A Ferroptosis and Pyroptosis Molecular Subtype-Related Signature Applicable for Prognosis and Immune Microenvironment Estimation in Hepatocellular Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34869350", "kind": "science-record", "revision": 3, "content_hash": "sha256:7a869b7955805d0a4db9af10007e04b8848337f2206d43cddf941374710d71c6", "created_at": "2026-11-01T17:36:34Z", "updated_at": "2026-11-01T21:26:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Ferroptosis and Pyroptosis Molecular Subtype-Related Signature Applicable for Prognosis and Immune Microenvironment Estimation in Hepatocellular Carcinoma.", "doi": "10.3389/fcell.2021.761839", "pmid": "34869350", "authors": ["Junyu Huo", "Jinzhen Cai", "Ge Guan", "Huan Liu", "Liqun Wu"], "journal": "Frontiers in cell and developmental biology", "year": 2021, "abstract": "Background: Due to the heterogeneity of tumors and the complexity of the immune microenvironment, the specific role of ferroptosis and pyroptosis in hepatocellular carcinoma (HCC) is not fully understood, especially its impact on prognosis. Methods: The training set (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) based on the prognosis-related genes associated with ferroptosis and pyroptosis. The intersecting differentially expressed genes (DEGs) among C1, C2, and C3 were used in univariate Cox and LASSO penalized Cox regression analysis for the construction of the risk score. The median risk score served as the unified cutoff to divide patients into high- and low-risk groups. Results: Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC. As an independent prognostic indicator, the risk score could be applicable for patients with different clinical features tested by subgroup (n = 26) survival analysis. In the high-risk patients with a lower infiltration abundance of activated B cells, activated CD8 T cells, eosinophils, and type I T helper cells and a higher infiltration abundance of immature dendritic cells, the cytolytic activity, HLA, inflammation promotion, and type I IFN response in the high-risk group were weaker. The TP53 mutation rate, TMB, and CSC characteristics in the high-risk group were significantly higher than those in the low-risk group. Low-risk patients have active metabolic activity and a more robust immune response. The high- and low-risk groups differed significantly in histology grade, vascular tumor cell type, AFP, new tumor event after initial treatment, main tumor size, cirrhosis, TNM stage, BCLC stage, and CLIP score. Conclusion: The ferroptosis and pyroptosis molecular subtype-related signature identified and validated in this work is applicable for prognosis prediction, immune microenvironment estimation, stem cell characteristics, and clinical feature assessment in HCC.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was used to identify genes associated with OS in the training cohort (n = 609), and the filter p-value was set as < 0.05. The overfitting between the prognosis-related genes was removed by the least absolute shrinkage and selection operator (LASSO) algorithm to reduce the scope of the prognosis-related genes with penalty parameter tuning performed via 10−fold cross−validation based on the R package “glmnet” (Huo et al., 2021a).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34869350", "locator": "sections[5] \"Building a Risk Score for Predicting the Overall Survival of Hepatocellular Carcinoma in the Training Cohort\" paragraph 2", "quote": "Univariate Cox regression analysis was used to identify genes associated with OS in the training cohort (n = 609), and the filter p-value was set as < 0.05. The overfitting between the prognosis-related genes was removed by the least absolute shrinkage and selection operator (LASSO) algorithm to reduce the scope of the prognosis-related genes with penalty parameter tuning performed via 10−fold cross−validation based on the R package “glmnet” (Huo et al., 2021a)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: The datasets analyzed 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/), FerrDb (http://www.zhounan.org/ferrdb), and the molecular signatures database (MSigDB, http://www.gsea-msigdb.org/gsea).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34869350", "locator": "sections[21] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: The datasets analyzed 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/), FerrDb (http://www.zhounan.org/ferrdb), and the molecular signatures database (MSigDB, http://www.gsea-msigdb.org/gsea)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34869350", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of 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:34869350"], "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:GSE14520"], "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:GSE14520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34869350", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcell.2021.761839", "sha256": "sha256:fe6ed7a0cdc31f0c2138aa7c0a89717a4e5fa1d3093fb35e3456b8bffb7244bb", "bytes": 52060, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcell.2021.761839", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8634890", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE14520", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE14520", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: The datasets analyzed 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/), FerrDb (http://www.zhounan.org/ferrdb), and the molecular signatures database (MSigDB, http://www.gsea-msigdb.org/gsea).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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"Gene expression data of human hepatocellular carcinoma (HCC)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE14520 not downloaded", "asset:data:geo:GSE14520 licence not read"]}, "analysis": {"state": "complete", "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": 1.0, "band": "verbatim", "quote": "Univariate Cox regression analysis was used to identify genes associated with OS in the training cohort (n = 609), and the filter p-value was set as < 0.05. The overfitting between the prognosis-related genes was removed by the least absolute shrinkage and selection operator (LASSO) algorithm to reduce the scope of the prognosis-related genes with penalty parameter tuning performed via 10−fold cross−validation based on the R package “glmnet” (Huo et al., 2021a)."}}], "targets": [], "gaps": []}, "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "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": "R1", "reachable": true, "accessions": ["GSE14520"], "resolved_accessions": ["GSE14520"], "statements": ["Publicly available datasets were analyzed in this study. This data can be found here: The datasets analyzed 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/), FerrDb (http://www.zhounan.org/ferrdb), and the molecular signatures database (MSigDB, http://www.gsea-msigdb.org/gsea)."], "basis": "The paper names GSE14520 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:34869350", "data:geo:GSE14520"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE14520"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:10:05Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 15250, "completion_tokens": 2386, "cached_tokens": 256}}, "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:34869350", "type": "paper", "label": "pmid:34869350"}, {"id": "source:pmid:34869350", "type": "manuscript", "label": "PMID 34869350 full text"}, {"id": "data:geo:GSE14520", "type": "dataset", "label": "GEO GSE14520"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34869350", "type": "paper", "label": "pmid:34869350"}, {"id": "source:pmid:34869350", "type": "manuscript", "label": "PMID 34869350 full text"}, {"id": "data:geo:GSE14520", "type": "dataset", "label": "GEO GSE14520"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34869350", "type": "extraction", "used": ["source:pmid:34869350"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE14520", "type": "accession_resolution", "used": ["data:geo:GSE14520"], "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:34869350", "parent_hash": "sha256:dddb01f942d8bc00d9cf60a9e2dbb860d5a26bdae5fe7089ea01455bd5dc1a9a", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE14520 licence not read", "asset:data:geo:GSE14520 not downloaded", "execution.not_run", "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": ["Univariate Cox regression analysis was used to identify genes associated with OS in the training cohort (n = 609), and the filter p-value was set as < 0.05. The overfitting between the prognosis-related genes was removed by the least absolute shrinkage and selection operator (LASSO) algorithm to reduce the scope of the prognosis-related genes with penalty parameter tuning performed via 10−fold cross−validation based on the R package “glmnet” (Huo et al., 2021a)."], "order_basis": "analysis_only", "id": "assay:1"}]}