Comprehensive analysis of metabolic pathway activity subtypes derived prognostic signature in hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35651292", "kind": "science-record", "revision": 4, "content_hash": "sha256:8fe13187b349def8012be044a57c09492fd7a978bc44fb06d74a2ad8782537e7", "created_at": "2026-11-01T18:14:35Z", "updated_at": "2026-11-01T22:06:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of metabolic pathway activity subtypes derived prognostic signature in hepatocellular carcinoma.", "doi": "10.1002/cam4.4858", "pmid": "35651292", "authors": ["Junyu Huo", "Jinzhen Cai", "Liqun Wu"], "journal": "Cancer medicine", "year": 2023, "abstract": "OBJECTIVE: Metabolic reprogramming is one of the hallmarks of cancer, but metabolic pathway activity-related subtypes of hepatocellular carcinoma (HCC) have not been identified.\nMETHODS: Based on the quantification results of 41 metabolic pathway activities by gene set variation analysis, the training cohort (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) with the nonnegative matrix factorization method. Totally 1371 differentially expressed genes among C1, C2, and C3 were identified, and an 8-gene risk score was established by univariable Cox regression analysis, least absolute shrinkage and selection operator method, and multivariable Cox regression analysis.\nRESULTS: C1 had the strongest metabolic activity, good prognosis, the highest CTNNB1 mutation rate, with massive infiltration of eosinophils and natural killer cells. C2 had the weakest metabolic activity, poor prognosis, was younger, was inclined to vascular invasion and advanced stage, had the highest TP53 mutation rate, exhibited a higher expression level of immune checkpoints, accompanied by massive infiltration of regulatory T cells. C3 had moderate metabolic activity and prognosis, the highest LRP1B mutation rate, and a higher infiltration level of neutrophils and macrophages. Internal cohorts (TCGA, n = 370; GSE14520, n = 239), external cohorts (ICGC, n = 231; GSE116174, n = 64), and clinical subgroup validation showed that the risk score was applicable for patients with diverse clinical features and was effective in predicting the prognosis and malignant progression of patients with HCC. Compared with the low-risk group, the high-risk group had a poor prognosis, enhanced cancer stem cell characteristics, activated DNA damage repair, weakened metabolic activity, cytolytic activity, and interferon response.\nCONCLUSION: We identified HCC subtypes from the perspective of metabolism-related pathway activity and proposed a robust prognostic signature for HCC.", "claims": [{"id": "claim-method", "statement": "We extracted a total of 41 KEGG metabolic pathways from the molecular signature database (MSigDB, http://www.gsea‐msigdb.org/gsea) (Table S1) and used the enrichment score (ES) generated from the gene set variation analysis (GSVA) to quantify the activity of metabolic pathways.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35651292", "locator": "sections[4] \"Quantification and cluster analysis of metabolic pathway activity\" paragraph 2", "quote": "We extracted a total of 41 KEGG metabolic pathways from the molecular signature database (MSigDB, http://www.gsea‐msigdb.org/gsea) (Table S1) and used the enrichment score (ES) generated from the gene set variation analysis (GSVA) to quantify the activity of metabolic pathways."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed for this study were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), International Cancer Genome Consortium database (ICGC, https://dcc.icgc.org/releases/current/Projects/LIRI‐JP), Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), and the molecular signatures database (MSigDB, http://www.gsea‐msigdb.org/gsea).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35651292", "locator": "sections[25] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The datasets analysed for this study were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), International Cancer Genome Consortium database (ICGC, https://dcc.icgc.org/releases/current/Projects/LIRI‐JP), Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), and the molecular signatures database (MSigDB, http://www.gsea‐msigdb.org/gsea)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We identified HCC subtypes from the perspective of metabolism‐related pathway activity and proposed a robust prognostic signature for HCC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35651292", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We identified HCC subtypes from the perspective of metabolism‐related pathway activity and proposed a robust prognostic signature for HCC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We identified HCC subtypes from the perspective of metabolism‐related pathway activity and proposed a robust prognostic signature for HCC.", "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:35651292"], "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": "complete", "blocked_on": null, "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": ["data:geo:GSE116174"], "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": ["data:geo:GSE116174"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35651292", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/cam4.4858", "sha256": 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE14520", "GSE116174"], "resolved_accessions": ["GSE14520", "GSE116174"], "statements": ["The datasets analysed for this study were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/), International Cancer Genome Consortium database (ICGC, https://dcc.icgc.org/releases/current/Projects/LIRI‐JP), Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), and the molecular signatures database (MSigDB, http://www.gsea‐msigdb.org/gsea)."], "basis": "The paper names GSE14520, GSE116174 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:35651292", "data:geo:GSE116174"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE116174"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:48:32Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13233, "completion_tokens": 2012, "cached_tokens": 264}}, "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:35651292", "type": "paper", "label": "pmid:35651292"}, {"id": "source:pmid:35651292", "type": "manuscript", "label": "PMID 35651292 full text"}, {"id": "data:geo:GSE116174", "type": "dataset", "label": "GEO GSE116174"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35651292", "type": "paper", "label": "pmid:35651292"}, {"id": "source:pmid:35651292", "type": "manuscript", "label": "PMID 35651292 full text"}, {"id": "data:geo:GSE116174", "type": "dataset", "label": "GEO GSE116174"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35651292", "type": "extraction", "used": ["source:pmid:35651292"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE116174", "type": "accession_resolution", "used": ["data:geo:GSE116174"], "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:35651292", "parent_hash": "sha256:0036e2fd26feae55ae74d58be6497e215d5a910f17cc7765b76ebb86fa898394", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE116174 licence not read", "asset:data:geo:GSE116174 not downloaded", "asset:data:geo:GSE14520 licence not read", "asset:data:geo:GSE14520 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "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": 1.0, "evidence": ["We extracted a total of 41 KEGG metabolic pathways from the molecular signature database (MSigDB, http://www.gsea‐msigdb.org/gsea) (Table S1) and used the enrichment score (ES) generated from the gene set variation analysis (GSVA) to quantify the activity of metabolic pathways."], "order_basis": "analysis_only", "id": "assay:1"}]}