Identification of the pyroptosis-related gene signature and risk score model for esophageal squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37071001", "kind": "science-record", "revision": 3, "content_hash": "sha256:59488f4695d698805cc43ca426c217b39bcb8dd3d11ab82360114f53e40eec7c", "created_at": "2026-11-01T19:01:44Z", "updated_at": "2026-11-01T22:09:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of the pyroptosis-related gene signature and risk score model for esophageal squamous cell carcinoma.", "doi": "10.18632/aging.204661", "pmid": "37071001", "authors": ["Minghong Pan", "Yuanyong Wang", "Zhaoyang Wang", "Changjian Shao", "Yingtong Feng", "Peng Ding", "Hongtao Duan", "Xiaoya Ren", "Weixun Duan", "Zhiqiang Ma", "Xiaolong Yan"], "journal": "Aging", "year": 2023, "abstract": "Advanced esophageal squamous cell carcinoma (ESCC) still has a dismal prognostic outcome. However, the current approaches are unable to evaluate patient survival. Pyroptosis represents a novel programmed cell death type which widely investigated in various disorders and can influence tumor growth, migration, and invasion. Furthermore, few existing studies have used pyroptosis-related genes (PRGs) to construct a model for predicting ESCC survival. Therefore, the present study utilized bioinformatics approaches for analyzing ESCC patient data obtained from the TCGA database to construct the prognostic risk model and applied it to the GSE53625 dataset for validation. There were 12 differentially expressed PRGs in healthy and ESCC tissue samples, among which eight were selected through univariate and LASSO cox regression for constructing the prognostic risk model. According to K-M and ROC curve analyses, our eight-gene model might be useful in predicting ESCC prognostic outcomes. Based on the cell validation analysis, C2, CD14, RTP4, FCER3A, and SLC7A7 were expressed higher in KYSE410 and KYSE510 than in normal cells (HET-1A). Hence, ESCC patient prognostic outcomes can be assessed by our PRGs-based risk model. Further, these PRGs may also serve as therapeutic targets.", "claims": [{"id": "claim-method", "statement": "This work conducted univariate regression to screen the prognostic PRGs upon the threshold of P < 0.2 to prevent omissions. Additionally, the R software “glmnet” function was utilized for LASSO analysis to construct a risk score model upon univariate regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37071001", "locator": "sections[5] \"Risk score model establishment based on univariate Cox as well as LASSO Cox regression\" paragraph 2", "quote": "This work conducted univariate regression to screen the prognostic PRGs upon the threshold of P < 0.2 to prevent omissions. Additionally, the R software “glmnet” function was utilized for LASSO analysis to construct a risk score model upon univariate regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We obtained mRNA expression profiles (Workflow Type: HT seq-FPKM) and matched clinical data of ESCC cases at the Cancer Genome Atlas (TCGA) website (https://portal.gdc.cancer.gov/repository) as a training cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37071001", "locator": "sections[3] \"Datasets and patients\" paragraph 2", "quote": "We obtained mRNA expression profiles (Workflow Type: HT seq-FPKM) and matched clinical data of ESCC cases at the Cancer Genome Atlas (TCGA) website (https://portal.gdc.cancer.gov/repository) as a training cohort."}], "state": "complete"}, {"id": "claim-target-result", "statement": "According to K-M and ROC curve analyses, our eight-gene model might be useful in predicting ESCC prognostic outcomes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37071001", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "According to K-M and ROC curve analyses, our eight-gene model might be useful in predicting ESCC prognostic outcomes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "According to K-M and ROC curve analyses, our eight-gene model might be useful in predicting ESCC prognostic outcomes.", "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:37071001"], "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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