Characterization of metabolism-associated molecular patterns in prostate cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37280589", "kind": "science-record", "revision": 2, "content_hash": "sha256:4cf6b4918bdeffa5d41a563b694ff6b1c6ef39d3dd34a951a3bdd36f2915e642", "created_at": "2026-11-01T19:02:00Z", "updated_at": "2026-11-01T21:30:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Characterization of metabolism-associated molecular patterns in prostate cancer.", "doi": "10.1186/s12894-023-01275-w", "pmid": "37280589", "authors": ["Bowei Yang", "Yongming Jiang", "Jun Yang", "Wenbo Zhou", "Tongxin Yang", "Rongchang Zhang", "Jinming Xu", "Haixiang Guo"], "journal": "BMC urology", "year": 2023, "abstract": "BACKGROUND: Metabolism is a hallmark of cancer and it involves in resistance to antitumor treatment. Therefore, the purposes of this study are to classify metabolism-related molecular pattern and to explore the molecular and tumor microenvironment characteristics for prognosis predicting in prostate cancer.\nMETHODS: The mRNA expression profiles and the corresponding clinical information for prostate cancer patients from TCGA, cBioPortal, and GEO databases. Samples were classified using unsupervised non-negative matrix factorization (NMF) clustering based on differentially expressed metabolism-related genes (MAGs). The characteristics of disease-free survival (DFS), clinicopathological characteristics, pathways, TME, immune cell infiltration, response to immunotherapy, and sensitivity to chemotherapy between subclusters were explored. A prognostic signature was constructed by LASSO cox regression analysis based on differentially expressed MAGs and followed by the development for prognostic prediction.\nRESULTS: A total of 76 MAGs between prostate cancer samples and non-tumorous samples were found, then 489 patients were divided into two metabolism-related subclusters for prostate cancer. The significant differences in clinical characteristics (age, T/N stage, Gleason) and DFS between two subclusters. Cluster 1 was associated with cell cycle and metabolism-related pathways, and epithelial-mesenchymal transition (EMT), etc., involved in cluster 2. Moreover, lower ESTIMATE/immune/stromal scores, lower expression of HLAs and immune checkpoint-related genes, and lower half-maximal inhibitory concentration (IC50) values in cluster 1 compared with cluster 2. The 10 MAG signature was identified and constructed a risk model for DFS predicting. The patients with high-risk scores showed poorer DFS. The area under the curve (AUC) values for 1-, 3-, 5-year DFS were 0.744, 0.731, 0.735 in TCGA-PRAD dataset, and 0.668, 0.712, 0.809 in GSE70768 dataset, 0.763, 0.802, 0.772 in GSE70769 dataset. Besides, risk score and Gleason score were identified as independent factors for DFS predicting, and the AUC values of risk score and Gleason score were respectively 0.743 and 0.738. The nomogram showed a favorable performance in DFS predicting.\nCONCLUSION: Our data identified two metabolism-related molecular subclusters for prostate cancer that were distinctly characterized in prostate cancer. Metabolism-related risk profiles were also constructed for prognostic prediction.", "claims": [{"id": "claim-method", "statement": "Based on the differentially expressed MAGs, 489 prostate cancer samples from TCGA-PARD dataset were classified into different molecular subclusters using unsupervised non-negative matrix factorization (NMF) clustering via NMF R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37280589", "locator": "sections[5] \"Unsupervised clustering of prostate cancer\" paragraph 2", "quote": "Based on the differentially expressed MAGs, 489 prostate cancer samples from TCGA-PARD dataset were classified into different molecular subclusters using unsupervised non-negative matrix factorization (NMF) clustering via NMF R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found and downloaded from online databases, TCGA (https://portal.gdc.cancer.gov/), cBioPortal database (https://www.cbioportal.org/), GEO (https://www.ncbi.nlm.nih.gov/gds). Besides, the source code and processed data used for all analyses presented here can be freely obtained from https://github.com/DocotorGuo/prostate-cancer.git.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37280589", "locator": "sections[30] \"Availability of data and materials\" paragraph 2", "quote": "The datasets presented in this study can be found and downloaded from online databases, TCGA (https://portal.gdc.cancer.gov/), cBioPortal database (https://www.cbioportal.org/), GEO (https://www.ncbi.nlm.nih.gov/gds). Besides, the source code and processed data used for all analyses presented here can be freely obtained from https://github.com/DocotorGuo/prostate-cancer.git."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Unsupervised non-negative matrix factorization clustering based on 76 differentially expressed metabolism-associated genes classified 489 prostate cancer patients into two metabolism-related molecular subclusters with distinct clinical characteristics and prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37280589", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 76 MAGs between prostate cancer samples and non-tumorous samples were found, then 489 patients were divided into two metabolism-related subclusters for prostate cancer."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Unsupervised non-negative matrix factorization clustering based on 76 differentially expressed metabolism-associated genes classified 489 prostate cancer patients into two metabolism-related molecular subclusters with distinct clinical characteristics and prognosis.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:37280589"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37280589", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12894-023-01275-w", "sha256": "sha256:6f7b988d355d0e90cf946e77e23909e49dc47880e25fd3250d79c6d82793644b", "bytes": 52830, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12894-023-01275-w", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10243086", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "cox regression"}}, {"id": "method-2", "family": null, "specific": null, "label": "Unsupervised non-negative matrix factorization (NMF) clustering", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Based on the differentially expressed MAGs, 489 prostate cancer samples from TCGA-PARD dataset were classified into different molecular subclusters using unsupervised non-negative matrix factorization (NMF) clustering via NMF R package."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The datasets presented in this study can be found and downloaded from online databases, TCGA (https://portal.gdc.cancer.gov/), cBioPortal database (https://www.cbioportal.org/), GEO (https://www.ncbi.nlm.nih.gov/gds). Besides, the source code and processed data used for all analyses presented here can be freely obtained from https://github.com/DocotorGuo/prostate-cancer.git."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:37280589"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:43:02Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14789, "completion_tokens": 2564, "cached_tokens": 0}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:37280589", "type": "paper", "label": "pmid:37280589"}, {"id": "source:pmid:37280589", "type": "manuscript", "label": "PMID 37280589 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37280589", "type": "paper", "label": "pmid:37280589"}, {"id": "source:pmid:37280589", "type": "manuscript", "label": "PMID 37280589 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37280589", "type": "extraction", "used": ["source:pmid:37280589"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:37280589", "parent_hash": "sha256:87aebf5a99b1633acd563de6c0c8907b477dcd2779780129ac828ac93071a5ba", "fork_of": null, "generation": 1}, "gaps": ["execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["cox regression"], "order_basis": "analysis_only", "id": "assay:1"}]}