Development and Validation of a Robust Pyroptosis-Related Signature for Predicting Prognosis and Immune Status in Patients with Colon Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34840571", "kind": "science-record", "revision": 5, "content_hash": "sha256:3f8b1a3bacebbc0224ed8f13eb15b6c9f9249134a33e4f3dcb5be576f5789187", "created_at": "2026-11-01T17:36:32Z", "updated_at": "2026-11-01T22:47:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and Validation of a Robust Pyroptosis-Related Signature for Predicting Prognosis and Immune Status in Patients with Colon Cancer.", "doi": "10.1155/2021/5818512", "pmid": "34840571", "authors": ["Zhicheng Zhuang", "Huajun Cai", "Hexin Lin", "Bingjie Guan", "Yong Wu", "Yiyi Zhang", "Xing Liu", "Jinfu Zhuang", "Guoxian Guan"], "journal": "Journal of oncology", "year": 2021, "abstract": "BACKGROUND: Pyroptosis has been confirmed as a type of inflammatory programmed cell death in recent years. However, the prognostic role of pyroptosis in colon cancer (CC) remains unclear.\nMETHODS: Dataset TCGA-COAD which came from the TCGA portal was taken as the training cohort. GSE17538 from the GEO database was treated as validation cohorts. Differential expression genes (DEGs) between normal and tumor tissues were confirmed. Patients were classified into two subgroups according to the expression characteristics of pyroptosis-related DEGs. The LASSO regression analysis was used to build the best prognostic signature, and its reliability was validated using Kaplan-Meier, ROC, PCA, and t-SNE analyses. And a nomogram based on the multivariate Cox analysis was developed. The enrichment analysis was performed in the GO and KEGG to investigate the potential mechanism. In addition, we explored the difference in the abundance of infiltrating immune cells and immune microenvironment between high- and low-risk groups. And we also predicted the association of common immune checkpoints with risk scores. Finally, we verified the expression of the pyroptosis-related hub gene at the protein level by immunohistochemistry.\nRESULTS: A total of 23 pyroptosis-related DEGs were identified in the TCGA cohort. Patients were classified into two molecular clusters (MC) based on DEGs. Kaplan-Meier survival analysis indicated that patients with MC1 represented significantly poorer OS than patients with MC2. 13 overall survival- (OS-) related DEGs in MCs were used to construct the prognostic signature. Patients in the high-risk group exhibited poorer OS compared to those in the low-risk group. Combined with the clinical features, the risk score was found to be an independent prognostic factor of CC patients. The above results are verified in the external dataset GSE17538. A nomogram was established and showed excellent performance. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that the varied prognostic performance between high- and low-risk groups may be related to the immune response mediated by local inflammation. Further analysis showed that the high-risk group has stronger immune cell infiltration and lower tumor purity than the low-risk group. Through the correlation between risk score and immune checkpoint expression, T-cell immunoglobulin and mucin domain-containing protein 3 (TIM-3) was predicted as a potential therapeutic target for the high-risk group.\nCONCLUSION: The 13-gene signature was associated with OS, immune cells, tumor purity, and immune checkpoints in CC patients, and it could provide the basis for immunotherapy and predicting prognosis and help clinicians make decisions for individualized treatment.", "claims": [{"id": "claim-method", "statement": "The “limma” package was used to identify DEGs between tumor and adjacent normal tissues in the TCGA cohort with a P value <0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34840571", "locator": "sections[4] \"2.2. Identification of Differentially Expressed PRGs\" paragraph 2", "quote": "The “limma” package was used to identify DEGs between tumor and adjacent normal tissues in the TCGA cohort with a P value <0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw transcriptome data of this study are available in the TCGA database (TCGA-COAD cohorts) (https://portal.gdc.cancer.gov/) and the GEO database (GSE17538), which are publicly available databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34840571", "locator": "sections[20] \"Data Availability\" paragraph 2", "quote": "The raw transcriptome data of this study are available in the TCGA database (TCGA-COAD cohorts) (https://portal.gdc.cancer.gov/) and the GEO database (GSE17538), which are publicly available databases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 23 pyroptosis-related DEGs were identified in the TCGA cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34840571", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 23 pyroptosis-related DEGs were identified in the TCGA cohort."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 23 pyroptosis-related DEGs were identified in the TCGA cohort.", "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:34840571"], "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:GSE17538"], "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:GSE17538"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34840571", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2021/5818512", "sha256": "sha256:b0b65f212faf7d210b05962cb225f023de968e012eb3cb5c965f1a258ed270f6", "bytes": 39371, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2021/5818512", "version": null, "note": "Full article fetched from PMC; 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