Identification and validation of a siglec-based and aging-related 9-gene signature for predicting prognosis in acute myeloid leukemia patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35854240", "kind": "science-record", "revision": 4, "content_hash": "sha256:73df4631f9028a5c5c1f7370b7f6860f83be5c44d12ad77b655ed4f71b910bfe", "created_at": "2026-11-01T18:14:56Z", "updated_at": "2026-11-01T22:07:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of a siglec-based and aging-related 9-gene signature for predicting prognosis in acute myeloid leukemia patients.", "doi": "10.1186/s12859-022-04841-5", "pmid": "35854240", "authors": ["Huiping Shi", "Liang Gao", "Weili Zhang", "Min Jiang"], "journal": "BMC bioinformatics", "year": 2022, "abstract": "BACKGROUND: Acute myeloid leukemia (AML) is a group of highly heterogenous and aggressive blood cancer. Despite recent progress in its diagnosis and treatment, patient outcome is variable and drug resistance results in increased mortality. The siglec family plays an important role in tumorigenesis and aging. Increasing age is a risk factor for AML and cellular aging contributes to leukemogenesis via various pathways.\nMETHODS: The differential expression of the siglec family was compared between 151 AML patients and 70 healthy controls, with their information downloaded from TCGA and GTEx databases, respectively. How siglec expression correlated to AML patient clinical features, immune cell infiltration, drug resistance and survival outcome was analyzed. Differentially expressed genes in AML patients with low- and high-expressed siglec9 and siglec14 were analyzed and functionally enriched. The aging-related gene set was merged with the differentially expressed genes in AML patients with low and high expression of siglec9, and merged genes were subjected to lasso regression analysis to construct a novel siglec-based and aging-related prognostic model. The prediction model was validated using a validation cohort from GEO database (GSE106291).\nRESULTS: The expression levels of all siglec members were significantly altered in AML. The expression of siglecs was significantly correlated with AML patient clinical features, immune cell infiltration, drug resistance, and survival outcome. Based on the differentially expressed genes and aging-related gene set, we developed a 9-gene prognostic model and decision curve analysis revealed the net benefit generated by our prediction model. The siglec-based and aging-related 9-gene prognostic model was tested using a validation data set, in which AML patients with higher risk scores had significantly reduced survival probability. Time-dependent receiver operating characteristic curve and nomogram were plotted and showed the diagnostic accuracy and predictive value of our 9-gene prognostic model, respectively.\nCONCLUSIONS: Overall, our study indicates the important role of siglec family in AML and the good performance of our novel siglec-based and aging-related 9-gene signature in predicting AML patient outcome.", "claims": [{"id": "claim-method", "statement": "The merged genes were subjected to least absolute shrinkage and selection operator (lasso) regression analysis using the glmnet package (version 4.1-2) and survival package (version 3.2-10). The lasso regression analysis was performed with tenfold cross validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35854240", "locator": "sections[13] \"Construction of the siglec-based and aging-related prognostic model\" paragraph 2", "quote": "The merged genes were subjected to least absolute shrinkage and selection operator (lasso) regression analysis using the glmnet package (version 4.1-2) and survival package (version 3.2-10). The lasso regression analysis was performed with tenfold cross validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during this study are publicly available in the TCGA database (https://portal.gdc.cancer.gov/), GTEx database (https://gtexportal.org/home/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and National Genomics Data Center (https://ngdc.cncb.ac.cn/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35854240", "locator": "sections[35] \"Availability of data and materials\" paragraph 2", "quote": "The datasets generated and/or analyzed during this study are publicly available in the TCGA database (https://portal.gdc.cancer.gov/), GTEx database (https://gtexportal.org/home/), GEO database (https://www.ncbi.nlm.nih.gov/geo/), and National Genomics Data Center (https://ngdc.cncb.ac.cn/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Overall, our study indicates the important role of siglec family in AML and the good performance of our novel siglec-based and aging-related 9-gene signature in predicting AML patient outcome.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35854240", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Overall, our study indicates the important role of siglec family in AML and the good performance of our novel siglec-based and aging-related 9-gene signature in predicting AML patient outcome."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Overall, our study indicates the important role of siglec family in AML and the good performance of our novel siglec-based and aging-related 9-gene signature in predicting AML patient outcome.", "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:35854240"], "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:GSE106291"], "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:GSE106291"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35854240", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12859-022-04841-5", "sha256": 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