Identification and Validation of a Prognostic Risk-Scoring Model Based on Ferroptosis-Associated Cluster in Acute Myeloid Leukemia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35127715", "kind": "science-record", "revision": 4, "content_hash": "sha256:c4e551500f914c0b76a8c51bc68aa4727d0d25be47168f21a3c16daa2562f962", "created_at": "2026-11-01T17:36:52Z", "updated_at": "2026-11-01T22:05:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and Validation of a Prognostic Risk-Scoring Model Based on Ferroptosis-Associated Cluster in Acute Myeloid Leukemia.", "doi": "10.3389/fcell.2021.800267", "pmid": "35127715", "authors": ["Jinghua Wang", "Zewei Zhuo", "Yanjun Wang", "Shuo Yang", "Jierong Chen", "Yulian Wang", "Suxia Geng", "Minming Li", "Xin Du", "Peilong Lai", "Jianyu Weng"], "journal": "Frontiers in cell and developmental biology", "year": 2021, "abstract": "Background: Emerging evidence has proven that ferroptosis plays an important role in the development of acute myeloid leukemia (AML), whereas the exact role of ferroptosis-associated genes in AML patients' prognosis remained unclear. Materials and Methods: Gene expression profiles and corresponding clinical information of AML cases were obtained from the TCGA (TCGA-LAML), GEO (GSE71014), and TARGET databases (TARGET-AML). Patients in the TCGA cohort were well-grouped into two clusters based on ferroptosis-related genes, and differentially expressed genes were screened between the two clusters. Univariate Cox and LASSO regression analyses were applied to select prognosis-related genes for the construction of a prognostic risk-scoring model. Survival analysis was analyzed by Kaplan-Meier and receiver operator characteristic curves. Furthermore, we explored the correlation of the prognostic risk-scoring model with immune infiltration and chemotherapy response. Risk gene expression level was detected by quantitative reverse transcription polymerase chain reaction. Results: Eighteen signature genes, including ZSCAN4, ASTN1, CCL23, DLL3, EFNB3, FAM155B, FOXL1, HMX2, HRASLS, LGALS1, LHX6, MXRA5, PCDHB12, PRINS, TMEM56, TWIST1, ZFPM2, and ZNF560, were developed to construct a prognostic risk-scoring model. AML patients could be grouped into high- and low-risk groups, and low-risk patients showed better survival than high-risk patients. Area under the curve values of 1, 3, and 5 years were 0.81, 0.827, and 0.786 in the training set, respectively, indicating a good predictive efficacy. In addition, age and risk score were the independent prognostic factors after univariate and multivariate Cox regression analyses. A nomogram containing clinical factors and prognostic risk-scoring model was constructed to better estimate individual survival. Further analyses demonstrated that risk score was associated with the immune infiltration and response to chemotherapy. Our experiment data revealed that LGALS1 and TMEM56 showed notably decreased expression in AML samples than that of the normal samples. Conclusion: Our study shows that the prognostic risk-scoring model and key risk gene may provide potential prognostic biomarkers and therapeutic option for AML patients.", "claims": [{"id": "claim-method", "statement": "Univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35127715", "locator": "sections[6] \"Prognostic Analysis of Prognostic Risk-Scoring Model\" paragraph 2", "quote": "Univariate and multivariate Cox regression analyses were performed to identify the independent prognostic factors."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available data sets were analyzed in this study. This data can be found here: Publicly available data sets can be obtained from the TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/gds/), and TARGET databases (https://ocg.cancer.gov/programs/target).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35127715", "locator": "sections[25] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available data sets were analyzed in this study. This data can be found here: Publicly available data sets can be obtained from the TCGA (https://portal.gdc.cancer.gov/), GEO (https://www.ncbi.nlm.nih.gov/gds/), and TARGET databases (https://ocg.cancer.gov/programs/target)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Area under the curve values of 1, 3, and 5 years were 0.81, 0.827, and 0.786 in the training set, respectively, indicating a good predictive efficacy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35127715", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Area under the curve values of 1, 3, and 5 years were 0.81, 0.827, and 0.786 in the training set, respectively, indicating a good predictive efficacy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Area under the curve values of 1, 3, and 5 years were 0.81, 0.827, and 0.786 in the training set, respectively, indicating a good predictive efficacy.", "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:35127715"], "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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