Prognosis analysis of necroptosis-related genes in colorectal cancer based on bioinformatic analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36046241", "kind": "science-record", "revision": 4, "content_hash": "sha256:09e411b3fcd67291aea4b1a178599225593fef250a418cccdcefcc5392d68d38", "created_at": "2026-11-01T18:15:13Z", "updated_at": "2026-11-01T22:07:24Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognosis analysis of necroptosis-related genes in colorectal cancer based on bioinformatic analysis.", "doi": "10.3389/fgene.2022.955424", "pmid": "36046241", "authors": ["Xiaojie Liang", "Zhaoxiang Cheng", "Xinhao Chen", "Jun Li"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Colorectal cancer (CRC) is one gastrointestinal malignancy, accounting for 10% of cancer diagnoses and cancer-related deaths worldwide each year. Therefore, it is urgent to identify genes involved in CRC predicting the prognosis. Methods: CRC's data were acquired from the Gene Expression Omnibus (GEO) database (GSE39582 and GSE41258 datasets) and The Cancer Genome Atlas (TCGA) database. The differentially expressed necroptosis-related genes (DENRGs) were sorted out between tumor and normal tissues. Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients' overall survival and to construct a prognostic biomarker. The effectiveness of this biomarker was assessed by the Kaplan-Meier curve and the receiver operating characteristic (ROC) analysis. The GSE39582 dataset was utilized as external validation for the prognostic signature. Moreover, using univariate and multivariate Cox regression analyses, independent prognostic factors were identified to construct a prognostic nomogram. Next, signaling pathways regulated by the signature were explored through the gene set enrichment analysis (GSEA). The single sample gene set enrichment analysis (ssGSEA) algorithm and tumor immune dysfunction and exclusion (TIDE) were used to explore immune correlation in the two groups, high-risk and low-risk ones. Finally, prognostic genes' expression was examined in the GSE41258 dataset. Results: In total, 27 DENRGs were filtered, and a necroptosis-related prognostic signature based on 6 DENRGs was constructed, which may better understand the overall survival (OS) of CRC. The Kaplan-Meier curve manifested the effectiveness of the prognostic signature, and the ROC curve showed the same result. In addition, univariate and multivariate Cox regression analyses revealed that age, pathology T, and risk score were independent prognostic factors, and a nomogram was established. Furthermore, the prognostic signature was most significantly associated with the apoptosis pathway. Meanwhile, 24 immune cells represented significant differences between two groups, like the activated B cell. Furthermore, 32 immune checkpoints, TIDE scores, PD-L1 scores, and T-cell exclusion scores were significantly different between the two groups. Finally, a 6-gene prognostic signature represented different expression levels between tumor and normal samples significantly in the GSE41258 dataset. Conclusion: Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients’ overall survival and to construct a prognostic biomarker.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36046241", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Univariate Cox regression analysis and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients’ overall survival and to construct a prognostic biomarker."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE39582 https://www.ncbi.nlm.nih.gov/geo/, GSE41258.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36046241", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE39582 https://www.ncbi.nlm.nih.gov/geo/, GSE41258."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study established a six-gene prognostic signature comprising CHMP2B, TRAF2, RIPK3, CXCL1, GPX4, and CHMP6, together with a prognostic nomogram for assessing survival in colorectal cancer.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36046241", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Our study established a signature including 6 genes and a prognostic nomogram that could significantly assess the prognosis of patients with CRC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study established a six-gene prognostic signature comprising CHMP2B, TRAF2, RIPK3, CXCL1, GPX4, and CHMP6, together with a prognostic nomogram for assessing survival in colorectal cancer.", "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:36046241"], "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:36046241", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.955424", "sha256": "sha256:310317e7aabca424ea8d09107d74b3256fe4bdc0fef6cb894931262c97f7d8d2", "bytes": 35937, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.955424", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9421078", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE39582", "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=GSE39582", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE39582", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE39582", "GSE41258"], "resolved_accessions": ["GSE39582", "GSE41258"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/, GSE39582 https://www.ncbi.nlm.nih.gov/geo/, GSE41258."], "basis": "The paper names GSE39582, GSE41258 in Gene Expression Omnibus (GEO) database 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:36046241", "data:geo:GSE39582"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE39582"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:02:52Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10460, "completion_tokens": 2677, "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:36046241", "type": "paper", "label": "pmid:36046241"}, {"id": "source:pmid:36046241", "type": "manuscript", "label": "PMID 36046241 full text"}, {"id": "data:geo:GSE39582", "type": "dataset", "label": "GEO GSE39582"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36046241", "type": "paper", "label": "pmid:36046241"}, {"id": "source:pmid:36046241", "type": "manuscript", "label": "PMID 36046241 full text"}, {"id": "data:geo:GSE39582", "type": "dataset", "label": "GEO GSE39582"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36046241", "type": "extraction", "used": ["source:pmid:36046241"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE39582", "type": "accession_resolution", "used": ["data:geo:GSE39582"], "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:36046241", "parent_hash": "sha256:6a26d9d31558ea38791117176e9ccabb69e81564616dfdcd79e772312183beb7", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE39582 licence not read", "asset:data:geo:GSE39582 not downloaded", "asset:data:geo:GSE41258 licence not read", "asset:data:geo:GSE41258 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.69, "evidence": ["e was most significantly associated with the apoptosis pathway. 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The cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM, Bi"], "order_basis": "paper_text_order:2911", "id": "assay:2"}, {"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 and least absolute shrinkage and selectionator operator (LASSO) analysis were applied to selected DENRGs concerning patients’ overall survival and to construct a prognostic biomarker."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}