A Novel Ferroptosis-Related 4-Gene Prognostic Signature for Cholangiocarcinoma and Photodynamic Therapy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34712611", "kind": "science-record", "revision": 5, "content_hash": "sha256:dd5eb43a2623ddfff0809ca8268d518355e950afa578dcc6c57b998d5e4e6a40", "created_at": "2026-11-01T17:36:23Z", "updated_at": "2026-11-01T22:47:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Ferroptosis-Related 4-Gene Prognostic Signature for Cholangiocarcinoma and Photodynamic Therapy.", "doi": "10.3389/fonc.2021.747445", "pmid": "34712611", "authors": ["Zi-Jian Zhang", "Yun-Peng Huang", "Xiao-Xue Li", "Zhong-Tao Liu", "Kai Liu", "Xiao-Feng Deng", "Li Xiong", "Heng Zou", "Yu Wen"], "journal": "Frontiers in oncology", "year": 2021, "abstract": "Cholangiocarcinoma is the second most common malignant tumor in the hepatobiliary system. Compared with data on hepatocellular carcinoma, fewer public data and prognostic-related studies on cholangiocarcinoma are available, and effective prognostic prediction methods for cholangiocarcinoma are lacking. In recent years, ferroptosis has become an important subject of tumor research. Some studies have indicated that ferroptosis plays an important role in hepatobiliary cancers. However, the prediction of the prognostic effect of ferroptosis in patients with cholangiocarcinoma has not been reported. In addition, many reports have described the ability of photodynamic therapy (PDT), a potential therapy for cholangiocarcinoma, to regulate ferroptosis by generating reactive oxygen species (ROS). By constructing ferroptosis scores, the prognoses of patients with cholangiocarcinoma can be effectively predicted, and potential gene targets can be discovered to further enhance the efficacy of PDT. In this study, gene expression profiles and clinical information (TCGA, E-MTAB-6389, and GSE107943) of patients with cholangiocarcinoma were collected and divided into training sets and validation sets. Then, a model of the ferroptosis gene signature was constructed using least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis. Furthermore, through the analysis of RNA-seq data after PDT treatment of cholangiocarcinoma, PDT-sensitive genes were obtained and verified by immunohistochemistry staining and Western blot. The results of this study provide new insight for predicting the prognosis of cholangiocarcinoma and screening target genes that enhance the efficacy of PDT.", "claims": [{"id": "claim-method", "statement": "Using the glmnet package in R software, we applied the LASSO Cox regression method to construct the Fer-score model of the FRGs with the best weighted coefficients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34712611", "locator": "sections[5] \"Fer-Score Model Construction\" paragraph 2", "quote": "Using the glmnet package in R software, we applied the LASSO Cox regression method to construct the Fer-score model of the FRGs with the best weighted coefficients."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets E-MTAB-6389 for this study can be found in the European Bioinformatics Institute (EMBL-EBI) database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6389/), the TCGA-CHOL data set (https://portal.gdc.cancer.gov/), and the GSE107943 data set from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse107943).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34712611", "locator": "sections[27] \"Data Availability Statement\" paragraph 2", "quote": "The datasets E-MTAB-6389 for this study can be found in the European Bioinformatics Institute (EMBL-EBI) database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6389/), the TCGA-CHOL data set (https://portal.gdc.cancer.gov/), and the GSE107943 data set from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse107943)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study constructed and validated a four-gene Fer-score prognostic model for cholangiocarcinoma, with LASSO Cox regression used for model construction and the model showing prognostic ability across the analyzed cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34712611", "locator": "sections[19] \"Construction of the Fer-Score Prognostic Model Based on FRGs Related to Clinical Characteristics\" paragraph 2", "quote": "Through cross-validation (default number of times), four FRGs were incorporated into the model: SLC2A1, SLC2A6, SLC7A5, and ZEB1 (Figures 3A, B)."}, {"asset": "source:pmid:34712611", "locator": "sections[20] \"Prognostic Performance Verification of the Fer-Score for the Training Data Set and the Validation Data Sets\" paragraph 2", "quote": "In general, the AUCs of OS showed a good prognostic ability of the Fer-score."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study constructed and validated a four-gene Fer-score prognostic model for cholangiocarcinoma, with LASSO Cox regression used for model construction and the model showing prognostic ability across the analyzed cohorts.", "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:34712611"], "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:E-MTAB-6389"], "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:E-MTAB-6389"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34712611", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2021.747445", "sha256": "sha256:877aad40dac9c806242bc889ba5fc236eaf4093078e6cdcdf6a104b880b1cfe6", "bytes": 45372, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2021.747445", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8545875", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:E-MTAB-6389", "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=E-MTAB-6389", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "E-MTAB-6389", "version": null, "note": "The datasets E-MTAB-6389 for this study can be found in the European Bioinformatics Institute (EMBL-EBI) database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6389/), the TCGA-CHOL data set (https://portal.gdc.cancer.gov/), and the GSE107943 data set from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse107943).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets E-MTAB-6389 for this study can be found in the European Bioinformatics Institute (EMBL-EBI) database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6389/), the TCGA-CHOL data set (https://portal.gdc.cancer.gov/), and the GSE107943 data set from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse107943).", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": null, "resolution_checked_at": "2026-11-01T17:30:51Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE107943", "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=GSE107943", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE107943", "version": null, "note": "The datasets E-MTAB-6389 for this study can be found in the European Bioinformatics Institute (EMBL-EBI) database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6389/), the TCGA-CHOL data set (https://portal.gdc.cancer.gov/), and the GSE107943 data set from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse107943).