Pyroptosis-related lncRNAs: A novel prognosis signature of colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36531020", "kind": "science-record", "revision": 3, "content_hash": "sha256:d81d5ed04f454d22c1bc0488cbf12f4c74e2106b0cde9053e7d6461dc4ce17e4", "created_at": "2026-11-01T19:01:03Z", "updated_at": "2026-11-01T22:08:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Pyroptosis-related lncRNAs: A novel prognosis signature of colorectal cancer.", "doi": "10.3389/fonc.2022.983895", "pmid": "36531020", "authors": ["Xing Cai", "Xiaoqing Liang", "Kun Wang", "Yin Liu", "Mengdi Hao", "Huimin Li", "Xiaofang Dai", "Lei Ding"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Pyroptosis is a newly discovered programmed cell death mechanism involved in tumorigenesis. Long non-coding RNAs (lncRNAs) have been implicated in colorectal cancer (CRC). However, the potential role of pyroptosis-related lncRNAs (PRLs) in CRC remains unelucidated. Therefore, we retrieved transcriptomic data of CRC patients from The Cancer Genome Atlas (TCGA). With the use of univariate and multivariate Cox proportional hazards regression models and the random forest algorithm, a new risk model was constructed based on eight PRLs: Z99289.2, FENDRR, CCDC144NL-ASL, TEX41, MNX1-AS1, NKILA, LINC02798, and LINC02381. Then, according to the Kaplan-Meier plots, the relationship of PRLs with the survival of CRC patients was explored and validated with our risk model in external datasets (Gene Expression Omnibus (GEO) databases; GEO17536, n = 177, and GSE161158, n = 250). To improve its clinical utility, a nomogram combining PRLs that could predict the clinical outcome of CRC patients was established. A full-spectrum immune landscape of CRC patients mediated by PRLs could be described. The PRLs were stratified into two molecular subtypes involved in immune modulators, immune infiltration of tumor immune microenvironment, and inflammatory pathways. Afterward, Tumor Immune Dysfunction and Exclusion (TIDE) and microsatellite instability (MSI) scores were analyzed. Three independent methods were applied to predict PRL-related sensitivity to chemotherapeutic drugs. Our comprehensive analysis of PRLs in CRC patients demonstrates a potential role of PRLs in predicting response to treatment and prognosis of CRC patients, which may provide a better understanding of molecular mechanisms underlying CRC pathogenesis and facilitate the development of effective immunotherapy.", "claims": [{"id": "claim-method", "statement": "Then, a univariate Cox proportional risk regression model was used to select candidate survival-related PRLs, and p< 0.01 was set as the threshold ( Supplementary Table S4 ). Then, the random survival forests-variable hunting (RSFVH) algorithm was used to further filter the genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36531020", "locator": "sections[4] \"The construction and validation of pyroptosis-related lncRNA prognostic model\" paragraph 2", "quote": "Then, a univariate Cox proportional risk regression model was used to select candidate survival-related PRLs, and p< 0.01 was set as the threshold ( Supplementary Table S4 ). Then, the random survival forests-variable hunting (RSFVH) algorithm was used to further filter the genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36531020", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The eight-PRL model predicted overall survival in the TCGA cohort with AUCs of 0.660, 0.768, and 0.734 at 1, 2, and 5 years, respectively, and its prognostic performance was supported in two external GEO cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36531020", "locator": "sections[11] \"Survival prediction performance of the model\" paragraph 2", "quote": "According to the ROC curves, PRLs might predict OS in TCGA cohort, with an AUC at 1, 2, and 5 years of 0.660, 0.768, and 0.734, respectively ( Figure 5C )."}, {"asset": "source:pmid:36531020", "locator": "sections[11] \"Survival prediction performance of the model\" paragraph 2", "quote": "Patients in the validation cohorts with a higher risk score had a lower survival rate compared to those with a low-risk score ( Figures 5D–I )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The eight-PRL model predicted overall survival in the TCGA cohort with AUCs of 0.660, 0.768, and 0.734 at 1, 2, and 5 years, respectively, and its prognostic performance was supported in two external GEO cohorts.", "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:36531020"], "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:GSE161158"], "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:GSE161158"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36531020", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2022.983895", "sha256": "sha256:463c696f9bdc5151dd625656d1b236482ed3e02c9b9a0b61addc17d7d215c1f2", "bytes": 