Ferroptosis-Related Gene Contributes to Immunity, Stemness and Predicts Prognosis in Glioblastoma Multiforme.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35359663", "kind": "science-record", "revision": 4, "content_hash": "sha256:233d5fc36eb3baf74499b3429f9fdf496e6a8f7908840b249037ef6c49d4dea3", "created_at": "2026-11-01T18:14:10Z", "updated_at": "2026-11-01T22:48:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Ferroptosis-Related Gene Contributes to Immunity, Stemness and Predicts Prognosis in Glioblastoma Multiforme.", "doi": "10.3389/fneur.2022.829926", "pmid": "35359663", "authors": ["Jiawei Dong", "Hongtao Zhao", "Fang Wang", "Jiaqi Jin", "Hang Ji", "Xiuwei Yan", "Nan Wang", "Jiheng Zhang", "Shaoshan Hu"], "journal": "Frontiers in neurology", "year": 2022, "abstract": "Ferroptosis, a recently discovered regulated programmed cell death, is associated with tumorigenesis and progression in glioblastoma. Based on widely recognized ferroptosis-related genes (FRGs), the regulation of ferroptosis patterns and corresponding characteristics of immune infiltration of 516 GBM samples with GSE13041, TCGA-GBM, and CGGA-325 were comprehensively analyzed. Here, we revealed the expression, mutations, and CNV of FRGs in GBM. We identified three distinct regulation patterns of ferroptosis and found the hub genes of immunity and stemness among DEGs in three patterns. A prognostic model was constructed based on five FRGs and verified at the mRNA and protein level. The risk score can not only predict the prognosis but also the degree of immune infiltration and ICB responsiveness by functional annotation. The overall assessment of FRGs in GBM patients will guide the direction of improved research and develop new prognostic prediction tools.", "claims": [{"id": "claim-method", "statement": "LASSO Cox analysis was performed to establish an FRG prognostic signature. Univariate and multivariate Cox regression analyses show the independent prognostic value of this risk score (Supplementary Figures 1A,B). The OS of the patients with glioma between different groups was analyzed using Kaplan–Meier curves with the log-rank test. The predictive ability of prognostic signature for clinical traits and survival was reflected by receiver operating characteristic (ROC) and the area under the curve (AUC).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35359663", "locator": "sections[15] \"Construct the FRG Prognostic Signature\" paragraph 2", "quote": "LASSO Cox analysis was performed to establish an FRG prognostic signature."}, {"asset": "source:pmid:35359663", "locator": "sections[15] \"Construct the FRG Prognostic Signature\" paragraph 2", "quote": "Univariate and multivariate Cox regression analyses show the independent prognostic value of this risk score (Supplementary Figures 1A,B)."}, {"asset": "source:pmid:35359663", "locator": "sections[8] \"Statistical Analysis\" paragraph 2", "quote": "The OS of the patients with glioma between different groups was analyzed using Kaplan–Meier curves with the log-rank test."}, {"asset": "source:pmid:35359663", "locator": "sections[6] \"Construction of a Scoring System and Calculate the Risk Score\" paragraph 4", "quote": "The predictive ability of prognostic signature for clinical traits and survival was reflected by receiver operating characteristic (ROC) and the area under the curve (AUC)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found at: The Cancer Genome Atlas (TCGA) database, https://portal.gdc.cancer.gov/, TCGA-GBM, Chinese Glioma Genome Atlas (CGGA), http://www.cgga.org.cn/, CGGA-325, and National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), https://www.ncbi.nlm.nih.gov/geo, GSE13041.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35359663", "locator": "sections[19] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found at: The Cancer Genome Atlas (TCGA) database, https://portal.gdc.cancer.gov/, TCGA-GBM, Chinese Glioma Genome Atlas (CGGA), http://www.cgga.org.cn/, CGGA-325, and National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), https://www.ncbi.nlm.nih.gov/geo, GSE13041."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Next, we established 3- and 5-year ROC curves and found that the risk score can effectively distinguish GBM patients with different survival statuses in train set (Figure 7E, 3-year AUC = 0.706, 5-year AUC = 0.782).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35359663", "locator": "sections[15] \"Construct the FRG Prognostic Signature\" paragraph 2", "quote": "Next, we established 3- and 5-year ROC curves and found that the risk score can effectively distinguish GBM patients with different survival statuses in train set (Figure 7E, 3-year AUC = 0.706, 5-year AUC = 0.782)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Next, we established 3- and 5-year ROC curves and found that the risk score can effectively distinguish GBM patients with different survival statuses in train set (Figure 7E, 3-year AUC = 0.706, 5-year AUC = 0.782).", "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:35359663"], "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:GSE13041"], "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:GSE13041"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35359663", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fneur.2022.829926", "sha256": "sha256:e35e0f0eb86b02e7b4d899f8c275647f24c2e40d41a6dfe1ab7109de9c2a6e47", "bytes": 42355, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fneur.2022.829926", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8960280", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13041", "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=GSE13041", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13041", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found at: The Cancer Genome Atlas (TCGA) database, https://portal.gdc.cancer.gov/, TCGA-GBM, Chinese Glioma Genome Atlas (CGGA), http://www.cgga.org.cn/, CGGA-325, and National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), https://www.ncbi.nlm.nih.gov/geo, GSE13041.