Comprehensive analysis of multiple regulated cell death risk signatures in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39398028", "kind": "science-record", "revision": 3, "content_hash": "sha256:6ae1b062605fdf29d1c89a1f1d198ebf069a6b064e87e02e8bb9dbbaac5bb363", "created_at": "2026-11-01T20:29:37Z", "updated_at": "2026-11-01T22:12:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of multiple regulated cell death risk signatures in lung adenocarcinoma.", "doi": "10.1016/j.heliyon.2024.e38641", "pmid": "39398028", "authors": ["Shan Gao", "Jiaqi Huang", "Rui Zhao", "Haiqi He", "Jia Zhang", "Xiaopeng Wen"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Regulated cell death (RCD) has considerable impact on tumor progress and sensitivity of treatment. Lung adenocarcinoma (LUAD) show a high resistance for conventional radiotherapies and chemotherapies. Currently, regulation of cancer cell death has been emerging as a new promising therapeutic avenue for LUAD patients. However, the crosstalk in each pattern RCD is unclear.\nMethods: We integrated collected the hub-genes of 12 RCD subroutines and compressively analyzed these hub-genes synergistic effect in LUAD. The characters of RCD genes expression and prognosis were developed in The Cancer Genome Atlas (TCGA)-LUAD data. We developed and validated an RCD risk model based on TCGA and GSE70294 data set, respectively. Functional annotation and tumor immunotherapy based on the risk model were also investigated.\nResults: 28 RCD-related genes and two LUAD molecular cluster were identified. Survival analysis revealed that the prognosis in high-risk group was worser than those in low-risk group. Functional enrichment analysis indicated that the RCD risk model correlated with immune responses. Further analysis indicated that the high-risk group in RCD risk model exhibited an immunosuppressive microenvironment and a lowly immunotherapy responder ratio.\nConclusions: We present an RCD risk model which have a promising ability in predicting LUAD prognosis and immunotherapy response.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression and Kaplan-Meier (KM) analysis was used to determine the DEGs associated with patients’ overall survival (OS) time.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39398028", "locator": "sections[4] \"RCD genes expression and prognosis analysis in LUAD\" paragraph 2", "quote": "Univariate Cox regression and Kaplan-Meier (KM) analysis was used to determine the DEGs associated with patients’ overall survival (OS) time."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data come from the publicly available datasets in present study. These datasets can be found here: TCGA database (http://cancergenome.nih.gov/), the GEO database (GSE70294) were included in this study, MSigDB datasets(http://www.gsea-msigdb.org/gsea/msigdb/index.jsp), TCIA website (https://tcia.at/home) and TIDE website (http://tide.dfci.harvard.edu/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39398028", "locator": "sections[28] \"Data availability statement\" paragraph 2", "quote": "All data come from the publicly available datasets in present study. These datasets can be found here: TCGA database (http://cancergenome.nih.gov/), the GEO database (GSE70294) were included in this study, MSigDB datasets(http://www.gsea-msigdb.org/gsea/msigdb/index.jsp), TCIA website (https://tcia.at/home) and TIDE website (http://tide.dfci.harvard.edu/)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:39398028"], "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:GSE70294"], "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:GSE70294"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39398028", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e38641", "sha256": "sha256:676c65e16457b1f56f2cea6ca398b83fe32e6b8e4ba73565ed33e929cf7a5605", "bytes": 40387, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e38641", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11471212", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE70294", "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=GSE70294", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE70294", "version": null, "note": "All data come from the publicly available datasets in present study. These datasets can be found here: TCGA database (http://cancergenome.nih.gov/), the GEO database (GSE70294) were included in this study, MSigDB datasets(http://www.gsea-msigdb.org/gsea/msigdb/index.jsp), TCIA website (https://tcia.at/home) and TIDE website (http://tide.dfci.harvard.edu/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data come from the publicly available datasets in present study. These datasets can be found here: TCGA database (http://cancergenome.nih.gov/), the GEO database (GSE70294) were included in this study, MSigDB datasets(http://www.gsea-msigdb.org/gsea/msigdb/index.jsp), TCIA website (https://tcia.at/home) and TIDE website (http://tide.dfci.harvard.edu/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1720826", "GSM1720827", "GSM1720828", "GSM1720829", "GSM1720830", "GSM1720831", "GSM1720832", "GSM1723019", "GSM1723020", "GSM1723021", "GSM1723022", "GSM1723023", "GSM1723024", "GSM1723025", "GSM1723026", "GSM1723027", "GSM2078181", "GSM2078182", "GSM2078183", "GSM2078184", "GSM2078185", "GSM2078186", "GSM2078187", "GSM2078188", "GSM2078189", "GSM2078190", "GSM2078191", "GSM2078192", "GSM2078193", "GSM2078194", "GSM2078195", "GSM2078196", "GSM2078197", "GSM2078198", "GSM2078199", "GSM2078200", "GSM2078201", "GSM2078202", "GSM2078203", "GSM2078204"], "sample_count": 40, "group_sizes": null, "group_sizes_basis": null, "platform": "17021;6885", "taxon": "Mus musculus", "title": "Plasma cell differentiation is coupled to division-dependent DNA hypomethylation and gene regulation.", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE70294 not downloaded", "asset:data:geo:GSE70294 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "Univariate Cox regression and Kaplan-Meier (KM) analysis was used to determine the DEGs associated with patients’ overall survival (OS) time."}}, {"id": "method-2", "family": "group_comparison", "specific": "student_t_test", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["student_t"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Student's t-test"}}, {"id": "method-3", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "ANOVA"}}, {"id": "method-4", "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": 0.8, "band": "high", "quote": "Cox regression"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["survival_km"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: kaplan_meier."}, "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": ["GSE70294"], "resolved_accessions": ["GSE70294"], "statements": ["All data come from the publicly available datasets in present study. These datasets can be found here: TCGA database (http://cancergenome.nih.gov/), the GEO database (GSE70294) were included in this study, MSigDB datasets(http://www.gsea-msigdb.org/gsea/msigdb/index.jsp), TCIA website (https://tcia.at/home) and TIDE website (http://tide.dfci.harvard.edu/)."], "basis": "The paper names GSE70294 in GEO database and the accession resolves in the repository. 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