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pmid:37066015
Construction and validation of a prognosis signature based on the immune microenvironment in gastric cancer.
Li-Hong Wu · Xiang-Xu Wang · Yan Wang · Jing Wei · Zi-Rong Liang · Xi Yan · Jun Wang
Frontiers in surgery · 2023 doi:10.3389/fsurg.2023.1088292
Paper
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Experiment
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Data
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Analysis
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Execution
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Complete file
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Therefore, a classification system of gastric cancer based explicitly on the immune microenvironment context might enrich the strategy for gastric cancer prognosis and therapy.\nMethods: A total of 668 GC patients were collected from TCGA-STAD (n = 350), GSE15459 (n = 192), GSE57303 (n = 70) and GSE34942 (n = 56) datasets. Three immune-related subtypes (immunity-H, -M, and -L) were identified by hierarchical cluster analysis based on the ssGSEA score of 29 immune microenvironment-related gene sets. The immune microenvironment-related prognosis signature (IMPS) was constructed via univariate Cox regression, Lasso-Cox regression and multivariate Cox regression, and nomogram model combining IMPS and clinical variables was further constructed by the \"rms\" package. RT-PCR was applied to validate the expression of 7 IMPS genes between two human GC cell lines (AGS and MKN45) and one normal gastric epithelial cell line (GES-1).\nResults: The patients classified as immunity-H subtype exhibited highly expressed immune checkpoint and HLA-related genes, with enriched naïve B cells, M1 macrophages and CD8 T cells. We further constructed and validated a 7-gene (CTLA4, CLDN6, EMB, GPR15, ENTPD2, VWF and AKR1B1) prognosis signature, termed as IMPS. The patients with higher IMPS expression were more likely to be associated with higher pathology grade, more advanced TNM stages, higher T and N stage, and higher ratio of death. In addition, the prediction values of the combined nomogram in predicting 1-year (AUC = 0.750), 3-year (AUC = 0.764) and 5-year (AUC = 0.802) OS was higher than IMPS and individual clinical characteristics.\nConclusions: The IMPS is a novel prognosis signature associated with the immune microenvironment and clinical characteristics. 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Finally, we constructed a 7-gene prognosis signature via multivariate Cox analysis with the “stepwise regression method” (17)."}], "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 author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37066015", "locator": "sections[22] \"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 author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The IMPS and the combined nomogram model provide a relatively reliable predictive index for predicting the survival outcomes of gastric cancer.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37066015", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The IMPS and the combined nomogram model provide a relatively reliable predictive index for predicting the survival outcomes of gastric cancer."}, {"asset": "source:pmid:37066015", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In addition, the prediction values of the combined nomogram in predicting 1-year (AUC = 0.750), 3-year (AUC = 0.764) and 5-year (AUC = 0.802) OS was higher than IMPS and individual clinical characteristics."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The IMPS and the combined nomogram model provide a relatively reliable predictive index for predicting the survival outcomes of gastric 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:37066015"], "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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Finally, we constructed a 7-gene prognosis signature via multivariate Cox analysis with the “stepwise regression method” (17)."}}, {"id": "method-2", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}], "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. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:37066015", "data:geo:GSE15459"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE15459"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:36:48Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12527, "completion_tokens": 2307, "cached_tokens": 256}}, "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:37066015", "type": "paper", "label": "pmid:37066015"}, {"id": "source:pmid:37066015", "type": "manuscript", "label": "PMID 37066015 full text"}, {"id": "data:geo:GSE15459", "type": "dataset", "label": "GEO GSE15459"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37066015", "type": "paper", "label": "pmid:37066015"}, {"id": "source:pmid:37066015", "type": "manuscript", "label": "PMID 37066015 full text"}, {"id": "data:geo:GSE15459", "type": "dataset", "label": "GEO GSE15459"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37066015", "type": "extraction", "used": ["source:pmid:37066015"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE15459", "type": "accession_resolution", "used": ["data:geo:GSE15459"], "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:37066015", "parent_hash": "sha256:c99d32f60b3ddbd8b30471f8a69922813c4a1af0f1bada9aae4643701e834bea", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:GSE15459 licence not read", "asset:data:geo:GSE15459 not downloaded", "asset:data:geo:GSE34942 licence not read", "asset:data:geo:GSE34942 not downloaded", "asset:data:geo:GSE57303 licence not read", "asset:data:geo:GSE57303 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["In the TCGA-training cohort, we first screened the prognostic-related genes by univariate Cox regression (P < 0.05), and further eliminate the collinearity among genes by Lasso-Cox regression analysis (16). Finally, we constructed a 7-gene prognosis signature via multivariate Cox analysis with the “stepwise regression method” (17)."], "order_basis": "analysis_only", "id": "assay:1"}]}