba9c9a07e1215a80d7f65b", "type": "sep_package", "label": "SEP v1 package for pmid:40085377"}], "activities": [{"id": "activity:conversion:pmid:40085377", "type": "conversion", "used": ["package:2c7e682401b4c36e5cacdb0fa7e9c284995d0a45ceba9c9a07e1215a80d7f65b"], "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": "0c5ea9c9-7a79-48ff-acec-ac39b10183fa", "parent_hash": "2c7e682401b4c36e5cacdb0fa7e9c284995d0a45ceba9c9a07e1215a80d7f65b", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:40085377 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.passed", "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:40085377' does not resolve to a declared asset", "targets"]}