52619, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2022.983895", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9748486", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE161158", "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=GSE161158", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE161158", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM4890607", "GSM4890608", "GSM4890609", "GSM4890610", "GSM4890611", "GSM4890612", "GSM4890613", "GSM4890614", "GSM4890615", "GSM4890616", "GSM4890617", "GSM4890618", "GSM4890619", "GSM4890620", "GSM4890621", "GSM4890622", "GSM4890623", "GSM4890624", "GSM4890625", "GSM4890626", "GSM4890627", "GSM4890628", "GSM4890629", "GSM4890630", "GSM4890631", "GSM4890632", "GSM4890633", "GSM4890634", "GSM4890635", "GSM4890636", "GSM4890637", "GSM4890638", "GSM4890639", "GSM4890640", "GSM4890641", "GSM4890642", "GSM4890643", "GSM4890644", "GSM4890645", "GSM4890646", "GSM4890647", "GSM4890648", "GSM4890649", "GSM4890650", "GSM4890651", "GSM4890652", "GSM4890653", "GSM4890654", "GSM4890655", "GSM4890656", "GSM4890657", "GSM4890658", "GSM4890659", "GSM4890660", "GSM4890661", "GSM4890662", "GSM4890663", "GSM4890664", "GSM4890665", "GSM4890666", "GSM4890667", "GSM4890668", "GSM4890669", "GSM4890670", "GSM4890671", "GSM4890672", "GSM4890673", "GSM4890674", "GSM4890675", "GSM4890676", "GSM4890677", "GSM4890678", "GSM4890679", "GSM4890680", "GSM4890681", "GSM4890682", "GSM4890683", "GSM4890684", "GSM4890685", "GSM4890686", "GSM4890687", "GSM4890688", "GSM4890689", "GSM4890690", "GSM4890691", "GSM4890692", "GSM4890693", "GSM4890694", "GSM4890695", "GSM4890696", "GSM4890697", "GSM4890698", "GSM4890699", "GSM4890700", "GSM4890701", "GSM4890702", "GSM4890703", "GSM4890704", "GSM4890705", "GSM4890706", "GSM4890707", "GSM4890708", "GSM4890709", "GSM4890710", "GSM4890711", "GSM4890712", "GSM4890713", "GSM4890714", "GSM4890715", "GSM4890716", "GSM4890717", "GSM4890718", "GSM4890719", "GSM4890720", "GSM4890721", "GSM4890722", "GSM4890723", "GSM4890724", "GSM4890725", "GSM4890726", "GSM4890727", "GSM4890728", "GSM4890729", "GSM4890730", "GSM4890731", "GSM4890732", "GSM4890733", "GSM4890734", "GSM4890735", "GSM4890736", "GSM4890737", "GSM4890738", "GSM4890739", "GSM4890740", "GSM4890741", "GSM4890742", "GSM4890743", "GSM4890744", "GSM4890745", "GSM4890746", "GSM4890747", "GSM4890748", "GSM4890749", "GSM4890750", "GSM4890751", "GSM4890752", "GSM4890753", "GSM4890754", "GSM4890755", "GSM4890756", "GSM4890757", "GSM4890758", "GSM4890759", "GSM4890760", "GSM4890761", "GSM4890762", "GSM4890763", "GSM4890764", "GSM4890765", "GSM4890766", "GSM4890767", "GSM4890768", "GSM4890769", "GSM4890770", "GSM4890771", "GSM4890772", "GSM4890773", "GSM4890774", "GSM4890775", "GSM4890776", "GSM4890777", "GSM4890778", "GSM4890779", "GSM4890780", "GSM4890781", "GSM4890782", "GSM4890783", "GSM4890784", "GSM4890785", "GSM4890786", "GSM4890787", "GSM4890788", "GSM4890789", "GSM4890790", "GSM4890791", "GSM4890792", "GSM4890793", "GSM4890794", "GSM4890795", "GSM4890796", "GSM4890797", "GSM4890798", "GSM4890799", "GSM4890800", "GSM4890801", "GSM4890802", "GSM4890803", "GSM4890804", "GSM4890805", "GSM4890806", "GSM4890807", "GSM4890808", "GSM4890809", "GSM4890810", "GSM4890811", "GSM4890812", "GSM4890813", "GSM4890814", "GSM4890815", "GSM4890816", "GSM4890817", "GSM4890818", "GSM4890819", "GSM4890820", "GSM4890821", "GSM4890822", "GSM4890823", "GSM4890824", "GSM4890825", "GSM4890826", "GSM4890827", "GSM4890828", "GSM4890829", "GSM4890830", "GSM4890831", "GSM4890832", "GSM4890833", "GSM4890834", "GSM4890835", "GSM4890836", "GSM4890837", "GSM4890838", "GSM4890839", "GSM4890840", "GSM4890841", "GSM4890842", "GSM4890843", "GSM4890844", "GSM4890845", "GSM4890846", "GSM4890847", "GSM4890848", "GSM4890849", "GSM4890850", "GSM4890851", "GSM4890852", "GSM4890853", "GSM4890854", "GSM4890855", "GSM4890856"], "sample_count": 250, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "A SMAD4-modulated gene profile predicts disease-free survival in stage II and III colorectal cancer", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE17536", "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=GSE17536", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17536", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM437093", "GSM437094", "GSM437095", "GSM437096", "GSM437097", "GSM437098", "GSM437099", "GSM437100", "GSM437101", "GSM437102", "GSM437103", "GSM437104", "GSM437105", "GSM437106", "GSM437107", "GSM437108", "GSM437109", "GSM437110", "GSM437111", "GSM437112", "GSM437113", "GSM437114", "GSM437115", "GSM437116", "GSM437117", "GSM437118", "GSM437119", "GSM437120", "GSM437121", "GSM437122", "GSM437123", "GSM437124", "GSM437125", "GSM437126", "GSM437127", "GSM437128", "GSM437129", "GSM437130", "GSM437131", "GSM437132", "GSM437133", "GSM437134", "GSM437135", "GSM437136", "GSM437137", "GSM437138", "GSM437139", "GSM437140", "GSM437141", "GSM437142", "GSM437143", "GSM437144", "GSM437145", "GSM437146", "GSM437147", "GSM437148", "GSM437149", "GSM437150", "GSM437151", "GSM437152", "GSM437153", "GSM437154", "GSM437155", "GSM437156", "GSM437157", "GSM437158", "GSM437159", "GSM437160", "GSM437161", "GSM437162", "GSM437163", "GSM437164", "GSM437165", "GSM437166", "GSM437167", "GSM437168", "GSM437169", "GSM437170", "GSM437171", "GSM437172", "GSM437173", "GSM437174", "GSM437175", "GSM437176", "GSM437177", "GSM437178", "GSM437179", "GSM437180", "GSM437181", "GSM437182", "GSM437183", "GSM437184", "GSM437185", "GSM437186", "GSM437187", "GSM437188", "GSM437189", "GSM437190", "GSM437191", "GSM437192", "GSM437193", "GSM437194", "GSM437195", "GSM437196", "GSM437197", "GSM437198", "GSM437199", "GSM437200", "GSM437201", "GSM437202", "GSM437203", "GSM437204", "GSM437205", "GSM437206", "GSM437207", "GSM437208", "GSM437209", "GSM437210", "GSM437211", "GSM437212", "GSM437213", "GSM437214", "GSM437215", "GSM437216", "GSM437217", "GSM437218", "GSM437219", "GSM437220", "GSM437221", "GSM437222", "GSM437223", "GSM437224", "GSM437225", "GSM437226", "GSM437227", "GSM437228", "GSM437229", "GSM437230", "GSM437231", "GSM437232", "GSM437233", "GSM437234", "GSM437235", "GSM437236", "GSM437237", "GSM437238", "GSM437239", "GSM437240", "GSM437241", "GSM437242", "GSM437243", "GSM437244", "GSM437245", "GSM437246", "GSM437247", "GSM437248", "GSM437249", "GSM437250", "GSM437251", "GSM437252", "GSM437253", "GSM437254", "GSM437255", "GSM437256", "GSM437257", "GSM437258", "GSM437259", "GSM437260", "GSM437261", "GSM437262", "GSM437263", "GSM437264", "GSM437265", "GSM437266", "GSM437267", "GSM437268", "GSM437269"], "sample_count": 177, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Metastasis Gene Expression Profile Predicts Recurrence and Death in Colon Cancer Patients (Moffitt Samples)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE161158 not downloaded", "asset:data:geo:GSE161158 licence not read", "asset:data:geo:GSE17536 not downloaded", "asset:data:geo:GSE17536 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Then, a univariate Cox proportional risk regression model was used to select candidate survival-related PRLs, and p< 0.01 was set as the threshold ( Supplementary Table S4 ). Then, the random survival forests-variable hunting (RSFVH) algorithm was used to further filter the genes."}}, {"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": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random forest"}}], "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": ["GSE17536", "GSE161158"], "resolved_accessions": ["GSE17536", "GSE161158"], "statements": ["The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors."], "basis": "The paper names GSE17536, GSE161158 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:36531020", "data:geo:GSE161158"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE161158"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:19:59Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 15539, "completion_tokens": 2086, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:36531020", "type": "paper", "label": "pmid:36531020"}, {"id": "source:pmid:36531020", "type": "manuscript", "label": "PMID 36531020 full text"}, {"id": "data:geo:GSE161158", "type": "dataset", "label": "GEO GSE161158"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36531020", "type": "paper", "label": "pmid:36531020"}, {"id": "source:pmid:36531020", "type": "manuscript", "label": "PMID 36531020 full text"}, {"id": "data:geo:GSE161158", "type": "dataset", "label": "GEO GSE161158"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36531020", "type": "extraction", "used": ["source:pmid:36531020"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE161158", "type": "accession_resolution", "used": ["data:geo:GSE161158"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:36531020", "parent_hash": "sha256:0aa0d8b01c201f242af1bd0359a7793eb8cb7ba7be1284d99d60217bb868bd8c", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE161158 licence not read", "asset:data:geo:GSE161158 not downloaded", "asset:data:geo:GSE17536 licence not read", "asset:data:geo:GSE17536 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "methylation_array", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["score subgroup. (Analysis method: CIBERSORT, EPIC, Estimate, MCP-counter, Quanti-seq, TIMER, x"], "order_basis": "paper_text_order:4787", "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": ["Then, a univariate Cox proportional risk regression model was used to select candidate survival-related PRLs, and p< 0.01 was set as the threshold ( Supplementary Table S4 ). Then, the random survival forests-variable hunting (RSFVH) algorithm was used to further filter the genes."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}