NF-κB-Related Metabolic Gene Signature Predicts the Prognosis and Immunotherapy Response in Gastric Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35036435", "kind": "science-record", "revision": 3, "content_hash": "sha256:ff0db2b34d5f2c76e35068eb44bd3e4447fa67c9dec18897c8a97f811f970c23", "created_at": "2026-11-01T17:36:45Z", "updated_at": "2026-11-01T21:26:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "NF-κB-Related Metabolic Gene Signature Predicts the Prognosis and Immunotherapy Response in Gastric Cancer.", "doi": "10.1155/2022/5092505", "pmid": "35036435", "authors": ["Qiuxiang Chen", "Xiaojing Du", "Sunkuan Hu", "Qingke Huang"], "journal": "BioMed research international", "year": 2022, "abstract": "BACKGROUND: Sufficient evidence indicated the crucial role of NF-κB family played in gastric cancer (GC). The novel discovery that NF-κB could regulate cancer metabolism and immune evasion greatly increased its attraction in cancer research. However, the correlation among NF-κB, metabolism, and cancer immunity in GC still requires further improvement.\nMETHODS: TCGA, hTFtarget, and MSigDB databases were employed to identify NF-κB-related metabolic genes (NFMGs). Based on NFMGs, we used consensus clustering to divide GC patients into two subtypes. GSVA was employed to analyze the enriched pathway. ESTIMATE, CIBERSORT, ssGSEA, and MCPcounter algorithms were applied to evaluate immune infiltration in GC. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict patients' response to immunotherapy. We also established a NFMG-related risk score by using the LASSO regression model and assessed its efficacy in TCGA and GSE62254 datasets.\nRESULTS: We used 27 NFMGs to conduct an unsupervised clustering on GC samples and classified them into two clusters. Cluster 1 was characterized by high active metabolism, tumor mutant burden, and microsatellite instability, while cluster 2 was featured with high immune infiltration. Compared to cluster 2, cluster 1 had a better prognosis and higher response to immunotherapy. In addition, we constructed a 12-NFMG (ADCY3, AHCY, CHDH, GUCY1A2, ITPA, MTHFD2, NRP1, POLA1, POLR1A, POLR3A, POLR3K, and SRM) risk score. Followed analysis indicated that this risk score acted as an effectively prognostic factor in GC.\nCONCLUSION: Our data suggested that GC subtypes classified by NFMGs may effectively guide prognosis and immunotherapy. Further study of these NFMGs will deepen our understanding of NF-κB-mediated cancer metabolism and immunity.", "claims": [{"id": "claim-method", "statement": "We also established a NFMG-related risk score by using the LASSO regression model and assessed its efficacy in TCGA and GSE62254 datasets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35036435", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We also established a NFMG-related risk score by using the LASSO regression model and assessed its efficacy in TCGA and GSE62254 datasets."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RNA-seq data and related clinical data were presented in TCGA (https://gdc-portal.nci.nih.gov/) and GSE62254 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254). The expression of NF-κB transcription factors was downloaded from TIMER 2.0 (http://timer.comp-genomics.org/) and HPA (https://www.proteinatlas.org/) datasets. We sourced the survival analysis of NF-κB transcription factors from the Kaplan-Meier Plotter online tool (http://kmplot.com/analysis/index.php?p=background). The response rate to ICI was predicted by using the TIDE dataset (http://tide.dfci.harvard.edu/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35036435", "locator": "sections[24] \"Data Availability\" paragraph 2", "quote": "The RNA-seq data and related clinical data were presented in TCGA (https://gdc-portal.nci.nih.gov/) and GSE62254 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254). The expression of NF-κB transcription factors was downloaded from TIMER 2.0 (http://timer.comp-genomics.org/) and HPA (https://www.proteinatlas.org/) datasets. We sourced the survival analysis of NF-κB transcription factors from the Kaplan-Meier Plotter online tool (http://kmplot.com/analysis/index.php?p=background). The response rate to ICI was predicted by using the TIDE dataset (http://tide.dfci.harvard.edu/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper constructed a 12-NFMG risk score based on ADCY3, AHCY, CHDH, GUCY1A2, ITPA, MTHFD2, NRP1, POLA1, POLR1A, POLR3A, POLR3K, and SRM that was an effectively prognostic factor in gastric cancer.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35036435", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In addition, we constructed a 12-NFMG (ADCY3, AHCY, CHDH, GUCY1A2, ITPA, MTHFD2, NRP1, POLA1, POLR1A, POLR3A, POLR3K, and SRM) risk score. Followed analysis indicated that this risk score acted as an effectively prognostic factor in GC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The paper constructed a 12-NFMG risk score based on ADCY3, AHCY, CHDH, GUCY1A2, ITPA, MTHFD2, NRP1, POLA1, POLR1A, POLR3A, POLR3K, and SRM that was an effectively prognostic factor in 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:35036435"], "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:GSE62254"], "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:GSE62254"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35036435", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/5092505", "sha256": "sha256:44cea24989963d815ea39efe4944234d1678c65e336cc7768c689149f9286189", "bytes": 52506, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/5092505", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8753254", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE62254", "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=GSE62254", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE62254", "version": null, "note": "The RNA-seq data and related clinical data were presented in TCGA (https://gdc-portal.nci.nih.gov/) and GSE62254 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254). The expression of NF-κB transcription factors was downloaded from TIMER 2.0 (http://timer.comp-genomics.org/) and HPA (https://www.proteinatlas.org/) datasets. We sourced the survival analysis of NF-κB transcription factors from the Kaplan-Meier Plotter online tool (http://kmplot.com/analysis/index.php?p=background). The response rate to ICI was predicted by using the TIDE dataset (http://tide.dfci.harvard.edu/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The RNA-seq data and related clinical data were presented in TCGA (https://gdc-portal.nci.nih.gov/) and GSE62254 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254). The expression of NF-κB transcription factors was downloaded from TIMER 2.0 (http://timer.comp-genomics.org/) and HPA (https://www.proteinatlas.org/) datasets. We sourced the survival analysis of NF-κB transcription factors from the Kaplan-Meier Plotter online tool (http://kmplot.com/analysis/index.php?p=background). 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