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pmid:35719369

Integrative Analysis Identifies a TNFα-Derived Gene Signature for Predicting Prognosis, Tumor Immunity, and Treatment Sensitivity in Gastric Cancer.

Ke Wang · Lina Qi · Hua Sun · Min Diao · Lin Yang

Frontiers in genetics · 2022 doi:10.3389/fgene.2022.882519

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

absent

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title
Integrative Analysis Identifies a TNFα-Derived Gene Signature for Predicting Prognosis, Tumor Immunity, and Treatment Sensitivity in Gastric Cancer.
doi
10.3389/fgene.2022.882519
pmid
35719369
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Ke Wang
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Lina Qi
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Hua Sun
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Min Diao
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Lin Yang
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Frontiers in genetics
year
2022
abstract
Objective: TNF-α is an essential pro-inflammatory cytokine in the tumor microenvironment of gastric cancer (GC), possessing a key biological and clinical impact. Here, we conducted an integrative analysis of the role of TNFα-derived genes in GC prognosis and precision medicine. Methods: We pooled transcriptome and clinical features of GC patients from TCGA and GSE15459 projects. TNFα signaling was quantified through the ssGSEA algorithm, and TNFα-derived genes were screened with WGCNA. Thereafter, a LASSO model was established. The somatic mutation was analyzed across GC specimens. Immune cell infiltrations were inferred through ESTIMATE and ssGSEA algorithms, followed by measuring the immune checkpoint expression. AKR1B1, CPVL, and CTSL expressions were measured in gastric mucosal cells GES-1 and GC cells (HGC-27, MKN-28, and AGS) through RT-qPCR and Western blotting. Results: A TNFα-derived gene signature (containing AKR1B1, CPVL, and CTSL) was developed for GC. A high-risk score indicated more undesirable OS, DFS, DSS, and PFS outcomes. Time-independent ROC curves and multivariate cox regression models confirmed that the signature reliably and independently predicted GC prognosis. Additionally, risk scores displayed significant correlations to more severe histological grades and pathological stages. A low-risk score was characterized by increased somatic mutation, while a high-risk score was characterized by immune and stromal activation, enhanced immune cell infiltrations, and increased expression of immune checkpoint molecules. Experimental results confirmed the significant upregulation of AKR1B1, CPVL, and CTSL in GC cells. Conclusion: Collectively, stratification based on the TNFα-derived gene signature might enable GC patients to predict prognosis, benefit from immunotherapy, and assist in formulating novel therapeutic regimens.
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s and precision medicine. Methods: We pooled transcriptome and clinical features of GC patients from TC
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d GC cells (HGC-27, MKN-28, and AGS) through RT-qPCR and Western blotting. Results: A TNFα-derive
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HGC-27, MKN-28, and AGS) through RT-qPCR and Western blotting. Results: A TNFα-derived gene signature (con
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35719369", "kind": "science-record", "revision": 4, "content_hash": "sha256:0a081cd4ca1d0015796ea4d8862e10ee094760adbd05e089846b709354a8e0a5", "created_at": "2026-11-01T18:14:42Z", "updated_at": "2026-11-01T22:06:48Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative Analysis Identifies a TNFα-Derived Gene Signature for Predicting Prognosis, Tumor Immunity, and Treatment Sensitivity in Gastric Cancer.", "doi": "10.3389/fgene.2022.882519", "pmid": "35719369", "authors": ["Ke Wang", "Lina Qi", "Hua Sun", "Min Diao", "Lin Yang"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Objective: TNF-α is an essential pro-inflammatory cytokine in the tumor microenvironment of gastric cancer (GC), possessing a key biological and clinical impact. Here, we conducted an integrative analysis of the role of TNFα-derived genes in GC prognosis and precision medicine. Methods: We pooled transcriptome and clinical features of GC patients from TCGA and GSE15459 projects. TNFα signaling was quantified through the ssGSEA algorithm, and TNFα-derived genes were screened with WGCNA. Thereafter, a LASSO model was established. The somatic mutation was analyzed across GC specimens. Immune cell infiltrations were inferred through ESTIMATE and ssGSEA algorithms, followed by measuring the immune checkpoint expression. AKR1B1, CPVL, and CTSL expressions were measured in gastric mucosal cells GES-1 and GC cells (HGC-27, MKN-28, and AGS) through RT-qPCR and Western blotting. Results: A TNFα-derived gene signature (containing AKR1B1, CPVL, and CTSL) was developed for GC. A high-risk score indicated more undesirable OS, DFS, DSS, and PFS outcomes. Time-independent ROC curves and multivariate cox regression models confirmed that the signature reliably and independently predicted GC prognosis. Additionally, risk scores displayed significant correlations to more severe histological grades and pathological stages. A low-risk score was characterized by increased somatic mutation, while a high-risk score was characterized by immune and stromal activation, enhanced immune cell infiltrations, and increased expression of immune checkpoint molecules. Experimental results confirmed the significant upregulation of AKR1B1, CPVL, and CTSL in GC cells. Conclusion: Collectively, stratification based on the TNFα-derived gene signature might enable GC patients to predict prognosis, benefit from immunotherapy, and assist in formulating novel therapeutic regimens.", "claims": [{"id": "claim-method", "statement": "TNFα signaling was quantified through the ssGSEA algorithm, and TNFα-derived genes were screened with WGCNA.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35719369", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "TNFα signaling was quantified through the ssGSEA algorithm, and TNFα-derived genes were screened with WGCNA."}], "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:35719369", "locator": "sections[28] \"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"}], "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:35719369"], "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:GSE15459"], "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:GSE15459"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35719369", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.882519", "sha256": 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE15459"], "resolved_accessions": ["GSE15459"], "statements": ["The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author."], "basis": "The paper names GSE15459 in Gene Expression Omnibus (GEO) repository and the accession resolves in the repository. 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Methods: We pooled transcriptome and clinical features of GC patients from TC", "Methods Patient Cohort and Data Acquisition RNA sequencing data (in fragments per kilobase per million"], "order_basis": "paper_text_order:402", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["d GC cells (HGC-27, MKN-28, and AGS) through RT-qPCR and Western blotting. Results: A TNFα-derive", "C cells (HGC-27, MKN-28, and AGS) through RT-qPCR and Western blotting. Results: A TNFα-derive", "uman GC cells HGC-27, MKN-28, and AGS. (H–J) RT-qPCR for verification of the expressions of AKR1B", "n GC cells HGC-27, MKN-28, and AGS. (H–J) RT-qPCR for verification of the expressions of AKR1B"], "order_basis": "paper_text_order:956", "id": "assay:2"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["HGC-27, MKN-28, and AGS) through RT-qPCR and Western blotting. Results: A TNFα-derived gene signature (con", "nd CTSL between GC and normal tissues. (D–G) Western blotting for validation of the expressions of AKR1B1,"], "order_basis": "paper_text_order:968", "id": "assay:3"}, {"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": 0.7, "evidence": ["cox regression"], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}