A novel necroptosis-related gene index for predicting prognosis and a cold tumor immune microenvironment in stomach adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36389725", "kind": "science-record", "revision": 3, "content_hash": "sha256:dd76493761e04638ac786cbec08106c17a1df682f3edb2bc35acd46e7e0bd5cb", "created_at": "2026-11-01T19:00:50Z", "updated_at": "2026-11-01T22:08:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel necroptosis-related gene index for predicting prognosis and a cold tumor immune microenvironment in stomach adenocarcinoma.", "doi": "10.3389/fimmu.2022.968165", "pmid": "36389725", "authors": ["Muhammad Khan", "Jie Lin", "Baiyao Wang", "Chengcong Chen", "Zhong Huang", "Yunhong Tian", "Yawei Yuan", "Junguo Bu"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Gastric cancer (GC) represents a major global clinical problem with very limited therapeutic options and poor prognosis. Necroptosis, a recently discovered inflammatory form of cell death, has been implicated in carcinogenesis and inducing necroptosis has also been considered as a therapeutic strategy.\nObjective: We aim to evaluate the role of this pathway in gastric cancer development, prognosis and immune aspects of its tumor microenvironment.\nMethods and results: In this study, we evaluated the gene expression of 55 necroptosis-related genes (NRGs) that were identified via carrying out a comprehensive review of the medical literature. Necroptosis pathway was deregulated in gastric cancer samples (n=375) as compared to adjacent normal tissues (n=32) obtained from the \"The Cancer Genome Atlas (TCGA)\". Based on the expression of these NRGs, two molecular subtypes were obtained through consensus clustering that also showed significant prognostic difference. Differentially expressed genes between these two clusters were retrieved and subjected to prognostic evaluation via univariate cox regression analysis and LASSO cox regression analysis. A 13-gene risk signature, termed as necroptosis-related genes prognostic index (NRGPI), was constructed that comprehensively differentiated the gastric cancer patients into high- and low-risk subgroups. The prognostic significance of NRGPI was validated in the GEO cohort (GSE84437: n=408). The NRGPI-high subgroup was characterized by upregulation of 10 genes (CYTL1, PLCL1, CGB5, CNTN1, GRP, APOD, CST6, GPX3, FCN1, SERPINE1) and downregulation of 3 genes (EFNA3, E2F2, SOX14). Further dissection of these two risk groups by differential gene expression analysis indicated involvement of signaling pathways associated with cancer cell progression and immune suppression such as WNT and TGF-β signaling pathway. Para-inflammation and type-II interferon pathways were activated in NRGPI-high patients with an increased infiltration of Tregs and M2 macrophage indicating an exhausted immune phenotype of the tumor microenvironment. These molecular characteristics were mainly driven by the eight NRGPI oncogenes (CYTL1, PLCL1, CNTN1, GRP, APOD, GPX3, FCN1, SERPINE1) as validated in the gastric cancer cell lines and clinical samples. NRGPI-high patients showed sensitivity to a number of targeted agents, in particular, the tyrosine kinase inhibitors.\nConclusions: Necroptosis appears to play a critical role in the development of gastric cancer, prognosis and shaping of its tumor immune microenvironment. NRGPI can be used as a promising prognostic biomarker to identify gastric cancer patients with a cold tumor immune microenvironment and poor prognosis who may response to selected molecular targeted therapy.", "claims": [{"id": "claim-method", "statement": "Next, univariate cox regression analysis was carried out to estimate the prognostic significance of the DEGs (n=1056) identified between the clusters. In total, 124 DEGs showed prognostic association when significance level was set at p<0.01. Using the R package “glmnet”, these DEGs were subjected to the least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis to construct the prognostic model by narrowing down the candidate genes (60).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36389725", "locator": "sections[6] \"Development and validation of necroptosis-related prognostic model\" paragraph 2", "quote": "Next, univariate cox regression analysis was carried out to estimate the prognostic significance of the DEGs (n=1056) identified between the clusters. In total, 124 DEGs showed prognostic association when significance level was set at p<0.01. Using the R package “glmnet”, these DEGs were subjected to the least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis to construct the prognostic model by narrowing down the candidate genes (60)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The mRNA expression and clinical data used in this article can be accessed from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases. The accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36389725", "locator": "sections[29] \"Data availability statement\" paragraph 2", "quote": "The mRNA expression and clinical data used in this article can be accessed from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases. The accession number(s) can be found in the article/ Supplementary Material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The 13-gene NRGPI risk signature significantly separated overall-survival outcomes in both the TCGA and GEO cohorts (p<0.001).", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "A K-M plot, as depicted in Figures 3D, E , showed significantly worst survival for high-risk patients (NRGPI-High) versus low-risk patients (NRGPI-Low) in both TCGA and GEO cohorts (p<0.001)."}, "evidence": [{"asset": "source:pmid:36389725", "locator": "sections[19] \"Development and validation of prognostic gene model\" paragraph 2", "quote": "A K-M plot, as depicted in Figures 3D, E , showed significantly worst survival for high-risk patients (NRGPI-High) versus low-risk patients (NRGPI-Low) in both TCGA and GEO cohorts (p<0.001)."}, {"asset": "source:pmid:36389725", "locator": "sections[19] \"Development and validation of prognostic gene model\" paragraph 2", "quote": "The area under the curves (AUCs) were 0.651/0.722/0.753 at 1/3/5 years in the TCGA cohort, and 0.557/0.611/0.607 at 1/3/5 years in the GEO cohort ( Figures 3F, G )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The 13-gene NRGPI risk signature significantly separated overall-survival outcomes in both the TCGA and GEO cohorts (p<0.001).", "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:36389725"], "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:GSE84437"], "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:GSE84437"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36389725", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.968165", "sha256": "sha256:9db24487576d2dcc24e444ff14ae6f077fe7c16dd1d5642205ff03d6b33f33eb", "bytes": 66361, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.968165", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9646549", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE84437", "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=GSE84437", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE84437", "version": null, "note": "The mRNA expression and clinical data used in this article can be accessed from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases. The accession number(s) can be found in the article/ Supplementary Material .", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The mRNA expression and clinical data used in this article can be accessed from TCGA (https://portal.gdc.cancer.gov/) and GEO (https://www.ncbi.nlm.nih.gov/geo/) databases. 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"group_sizes_basis": null, "platform": "6947", "taxon": "Homo sapiens", "title": "Molecular subtypes in gastric cancer.", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE84437 not downloaded", "asset:data:geo:GSE84437 licence not read"]}, "analysis": {"state": "partial", "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": "Next, univariate cox regression analysis was carried out to estimate the prognostic significance of the DEGs (n=1056) identified between the clusters. In total, 124 DEGs showed prognostic association when significance level was set at p<0.01. Using the R package “glmnet”, these DEGs were subjected to the least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis to construct the prognostic model by narrowing down the candidate genes (60)."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "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": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-4", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-5", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "Principal component analysis"}}, {"id": "method-6", "family": "regression_modelling", "specific": "ols", "label": "Regression modelling", "dsl_operations": ["ols_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.8, "band": "high", "quote": "Linear regression"}}, {"id": "method-7", "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"}}, {"id": "method-8", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "Chi-square"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "chi_square_test", "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. 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(A) Expression level (IHC quantification) of RIPK1, RIPK3, MLKL (necro", "ocarcinoma. The scattered dots represent the IHC score of each individual sample. The thick m"], "order_basis": "paper_text_order:6991", "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": ["Next, univariate cox regression analysis was carried out to estimate the prognostic significance of the DEGs (n=1056) identified between the clusters. In total, 124 DEGs showed prognostic association when significance level was set at p<0.01. 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