Comprehensive Analyses of Ferroptosis-Related Alterations and Their Prognostic Significance in Glioblastoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35720126", "kind": "science-record", "revision": 4, "content_hash": "sha256:133b182631877b132e3b4ccea1108dea5398fe1bdf2a7344e8c68f191b7c86f1", "created_at": "2026-11-01T18:14:43Z", "updated_at": "2026-11-01T22:06:49Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive Analyses of Ferroptosis-Related Alterations and Their Prognostic Significance in Glioblastoma.", "doi": "10.3389/fmolb.2022.904098", "pmid": "35720126", "authors": ["Yuan Tian", "Hongtao Liu", "Caiqing Zhang", "Wei Liu", "Tong Wu", "Xiaowei Yang", "Junyan Zhao", "Yuping Sun"], "journal": "Frontiers in molecular biosciences", "year": 2022, "abstract": "Background: This study was designed to explore the implications of ferroptosis-related alterations in glioblastoma patients. Method: After obtaining the data sets CGGA325, CGGA623, TCGA-GBM, and GSE83300 online, extensive analysis and mutual verification were performed using R language-based analytic technology, followed by further immunohistochemistry staining verification utilizing clinical pathological tissues. Results: The analysis revealed a substantial difference in the expression of ferroptosis-related genes between malignant and paracancerous samples, which was compatible with immunohistochemistry staining results from clinicopathological samples. Three distinct clustering studies were run sequentially on these data. All of the findings were consistent and had a high prediction value for glioblastoma. Then, the risk score predicting model containing 23 genes (CP, EMP1, AKR1C1, FMOD, MYBPH, IFI30, SRPX2, PDLIM1, MMP19, SPOCD1, FCGBP, NAMPT, SLC11A1, S100A10, TNC, CSMD3, ATP1A2, CUX2, GALNT9, TNFAIP6, C15orf48, WSCD2, and CBLN1) on the basis of \"Ferroptosis.gene.cluster\" was constructed. In the subsequent correlation analysis of clinical characteristics, tumor mutation burden, HRD, neoantigen burden and chromosomal instability, mRNAsi, TIDE, and GDSC, all the results indicated that the risk score model might have a better predictive efficiency. Conclusion: In glioblastoma, there were a large number of abnormal ferroptosis-related alterations, which were significant for the prognosis of patients. The risk score-predicting model integrating 23 genes would have a higher predictive value.", "claims": [{"id": "claim-method", "statement": "After the sample risk score was obtained, the high- and low-risk score groups were divided by the median node, the survival analysis was performed in the high- and low-risk score groups, and then the Kaplan–Meier survival curve was drawn.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35720126", "locator": "sections[25] \"2.11 The Evaluation of the Prognostic Efficacy\" paragraph 2", "quote": "After the sample risk score was obtained, the high- and low-risk score groups were divided by the median node, the survival analysis was performed in the high- and low-risk score groups, and then the Kaplan–Meier survival curve was drawn."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35720126", "locator": "sections[68] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "After that, the time-based ROC curve was drawn, and the AUC values of 1, 3, and 5 years were all greater than 0.6, indicating that the predictive model had a good predictive efficiency (Figure 4G).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35720126", "locator": "sections[53] \"3.11 Prognostic Survival Assessment\" paragraph 4", "quote": "After that, the time-based ROC curve was drawn, and the AUC values of 1, 3, and 5 years were all greater than 0.6, indicating that the predictive model had a good predictive efficiency (Figure 4G)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "After that, the time-based ROC curve was drawn, and the AUC values of 1, 3, and 5 years were all greater than 0.6, indicating that the predictive model had a good predictive efficiency (Figure 4G).", "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:35720126"], "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. 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