Identification and validation of pyroptosis patterns with a novel quantification system for the prediction of prognosis in lung squamous cell carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39830751", "kind": "science-record", "revision": 4, "content_hash": "sha256:67759b4ebcb9ed50a6d94631c5b0ef90a7070640aa692ad96cef27b6c127b736", "created_at": "2026-11-01T20:30:07Z", "updated_at": "2026-11-01T22:56:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of pyroptosis patterns with a novel quantification system for the prediction of prognosis in lung squamous cell carcinoma.", "doi": "10.21037/tlcr-24-1003", "pmid": "39830751", "authors": ["Xianyu Qin", "Jiayan Wu", "Fei Qin", "Yuzhen Zheng", "Junguo Chen", "Zui Liu", "Jian Tan", "Weijie Cai", "Shiyun He", "Bozhu Jian", "Haosheng Zheng", "Hongying Liao"], "journal": "Translational lung cancer research", "year": 2024, "abstract": "Background: The role of pyroptosis in lung squamous cell carcinoma (LUSC) remains unclear. This study aimed to screen pyroptosis-related genes (PRGs) and construct a model to investigate the immune infiltration, gene mutations, and immune response of patients of LUSC.\nMethods: We conducted a comprehensive evaluation of pyroptosis patterns in patients with LUSC with 51 PRGs. Pyroptosis-related clusters were identified using consistency clustering algorithm. Differences in the biologic and clinical characteristics between the clusters were analyzed. Cox regression analysis was performed to screen for differentially expressed genes (DEGs) related to prognosis, and a principal component analysis (PCA) algorithm was used to construct a model based on these genes. The pyroptosis score was calculated for each tumor sample, and the samples were classified into high- and low-score groups based on the score. The disparities in survival, single-nucleotide variation (SNV), copy number variation (CNV), and immunotherapy response between high-score and low-score groups were analyzed.\nResults: A total of 51 PRGs were used to classify LUSC samples into three pyroptosis clusters with significant differences in survival (P=0.005). Based on the 390 DEGs between the three clusters, two distinct pyroptosis gene clusters were identified by secondary clustering, with significant differences in prognosis (P=0.005). A pyroptosis scoring model was established to evaluate the regulatory patterns of PRGs, and patients were stratified into two groups with high and low scores, using the median pyroptosis score as the cutoff. The survival analyses indicated that patients with high scores had worse prognoses in The Cancer Genome Atlas (TCGA)-LUSC cohort (P=0.002), which was further supported by the analysis of the GSE37745 (P=0.006) and GSE135222 datasets (P=0.02).\nConclusions: The quantification of pyroptosis patterns was found to be important in predicting prognosis and devising personalized treatment strategies in patients with LUSC.", "claims": [{"id": "claim-method", "statement": "First, univariate Cox regression was performed to screen DEGs correlated with prognosis. Second, PCA was conducted based on the DEGs significantly related to prognosis. The sums of principal components 1 and 2 were calculated as the pyroptosis score of each sample as follows:", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39830751", "locator": "sections[10] \"Construction of a prognostic pyroptosis score\" paragraph 2", "quote": "First, univariate Cox regression was performed to screen DEGs correlated with prognosis. Second, PCA was conducted based on the DEGs significantly related to prognosis. The sums of principal components 1 and 2 were calculated as the pyroptosis score of each sample as follows:"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression data, somatic mutation data, copy number variation (CNV) data, and corresponding clinical parameters of LUSC were obtained from TCGA (https://portal.gdc.cancer.gov/) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39830751", "locator": "sections[4] \"Data sources\" paragraph 2", "quote": "The gene expression data, somatic mutation data, copy number variation (CNV) data, and corresponding clinical parameters of LUSC were obtained from TCGA (https://portal.gdc.cancer.gov/) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The survival analyses indicated that patients with high scores had worse prognoses in The Cancer Genome Atlas (TCGA)-LUSC cohort (P=0.002), which was further supported by the analysis of the GSE37745 (P=0.006) and GSE135222 datasets (P=0.02).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39830751", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The survival analyses indicated that patients with high scores had worse prognoses in The Cancer Genome Atlas (TCGA)-LUSC cohort (P=0.002), which was further supported by the analysis of the GSE37745 (P=0.006) and GSE135222 datasets (P=0.02)."}, {"asset": "source:pmid:39830751", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The quantification of pyroptosis patterns was found to be important in predicting prognosis and devising personalized treatment strategies in patients with LUSC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The survival analyses indicated that patients with high scores had worse prognoses in The Cancer Genome Atlas (TCGA)-LUSC cohort (P=0.002), which was further supported by the analysis of the GSE37745 (P=0.006) and GSE135222 datasets (P=0.02).", "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:39830751"], "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:GSE135222"], "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:GSE135222"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39830751", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tlcr-24-1003", "sha256": 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