A Novel Pyroptosis-Associated Long Noncoding RNA Signature to Predict the Prognosis of Patients with Colorectal Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34611426", "kind": "science-record", "revision": 4, "content_hash": "sha256:f3e5e2e2c446083c7fd825ebb61b18011ffefac755398a49efe28c6b093709af", "created_at": "2026-11-01T17:36:15Z", "updated_at": "2026-11-01T22:47:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Pyroptosis-Associated Long Noncoding RNA Signature to Predict the Prognosis of Patients with Colorectal Cancer.", "doi": "10.2147/IJGM.S328842", "pmid": "34611426", "authors": ["Sijun Chen", "Jianwei Zhu", "Xiaofei Zhi"], "journal": "International journal of general medicine", "year": 2021, "abstract": "PURPOSE: Pyroptosis plays an important role in tumor progression. However, there is no pyroptosis-associated long noncoding RNA (lncRNA) signature to predict the prognosis of patients with colorectal cancer (CRC).\nMATERIALS AND METHODS: The RNA sequencing data (RNA-seq) and corresponding clinical information relating to CRC patients were obtained from the Cancer Genome Atlas (TCGA) database and the GSE39582 dataset. Univariate Cox regression analysis was used to identify pyroptosis-associated lncRNAs linked to CRC prognosis. Subsequently, multivariate Cox regression analysis was performed to construct a pyroptosis-associated lncRNAs signature within the TCGA cohort, which was then validated using the GSE39582 dataset. We used Kaplan-Meier (K-M) analysis, principal component analysis (PCA), and receiver operating characteristic curve (ROC) analysis to evaluate our novel lncRNA signature. Finally, gene set enrichment analysis (GSEA) was performed to explore the potential function of the lncRNA signature.\nRESULTS: We constructed a pyroptosis-associated lncRNA signature comprising four lncRNAs (ELFN1-AS1, PCAT6, TNRC6C-AS1, and ZEB1-AS1). CRC patients were subdivided into high- and low-risk groups based on median risk scores. The results of the K-M, PCA, and ROC analyses showed that this signature could accurately predict the prognosis of CRC patients. Univariate and multivariate Cox regression analyses showed that the pyroptosis-associated signature was an independent prognostic factor. Functional analysis suggested that tumor-associated pathways were enriched for in the high-risk CRC patient group.\nCONCLUSION: Our study established an effective prognostic signature for CRC patients that may represent a potential therapeutic target.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis was used to identify pyroptosis-associated lncRNAs linked to CRC prognosis. Subsequently, multivariate Cox regression analysis was performed to construct a pyroptosis-associated lncRNAs signature within the TCGA cohort, which was then validated using the GSE39582 dataset. We used Kaplan–Meier (K-M) analysis, principal component analysis (PCA), and receiver operating characteristic curve (ROC) analysis to evaluate our novel lncRNA signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34611426", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Univariate Cox regression analysis was used to identify pyroptosis-associated lncRNAs linked to CRC prognosis. Subsequently, multivariate Cox regression analysis was performed to construct a pyroptosis-associated lncRNAs signature within the TCGA cohort, which was then validated using the GSE39582 dataset. We used Kaplan–Meier (K-M) analysis, principal component analysis (PCA), and receiver operating characteristic curve (ROC) analysis to evaluate our novel lncRNA signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNA sequencing data (RNA-seq) and corresponding clinical information associated with the training cohort were downloaded from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34611426", "locator": "sections[3] \"Data Sources\" paragraph 2", "quote": "The RNA sequencing data (RNA-seq) and corresponding clinical information associated with the training cohort were downloaded from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We constructed a pyroptosis-associated lncRNA signature comprising four lncRNAs (ELFN1-AS1, PCAT6, TNRC6C-AS1, and ZEB1-AS1).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:34611426", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We constructed a pyroptosis-associated lncRNA signature comprising four lncRNAs (ELFN1-AS1, PCAT6, TNRC6C-AS1, and ZEB1-AS1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We constructed a pyroptosis-associated lncRNA signature comprising four lncRNAs (ELFN1-AS1, PCAT6, TNRC6C-AS1, and ZEB1-AS1).", "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:34611426"], "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:GSE39582"], "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:GSE39582"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:34611426", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJGM.S328842", "sha256": 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"quote": "Univariate Cox regression analysis was used to identify pyroptosis-associated lncRNAs linked to CRC prognosis. Subsequently, multivariate Cox regression analysis was performed to construct a pyroptosis-associated lncRNAs signature within the TCGA cohort, which was then validated using the GSE39582 dataset. We used Kaplan–Meier (K-M) analysis, principal component analysis (PCA), and receiver operating characteristic curve (ROC) analysis to evaluate our novel lncRNA signature."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-3", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "principal component analysis"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE39582"], "resolved_accessions": ["GSE39582"], "statements": ["The RNA sequencing data (RNA-seq) and corresponding clinical information associated with the training cohort were downloaded from the Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database."], "basis": "The paper names GSE39582 in gene expression omnibus (GEO: https://www.ncbi.nlm.nih.gov/geo/) and the accession resolves in the repository. 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MATERIALS AND METHODS: The RNA sequencing data (RNA-seq) and corresponding clinical in"], "order_basis": "paper_text_order:316", "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": ["Univariate Cox regression analysis was used to identify pyroptosis-associated lncRNAs linked to CRC prognosis. Subsequently, multivariate Cox regression analysis was performed to construct a pyroptosis-associated lncRNAs signature within the TCGA cohort, which was then validated using the GSE39582 dataset. We used Kaplan–Meier (K-M) analysis, principal component analysis (PCA), and receiver operating characteristic curve (ROC) analysis to evaluate our novel lncRNA signature."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}