Identifying a prognostic model and screening of potential natural compounds for acute myeloid leukemia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37434693", "kind": "science-record", "revision": 3, "content_hash": "sha256:bb7c331e24a524d64265219036c19570e31dec534ffaf29ff88dbda4b0c091ad", "created_at": "2026-11-01T19:02:12Z", "updated_at": "2026-11-01T22:09:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying a prognostic model and screening of potential natural compounds for acute myeloid leukemia.", "doi": "10.21037/tcr-22-2500", "pmid": "37434693", "authors": ["Xiao-Hong Sun", "Shun Wan", "Yi-Hong Chai", "Xiao-Teng Bai", "Hong-Xing Li", "Ya-Ming Xi"], "journal": "Translational cancer research", "year": 2023, "abstract": "Background: Acute myeloid leukemia (AML) is one of the most common hematologic malignancies with a poor prognosis and high recurrence rate. The discovery of new predictive models and therapeutic agents plays a crucial role.\nMethods: The differentially expressed gene that was explicitly highly expressed in The Cancer Genome Atlas (TCGA) and GSE9476 transcriptome databases were screened and included in the least absolute shrinkage and selection operator (LASSO) regression model to derive risk coefficients and build a risk score model. Functional enrichment analysis was conducted on the screened hub genes to explore the potential mechanisms. Subsequently, critical genes were incorporated into a nomogram model based on risk scores to analyze prognostic value. Finally, this study combined network pharmacology to find potential natural compounds for hub genes and used molecular docking to verify the binding ability of molecular structures to natural compounds to explore drug development for possible efficacy in AML.\nResults: A total of 33 highly expressed genes may be associated with poor prognosis of AML patients. After LASSO and multivariate Cox regression analysis of 33 critical genes, Rho-related BTB domain containing 2 (RHOBTB2), phospholipase A2 (PLA2G4A), interleukin-2 receptor-α (IL2RA), cysteine and glycine-rich protein 1 (CSRP1), and olfactomedin-like 2A (OLFML2A) were found to played a significant role in the prognosis of AML patients. CSRP1 and OLFML2A were independent prognostic factors of AML. The predictive power of these 5 hub genes in combination with clinical features was better than clinical data alone in predicting AML in the column line graphs and had better predictive value at 1, 3, and 5 years. Finally, through network pharmacology and molecular docking, this study found that diosgenin in Guadi docked well with PLA2G4A, beta-sitosterol in Fangji docked well with IL2RA, and OLFML2A docked well with 3,4-di-O-caffeoylquinic acid in Beiliujinu.\nConclusions: The predictive model of RHOBTB2, PLA2G4A, IL2RA, CSRP1, and OLFML2A combined with clinical features can better guide the prognosis of AML. In addition, the stable docking of PLA2G4A, IL2RA, and OLFML2A with natural compounds may provide new options for treating AML.", "claims": [{"id": "claim-method", "statement": "Thirty-three differentially expressed hub genes were included in the least absolute shrinkage and selection operator (LASSO) (glmnet package & survival package) regression model to obtain the risk coefficient and establish the risk score model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37434693", "locator": "sections[8] \"Construction of prognostic risk score model\" paragraph 2", "quote": "Thirty-three differentially expressed hub genes were included in the least absolute shrinkage and selection operator (LASSO) (glmnet package & survival package) regression model to obtain the risk coefficient and establish the risk score model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "RNA sequencing (RNA-seq) data on AML were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). The complete clinical information of patients was downloaded from TCGA, and a total of 243 AML patients met the criteria at clinical information screening step, excluding samples with less than 30 days of follow-up.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37434693", "locator": "sections[4] \"Data collection\" paragraph 2", "quote": "RNA sequencing (RNA-seq) data on AML were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). The complete clinical information of patients was downloaded from TCGA, and a total of 243 AML patients met the criteria at clinical information screening step, excluding samples with less than 30 days of follow-up."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using LASSO regression and multivariate Cox regression on data from TCGA and GSE9476, the study identified five prognostic hub genes in AML: RHOBTB2, PLA2G4A, IL2RA, CSRP1, and OLFML2A.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37434693", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The differentially expressed gene that was explicitly highly expressed in The Cancer Genome Atlas (TCGA) and GSE9476 transcriptome databases were screened and included in the least absolute shrinkage and selection operator (LASSO) regression model to derive risk coefficients and build a risk score model."}, {"asset": "source:pmid:37434693", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "After LASSO and multivariate Cox regression analysis of 33 critical genes, Rho-related BTB domain containing 2 (RHOBTB2), phospholipase A2 (PLA2G4A), interleukin-2 receptor-α (IL2RA), cysteine and glycine-rich protein 1 (CSRP1), and olfactomedin-like 2A (OLFML2A) were found to played a significant role in the prognosis of AML patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using LASSO regression and multivariate Cox regression on data from TCGA and GSE9476, the study identified five prognostic hub genes in AML: RHOBTB2, PLA2G4A, IL2RA, CSRP1, and OLFML2A.", "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:37434693"], "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:GSE9476"], "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:GSE9476"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37434693", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-22-2500", "sha256": "sha256:91ffb4bc3deec931cb4ea474dbdaebd71b41214f4abd64dc699bb5a5ce6dc052", "bytes": 38250, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tcr-22-2500", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10331709", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE9476", "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=GSE9476", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE9476", "version": null, "note": "RNA sequencing (RNA-seq) data on AML were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). 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