Non-apoptotic regulated cell death based prognostic risk model for colorectal cancer using machine learning guided two-step framework.

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To address this, we leveraged the emerging role of non-apoptotic regulated cell death (NARCD) pathways to develop a two-step machine learning (ML) framework to develop a prognostic risk model. We used two largest and independent CRC patient cohorts (TCGA and E-MTAB-12862). Our novel pipeline consists of two steps, first, a library of 46 combination ML survival models was applied to each of the 13 NARCD pathways to establish robust, pathway-specific prognostic models. Second, logistic regression was used to integrate these models, revealing that the synergistic combination of ferroptosis, NETosis, pyroptosis, and autosis yielded the highest predictive power. The resulting 43-gene prognostic risk model, the combined-regulated cell death index (c-RCDI), robustly stratified patients and proved to be a powerful independent prognostic factor (HR = 55.1, P < 0.001), with high predictive accuracy (5-year AUROC = 0.88). Notably, this prognostic power was exclusive to CRC. Biologically, the high-risk class showed enriched angiogenesis and EMT pathways, an immunosuppressive microenvironment, reduced immunotherapy response, and predicted increased sensitivity to CDK, MEK, and metabolic inhibitors, while the low-risk class showed increased sensitivity to the drug \"Obatoclax Mesylate_1068\". c-RCDI developed in this study using a novel two-step ML framework based on NARCD pathways showed robust predictive ability for CRC patients, with a potential for improving diagnosis and therapy.", "claims": [{"id": "claim-method", "statement": "To develop our prognostic risk model, we implemented a novel two-step ML framework.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41348597", "locator": "sections[5] \"Development of a combined prognostic risk model\" paragraph 2", "quote": "To develop our prognostic risk model, we implemented a novel two-step ML framework."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The clinical and expression data in the dataset E-MTAB-12862 is obtained from ArrayExpress (website: https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-12862), and the TCGA data is obtained from UCSC Xena Browser GDC database (website: https://xenabrowser.net/datapages). The data of validation datasets GSE39582 and GSE161158 are retrieved from GEO database website (website: https://www.ncbi.nlm.nih.gov/geo). The gene expression and survival data of TCGA-BRCA, TCGA-GBM, TCGA-PAAD, and TCGA-SKCM were downloaded from UCSC Xena Browser GDC database. The codes for the study have been uploaded to github under “CGnTLab” user with the repository “c_RCDI_prognostic_risk_model” (website: https://github.com/CGnTLab/c_RCDI_prognostic_risk_model). The interactive R Shiny web tool for c-RCDI calculation is publicly available at: https://asgda.shinyapps.io/r_shiny/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41348597", "locator": "sections[33] \"Data availability\" paragraph 2", "quote": "The clinical and expression data in the dataset E-MTAB-12862 is obtained from ArrayExpress (website: https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-12862), and the TCGA data is obtained from UCSC Xena Browser GDC database (website: https://xenabrowser.net/datapages). The data of validation datasets GSE39582 and GSE161158 are retrieved from GEO database website (website: https://www.ncbi.nlm.nih.gov/geo). The gene expression and survival data of TCGA-BRCA, TCGA-GBM, TCGA-PAAD, and TCGA-SKCM were downloaded from UCSC Xena Browser GDC database. The codes for the study have been uploaded to github under “CGnTLab” user with the repository “c_RCDI_prognostic_risk_model” (website: https://github.com/CGnTLab/c_RCDI_prognostic_risk_model). The interactive R Shiny web tool for c-RCDI calculation is publicly available at: https://asgda.shinyapps.io/r_shiny/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The resulting 43-gene prognostic risk model, the combined-regulated cell death index (c-RCDI), robustly stratified patients and proved to be a powerful independent prognostic factor (HR = 55.1, P < 0.001), with high predictive accuracy (5-year AUROC = 0.88).", "kind": "quantitative", "target": {"value": 0.88, "unit": "auc", "tolerance": null, "source": "The resulting 43-gene prognostic risk model, the combined-regulated cell death index (c-RCDI), robustly stratified patients and proved to be a powerful independent prognostic factor (HR = 55.1, P < 0.001), with high predictive accuracy (5-year AUROC = 0.88)."