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. This data can be found at: The Cancer Genome Atlas (TCGA) database, https://portal.gdc.cancer.gov/, TCGA-GBM, Chinese Glioma Genome Atlas (CGGA), http://www.cgga.org.cn/, CGGA-325, and National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO), https://www.ncbi.nlm.nih.gov/geo, GSE13041.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM326661", "GSM326662", "GSM326663", "GSM326664", "GSM326665", "GSM326666", "GSM326667", "GSM326668", "GSM326669", "GSM326670", "GSM326671", "GSM326672", "GSM326673", "GSM326674", "GSM326675", "GSM326676", "GSM326677", "GSM326678", "GSM326679", "GSM326680", "GSM326681", "GSM326682", "GSM326683", "GSM326684", "GSM326685", "GSM326686", "GSM326687", "GSM326688", "GSM326689", "GSM326690", "GSM326691", "GSM326692", "GSM326693", "GSM326694", "GSM326695", "GSM326696", "GSM326697", "GSM326698", "GSM326699", "GSM326700", "GSM326701", "GSM326702", "GSM326703", "GSM326704", "GSM326705", "GSM326706", "GSM326707", "GSM326708", "GSM326709", "GSM326710", "GSM326711", "GSM326712", "GSM326713", "GSM326714", "GSM326715", "GSM326716", "GSM326717", "GSM326718", "GSM326719", "GSM326720", "GSM326721", "GSM326722", "GSM326723", "GSM326724", "GSM326725", "GSM326726", "GSM326727", "GSM326728", "GSM326729", "GSM326730", "GSM326731", "GSM326732", "GSM326733", "GSM326734", "GSM326735", "GSM326736", "GSM326737", "GSM326738", "GSM326739", "GSM326740", "GSM326741", "GSM326742", "GSM326743", "GSM326744", "GSM326745", "GSM326746", "GSM326747", "GSM326748", "GSM326749", "GSM326750", "GSM326751", "GSM326752", "GSM326753", "GSM326754", "GSM326755", "GSM326756", "GSM326757", "GSM326758", "GSM326759", "GSM326760", "GSM326761", "GSM326762", "GSM326763", "GSM326764", "GSM326765", "GSM326766", "GSM326767", "GSM326768", "GSM326769", "GSM326770", "GSM326771", "GSM326772", "GSM326773", "GSM326774", "GSM326775", "GSM326776", "GSM326777", "GSM326778", "GSM326779", "GSM326780", "GSM326781", "GSM326782", "GSM326783", "GSM326784", "GSM326785", "GSM326786", "GSM326787", "GSM326788", "GSM326789", "GSM326790", "GSM326791", "GSM326792", "GSM326793", "GSM326794", "GSM326795", "GSM326796", "GSM326797", "GSM326798", "GSM326799", "GSM326800", "GSM326801", "GSM326802", "GSM326803", "GSM326804", "GSM326805", "GSM326806", "GSM326807", "GSM326808", "GSM326809", "GSM326810", "GSM326811", "GSM326812", "GSM326813", "GSM326814", "GSM326815", "GSM326816", "GSM326817", "GSM326818", "GSM326819", "GSM326820", "GSM326821", "GSM326822", "GSM326823", "GSM326824", "GSM326825", "GSM326826", "GSM326827", "GSM326828", "GSM326829", "GSM326830", "GSM326831", "GSM326832", "GSM326833", "GSM326834", "GSM326835", "GSM326836", "GSM326837", "GSM326838", "GSM326839", "GSM326840", "GSM326841", "GSM326842", "GSM326843", "GSM326844", "GSM326845", "GSM326846", "GSM326847", "GSM326848", "GSM326849", "GSM326850", "GSM326851", "GSM326852", "GSM326853", "GSM326854", "GSM326855", "GSM326856", "GSM326857", "GSM326858", "GSM326859", "GSM326860", "GSM326861", "GSM326862", "GSM326863", "GSM326864", "GSM326865", "GSM326866", "GSM326867", "GSM326868", "GSM326869", "GSM326870", "GSM326871", "GSM326872", "GSM326873", "GSM326874", "GSM326875", "GSM326876", "GSM326877", "GSM326878", "GSM326879", "GSM326880", "GSM326881", "GSM326882", "GSM326883", "GSM326884", "GSM326885", "GSM326886", "GSM326887", "GSM326888", "GSM326889", "GSM326890", "GSM326891", "GSM326892", "GSM326893", "GSM326894", "GSM326895", "GSM326896", "GSM326897", "GSM326898", "GSM326899", "GSM326900", "GSM326901", "GSM326902", "GSM326903", "GSM326904", "GSM326905", "GSM326906", "GSM326907", "GSM326908", "GSM326909", "GSM326910", "GSM326911", "GSM326912", "GSM326913", "GSM326914", "GSM326915", "GSM326916", "GSM326917", "GSM326918", "GSM326919", "GSM326920", "GSM326921", "GSM326922", "GSM326923", "GSM326924", "GSM326925", "GSM326926", "GSM326927"], "sample_count": 267, "group_sizes": null, "group_sizes_basis": null, "platform": "570;8300;96", "taxon": "Homo sapiens", "title": "Gene expression analysis of glioblastomas identifies the major molecular basis for the prognostic benefit of younger age", "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:GSE13041 not downloaded", "asset:data:geo:GSE13041 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": "reordered", "quote": "LASSO Cox analysis was performed to establish an FRG prognostic signature. Univariate and multivariate Cox regression analyses show the independent prognostic value of this risk score (Supplementary Figures 1A,B). The OS of the patients with glioma between different groups was analyzed using Kaplan–Meier curves with the log-rank test. The predictive ability of prognostic signature for clinical traits and survival was reflected by receiver operating characteristic (ROC) and the area under the curve (AUC)."}}, {"id": "method-2", "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"}}], "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. 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