Prognostic Index for Nonsmall Cell Lung Cancer Based on Immune-Related Genes Expression.

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Here, we established an RNA-based individualized immune signature associated with prognosis for nonsmall cell lung cancer (NSCLC) to guide adjuvant therapy. We downloaded publicly accessible data on RNA expression and clinical characteristics of NSCLC from the Cancer Genome Atlas (TCGA). From immune-related genes (IRGs) retrieved from the immunology database and analysis portal (ImmPort) database, we then screened differentially expressed immune-related genes (DEIRGs). Using overall survival (OS) as a clinical endpoint, we identified 26 prognostic DEIRGs via univariate and multivariate Cox regression analysis, and then developed a risk model based on these 26 IRGs with an area under the curve (AUC) of 0.701, and its predictive ability independent from other clinical factors. We also downloaded tumor immune infiltrate data and analyzed the correlations between lymphocytic infiltration with our risk scores, but found no significant association. Furthermore, we retrieved 86 differentially expressed transcription factors (TFs) to assess their regulatory relationships with the 26 prognostic DEIRGs. In summary, we developed a robust risk model to predict survival in patients with NSCLC, based on the expression of 26 IRGs. It provides novel predictive and therapeutic molecular targets.", "claims": [{"id": "claim-method", "statement": "Using overall survival (OS) as a clinical endpoint, we identified 26 prognostic DEIRGs via univariate and multivariate Cox regression analysis, and then developed a risk model based on these 26 IRGs with an area under the curve (AUC) of 0.701, and its predictive ability independent from other clinical factors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36193311", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Using overall survival (OS) as a clinical endpoint, we identified 26 prognostic DEIRGs via univariate and multivariate Cox regression analysis, and then developed a risk model based on these 26 IRGs with an area under the curve (AUC) of 0.701, and its predictive ability independent from other clinical factors."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We extracted RNA sequencing data and clinical information of NSCLC patients from the TCGA data portal (https://cancergenome.nih.gov/). In the ImmPort database (https://immport.niaid.nih.gov/), the IRGs list was retrieved, and TFs were retrieved from the Cistrome database (http://cistrome.org/). From TIMER online database (https://cistrome.shinyapps.io/timer/), we retrieved the information about the immune infiltrate of NCSLC patients. All data downloaded at 13 January 2020.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36193311", "locator": "sections[18] \"Data Availability\" paragraph 2", "quote": "We extracted RNA sequencing data and clinical information of NSCLC patients from the TCGA data portal (https://cancergenome.nih.gov/). In the ImmPort database (https://immport.niaid.nih.gov/), the IRGs list was retrieved, and TFs were retrieved from the Cistrome database (http://cistrome.org/). From TIMER online database (https://cistrome.shinyapps.io/timer/), we retrieved the information about the immune infiltrate of NCSLC patients. 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