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets E-MTAB-6389 for this study can be found in the European Bioinformatics Institute (EMBL-EBI) database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6389/), the TCGA-CHOL data set (https://portal.gdc.cancer.gov/), and the GSE107943 data set from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse107943).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2883928", "GSM2883929", "GSM2883930", "GSM2883931", "GSM2883932", "GSM2883933", "GSM2883934", "GSM2883935", "GSM2883936", "GSM2883937", "GSM2883938", "GSM2883939", "GSM2883940", "GSM2883941", "GSM2883942", "GSM2883943", "GSM2883944", "GSM2883945", "GSM2883946", "GSM2883947", "GSM2883948", "GSM2883949", "GSM2883950", "GSM2883951", "GSM2883952", "GSM2883953", "GSM2883954", "GSM2883955", "GSM2883956", "GSM2883957", "GSM2883958", "GSM2883959", "GSM2883960", "GSM2883961", "GSM2883962", "GSM2883963", "GSM2883964", "GSM2883965", "GSM2883966", "GSM2883967", "GSM2883968", "GSM2883969", "GSM2883970", "GSM2883971", "GSM2883972", "GSM2883973", "GSM2883974", "GSM2883975", "GSM2883976", "GSM2883977", "GSM2883978", "GSM2883979", "GSM2883980", "GSM2883981", "GSM2883982", "GSM2883983", "GSM2883984"], "sample_count": 57, "group_sizes": null, "group_sizes_basis": null, "platform": "18573", "taxon": "Homo sapiens", "title": "Integrative molecular and clinical analysis of intrahepatic cholangiocarcinoma reveals two prognostic subclassees", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:E-MTAB-6389 not downloaded", "asset:data:geo:E-MTAB-6389 licence not read", "asset:data:geo:GSE107943 not downloaded", "asset:data:geo:GSE107943 licence not read"]}, "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": 1.0, "band": "verbatim", "quote": "Using the glmnet package in R software, we applied the LASSO Cox regression method to construct the Fer-score model of the FRGs with the best weighted coefficients."}}, {"id": "method-2", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "ANOVA"}}, {"id": "method-3", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "KM"}}, {"id": "method-4", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-5", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}, {"id": "method-6", "family": "clustering", "specific": "hierarchical", "label": "Clustering", "dsl_operations": ["hierarchical_cluster"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "dendrogram"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["GSE107943", "E-MTAB-6389"], "resolved_accessions": ["GSE107943", "E-MTAB-6389"], "statements": ["The datasets E-MTAB-6389 for this study can be found in the European Bioinformatics Institute (EMBL-EBI) database (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6389/), the TCGA-CHOL data set (https://portal.gdc.cancer.gov/), and the GSE107943 data set from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse107943)."], "basis": "The paper names GSE107943, E-MTAB-6389 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:34712611", "data:geo:E-MTAB-6389"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:E-MTAB-6389"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:05:13Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12734, "completion_tokens": 1876, "cached_tokens": 128}}, "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:34712611", "type": "paper", "label": "pmid:34712611"}, {"id": "source:pmid:34712611", "type": "manuscript", "label": "PMID 34712611 full text"}, {"id": "data:geo:E-MTAB-6389", "type": "dataset", "label": "GEO E-MTAB-6389"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:34712611", "type": "paper", "label": "pmid:34712611"}, {"id": "source:pmid:34712611", "type": "manuscript", "label": "PMID 34712611 full text"}, {"id": "data:geo:E-MTAB-6389", "type": "dataset", "label": "GEO E-MTAB-6389"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-34712611", "type": "extraction", "used": ["source:pmid:34712611"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-E-MTAB-6389", "type": "accession_resolution", "used": ["data:geo:E-MTAB-6389"], "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:34712611", "parent_hash": "sha256:d6bb2f98750c7d0ce38d3674c95c379cba3d0c936e6ebbc635e8c0cd49cd148e", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:E-MTAB-6389 licence not read", "asset:data:geo:E-MTAB-6389 not downloaded", "asset:data:geo:GSE107943 licence not read", "asset:data:geo:GSE107943 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["alysis. Furthermore, through the analysis of RNA-seq data after PDT treatment of cholangiocarcino"], "order_basis": "paper_text_order:1461", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ensitive genes were obtained and verified by immunohistochemistry staining and Western blot. The results of th", "d model treated with DVDMS-PDT, we also used IHC to detect changes in the expression of the a"], "order_basis": "paper_text_order:1567", "id": "assay:2"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["erified by immunohistochemistry staining and Western blot. The results of this study provide new insig", "han that in the untreated group (Figure 9C). Western blot analysis indicated that the expression of ZE"], "order_basis": "paper_text_order:1601", "id": "assay:3"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ent. Cell viability was determined using the Celltiter Blue® reagent (Promega, G8082, United States", "tes). A 37°C water bath was used to thaw the Celltiter Blue® reagent and bring it to ambient temper", "d from the 37°C incubator, and 20 µl/well of Celltiter Blue® Reagent was added. The plates were sha"], "order_basis": "paper_text_order:4178", "id": "assay:4"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["or statistical analyses and drawing, and RNA microarray data were analyzed using R 3.6.2. All quanti", "reatment, RBE cells were subjected to an RNA microarray. After removing duplicates, the results sugg", "E) Construction of the gene network based on microarray data. Supplementary Material The Supplementa"], "order_basis": "paper_text_order:5508", "id": "assay:5"}, {"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": ["Using the glmnet package in R software, we applied the LASSO Cox regression method to construct the Fer-score model of the FRGs with the best weighted coefficients."], "order_basis": "consumes_earlier_output", "id": "assay:6"}]}