}, "evidence": [{"asset": "source:pmid:41348597", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The resulting 43-gene prognostic risk model, the combined-regulated cell death index (c-RCDI), robustly stratified patients and proved to be a powerful independent prognostic factor (HR = 55.1, P < 0.001), with high predictive accuracy (5-year AUROC = 0.88)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The resulting 43-gene prognostic risk model, the combined-regulated cell death index (c-RCDI), robustly stratified patients and proved to be a powerful independent prognostic factor (HR = 55.1, P < 0.001), with high predictive accuracy (5-year AUROC = 0.88).", "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:41348597"], "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. 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six molecular subtypes with distinct clinical, molecular and survival characteristics [Expression]", "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:E-MTAB-12862 not downloaded", "asset:data:geo:E-MTAB-12862 licence not read", "asset:data:geo:GSE161158 not downloaded", "asset:data:geo:GSE161158 licence not read", "asset:data:geo:GSE39582 not downloaded", "asset:data:geo:GSE39582 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.7, "band": "medium", "quote": "logistic regression"}}, {"id": "method-2", "family": "network_analysis", "specific": 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our prognostic risk model, we implemented a novel two-step ML framework."}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "classification_metrics", "expected_value": 0.88, "unit": "auc", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}, {"id": "target-published-estimate-hazard-ratio", "claim_id": "claim-target-result", "operation": "classification_metrics", "expected_value": 55.1, "unit": "hazard ratio", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}, {"id": "target-published-estimate-p-value", "claim_id": "claim-target-result", "operation": "classification_metrics", "expected_value": 0.001, "unit": "p-value", "tolerance": null, "state": "complete", "note": "The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold."}], "gaps": ["method.reader_named_unmapped"]}, "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": ["glm_logit_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: logistic."}, "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": ["E-MTAB-12862", "GSE39582", "GSE161158"], "resolved_accessions": ["E-MTAB-12862", "GSE39582", "GSE161158"], "statements": ["The clinical and expression data in the dataset E-MTAB-12862 is obtained from ArrayExpress (website: https://www.ebi.ac.uk/biostudies/arrayexpress/studies/E-MTAB-12862), and the TCGA data is obtained from UCSC Xena Browser GDC database (website: https://xenabrowser.net/datapages). The data of validation datasets GSE39582 and GSE161158 are retrieved from GEO database website (website: https://www.ncbi.nlm.nih.gov/geo). The gene expression and survival data of TCGA-BRCA, TCGA-GBM, TCGA-PAAD, and TCGA-SKCM were downloaded from UCSC Xena Browser GDC database. The codes for the study have been uploaded to github under “CGnTLab” user with the repository “c_RCDI_prognostic_risk_model” (website: https://github.com/CGnTLab/c_RCDI_prognostic_risk_model). The interactive R Shiny web tool for c-RCDI calculation is publicly available at: https://asgda.shinyapps.io/r_shiny/."], "basis": "The paper names E-MTAB-12862, GSE39582, GSE161158 in ArrayExpress and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["glm_logit_fit"], "assets": ["source:pmid:41348597", "data:geo:E-MTAB-12862"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:E-MTAB-12862"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:07:11Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 16420, "completion_tokens": 2151, "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:41348597", "type": "paper", "label": "pmid:41348597"}, {"id": "source:pmid:41348597", "type": "manuscript", "label": "PMID 41348597 full text"}, {"id": "data:geo:E-MTAB-12862", "type": "dataset", "label": "GEO E-MTAB-12862"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:41348597", "type": "paper", "label": "pmid:41348597"}, {"id": "source:pmid:41348597", "type": "manuscript", "label": "PMID 41348597 full text"}, {"id": "data:geo:E-MTAB-12862", "type": "dataset", "label": "GEO E-MTAB-12862"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-41348597", "type": "extraction", "used": ["source:pmid:41348597"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-E-MTAB-12862", "type": "accession_resolution", "used": ["data:geo:E-MTAB-12862"], "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:41348597", "parent_hash": "sha256:c44edc889c8ea0210fdf2e94a6072ce35f0c449ffe202649b0e514590ad3a31b", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:E-MTAB-12862 licence not read", "asset:data:geo:E-MTAB-12862 not downloaded", "asset:data:geo:GSE161158 licence not read", "asset:data:geo:GSE161158 not downloaded", "asset:data:geo:GSE39582 licence not read", "asset:data:geo:GSE39582 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["logistic regression"], "order_basis": "analysis_only", "id": "assay:1"}]}