A Novel Immune-Related Gene Signature Predicts Prognosis of Lung Adenocarcinoma.
Background. Lung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironm...
| Publicado en: | BioMed Research International pp. 1 - 17 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/9/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156229377&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156229377 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/9/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 156229377 156229377 156229377 10.1155/2022/4995874 156229377 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Immune-Related Gene Signature Predicts Prognosis of Lung Adenocarcinoma. aug: au: Ma, Chao Li, Feng Wang, Ziming Luo, Huan affil: Department of Thoracic Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China sug: subj: Adenocarcinoma of Lung Prognosis Adenocarcinoma of Lung Drug Therapy Treatment Outcomes Immune System Physiology Genes Human Cox Proportional Hazards Model Prospective Studies Kaplan-Meier Estimator Descriptive Statistics ROC Curve ab: Background. Lung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes. Methods. First, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability. Results. A nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability. Conclusions. This study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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