System analysis based on the pyroptosis-related genes identifes GSDMD as a novel therapy target for skin cutaneous melanoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37950289", "kind": "science-record", "revision": 3, "content_hash": "sha256:e52ee1115c8cca9f63e20f88dc40d3e3e540a16feb8777792d9611718091430a", "created_at": "2026-11-01T19:02:50Z", "updated_at": "2026-11-01T22:10:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "System analysis based on the pyroptosis-related genes identifes GSDMD as a novel therapy target for skin cutaneous melanoma.", "doi": "10.1186/s12967-023-04513-9", "pmid": "37950289", "authors": ["Shixin Zhao", "Yongkang Zhu", "Hengdeng Liu", "Xuefeng He", "Julin Xie"], "journal": "Journal of translational medicine", "year": 2023, "abstract": "BACKGROUND: Skin cutaneous melanoma (SKCM) is the most aggressive skin cancer, accounting for more than 75% mortality rate of skin-related cancers. As a newly identified programmed cell death, pyroptosis has been found to be closely associated with tumor progression. Nevertheless, the prognostic significance of pyroptosis in SKCM remains elusive.\nMETHODS: A total of 469 SKCM samples and 812 normal samples were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Firstly, differentially expressed pyroptosis-related genes (PRGs) between normal samples and SKCM samples were identified. Secondly, we established a prognostic model based on univariate Cox and LASSO Cox regression analyses, which was validated in the test cohort from GSE65904. Thirdly, a nomogram was used to predict the survival probability of SKCM patients. The R package \"pRRophetic\" was utilized to identify the drug sensitivity between the low- and high-risk groups. Tumor immune infiltration was evaluated using \"immuneeconv\" R package. Finally, the function of GSDMD and SB525334 was explored in A375 and A2058 cells.\nRESULTS: Based on univariate Cox and LASSO regression analyses, we established a prognostic model with identified eight PRGs (AIM2, CASP3, GSDMA, GSDMC, GSDMD, IL18, NLRP3, and NOD2), which was validated in the test cohort. SKCM patients were divided into low- and high-risk groups based on the median of risk score. Kaplan-Meier survival analysis showed that high-risk patients had shorter overall survival than low-risk patients. Additionally, time-dependent ROC curves validated the accuracy of the risk model in predicting the prognosis of SKCM. More importantly, 4 small molecular compounds (SB525334, SR8278, Gemcitabine, AT13387) were identified, which might be potential drugs for patients in different risk groups. Finally, overexpression of GSDMD and SB525334 treatment inhibit the proliferation, migration, and invasion of SKCM cells.\nCONCLUSION: In this study, we constructed a prognostic model based on PRGs and identified GSDMD as a potential therapeutic target, which provide new insights into SKCM treatment.", "claims": [{"id": "claim-method", "statement": "Secondly, we established a prognostic model based on univariate Cox and LASSO Cox regression analyses, which was validated in the test cohort from GSE65904.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37950289", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Secondly, we established a prognostic model based on univariate Cox and LASSO Cox regression analyses, which was validated in the test cohort from GSE65904."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data and R script in this study are available from the corresponding author upon reasonable request. Publicly available datasets were analyzed in this study, these can be found in TCGA (https://portal.gdc.cancer.gov) and GTEx (https://www.gtexportal.org/home/) databases, and GEO (GSE65904).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37950289", "locator": "sections[44] \"notes\" paragraph 1", "quote": "All data and R script in this study are available from the corresponding author upon reasonable request. Publicly available datasets were analyzed in this study, these can be found in TCGA (https://portal.gdc.cancer.gov) and GTEx (https://www.gtexportal.org/home/) databases, and GEO (GSE65904)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Time dependent ROC curves indicated that the prognostic accuracy of OS was 0.693 at 1-year, 0.699 at 5-year, and 0.709 at 10-year (Fig. 3F).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37950289", "locator": "sections[28] \"Construction of a prognostic model based on PRGs in the training cohort\" paragraph 3", "quote": "Time dependent ROC curves indicated that the prognostic accuracy of OS was 0.693 at 1-year, 0.699 at 5-year, and 0.709 at 10-year (Fig. 3F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Time dependent ROC curves indicated that the prognostic accuracy of OS was 0.693 at 1-year, 0.699 at 5-year, and 0.709 at 10-year (Fig. 3F).", "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:37950289"], "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:GSE65904"], "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:GSE65904"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37950289", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-023-04513-9", "sha256": "sha256:c699a11d5b4863b1240195af3996e7c77580cf57c9efc8da788525572fc00d61", "bytes": 47841, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12967-023-04513-9", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10636830", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE65904", "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=GSE65904", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE65904", "version": null, "note": "All data and R script in this study are available from the corresponding author upon reasonable request. 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This study followed the Helsinki Declaratio", "s and CD4 levels (Fig. 8B). Furthermore, our immunofluorescence analysis demonstrated that GSDMD was highly", "en GSDMD and CD4. C Representative images of immunofluorescence and quantitative analysis demonstrated that"], "order_basis": "paper_text_order:2950", "id": "assay:1"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Secondly, we established a prognostic model based on univariate Cox and LASSO Cox regression analyses, which was validated in the test cohort from GSE65904."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}