A novel prognostic model based on pyroptosis-related genes for multiple myeloma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36823654", "kind": "science-record", "revision": 3, "content_hash": "sha256:9b26a36b9b253d74de9fc89c760ecbc5e5e6a8632cdf8c23a01154ecf9b132ef", "created_at": "2026-11-01T19:01:26Z", "updated_at": "2026-11-01T22:08:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel prognostic model based on pyroptosis-related genes for multiple myeloma.", "doi": "10.1186/s12920-023-01455-5", "pmid": "36823654", "authors": ["Cuiling Zhang", "Sungui Wu", "Bing Chen"], "journal": "BMC medical genomics", "year": 2023, "abstract": "BACKGROUND: Multiple myeloma (MM) is an incurable and relapse-prone disease with apparently prognostic heterogeneity. At present, the risk stratification of myeloma is still incomplete. Pyroptosis, a type of programmed cell death, has been shown to regulate tumor growth and may have potential prognostic value. However, the role of pyroptosis-related genes (PRGs) in MM remains undetermined. The aims of this study were to identify potential prognostic biomarkers and to construct a predictive model related to PRGs.\nMETHODS: Sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Non-negative matrix factorization (NMF) was performed to identify molecular subtype screening. LASSO regression was used to screen for prognostic markers, and then a risk score model was constructed. The Maxstat package was utilized to calculate the optimal cutoff value, according to which patients were divided into a high-risk group and a low-risk group, and the survival curves were plotted using the Kaplan-Meier (K-M) method. Nomograms and calibration curves were established using the rms package.\nRESULTS: A total of 33 PRGs were extracted from the TCGA database underlying which 4 MM molecular subtypes were defined. Patients in cluster 1 had poorer survival than those in cluster 2 (p = 0.035). A total of 9 PRGs were screened out as prognostic markers, and the predictive ability of the 9-gene risk score for 3-year survival was best (AUC = 0.658). Patients in the high-risk group had worse survival than those in the low-risk group (p < 0.001), which was consistent with the results verified by the GSE2658 dataset. The nomogram constructed by gender, age, International Staging System (ISS) stage, and risk score had the best prognostic predictive performance with a c-index of 0.721.\nCONCLUSION: Our model could enhance the predictive ability of ISS staging and give a reference for clinical decision-making. The new, prognostic, and pyroptosis-related markers screened out by us may facilitate the development of novel risk stratification for MM.\nCLINICAL TRIAL REGISTRATION: Not applicable.", "claims": [{"id": "claim-method", "statement": "We used univariate Cox regression to screen for PRGs associated with the survival of patients with MM from the MMRF project, and the LASSO regression to screen for prognostic markers.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36823654", "locator": "sections[8] \"Identification of prognostic marker (LASSO analysis)\" paragraph 2", "quote": "We used univariate Cox regression to screen for PRGs associated with the survival of patients with MM from the MMRF project, and the LASSO regression to screen for prognostic markers."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyses during the current study are available in the TCGA and GEO Database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36823654", "locator": "sections[24] \"Data availability\" paragraph 2", "quote": "The datasets generated during and/or analyses during the current study are available in the TCGA and GEO Database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO regression was used to screen for prognostic markers from TCGA sequencing and clinical data, and the resulting nine-gene risk score model was validated with the GSE2658 dataset.", "kind": "quantitative", "target": {"value": 0.658, "unit": "auc", "tolerance": null, "source": "A total of 9 PRGs were screened out as prognostic markers, and the predictive ability of the 9-gene risk score for 3-year survival was best (AUC = 0.658)."}, "evidence": [{"asset": "source:pmid:36823654", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases."}, {"asset": "source:pmid:36823654", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "LASSO regression was used to screen for prognostic markers, and then a risk score model was constructed."}, {"asset": "source:pmid:36823654", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 9 PRGs were screened out as prognostic markers, and the predictive ability of the 9-gene risk score for 3-year survival was best (AUC = 0.658)."}, {"asset": "source:pmid:36823654", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Patients in the high-risk group had worse survival than those in the low-risk group (p < 0.001), which was consistent with the results verified by the GSE2658 dataset."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "LASSO regression was used to screen for prognostic markers from TCGA sequencing and clinical data, and the resulting nine-gene risk score model was validated with the GSE2658 dataset.", "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:36823654"], "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:GSE2658"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_mann_whitney", "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:GSE2658"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_mann_whitney"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36823654", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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