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pmid:33336043
An immune-relevant signature of nine genes as a prognostic biomarker in patients with gastric carcinoma.
Bing Wang · Yang Zhang
Open medicine (Warsaw, Poland) · 2020 doi:10.1515/med-2020-0142
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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The aim of this study was to construct a signature according to immune-relevant genes to predict the survival outcome of GC patients using The Cancer Genome Altas (TCGA).\nMETHODS: Univariate Cox regression analysis was used to assess the relationship between immune-relevant genes regarding the prognosis of patients with GC. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to select prognostic immune-relevant genes and to establish the signature for the prognostic evaluation of patients with GC. Multivariate Cox regression analysis and Kaplan-Meier survival analysis were used to assess the independent prognostic ability of the immune-relevant gene signature.\nRESULTS: A total of 113 prognostic immune-relevant genes were identified using univariate Cox proportional hazards regression analysis. A signature of nine immune-relevant genes was constructed using the LASSO Cox regression. The GC samples were assigned to two groups (low- and high risk) according to the optimal cutoff value of the signature score. Compared with the patients in the high-risk group, patients in the low-risk group had a significantly better prognosis in the TCGA and GSE84437 cohorts (log-rank test P < 0.001). Multivariate Cox regression analysis demonstrated that the signature of nine immune-relevant genes might serve as an independent predictor of GC.\nCONCLUSIONS: Our results showed that the signature of nine immune-relevant genes may potentially serve as a prognostic prediction for patients with GC, which may contribute to the decision-making of personalized treatment for the patients.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to select prognostic immune-relevant genes and to establish the signature for the prognostic evaluation of patients with GC.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33336043", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to select prognostic immune-relevant genes and to establish the signature for the prognostic evaluation of patients with GC."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Clinical follow-up information and transcriptomic data (407 samples, Workflow Type: HTSeq-Counts) of GC samples were downloaded from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) database and were used as the training cohort.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33336043", "locator": "sections[3] \"Patients and datasets\" paragraph 2", "quote": "Clinical follow-up information and transcriptomic data (407 samples, Workflow Type: HTSeq-Counts) of GC samples were downloaded from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) database and were used as the training cohort."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33336043"], "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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