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IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis.

ABSTRACT: BACKGROUND:Tumour cells interfere with normal immune functions by affecting the expression of some immune-related genes, which play roles in the prognosis of cancer patients. In recent years, immunotherapy for tumours has been widely studied, but a practical prognostic model based on immune-related genes in lung adenocarcinoma comparable to existing model has not been established and reported. METHODS:We first obtained publicly accessible lung adenocarcinoma RNA expression data from The Cancer Genome Atlas (TCGA) for differential gene expression analysis and then filtered immune-related genes based on the ImmPort database. By using the lasso algorithm and multivariate Cox Proportional-Hazards (CoxPH) regression analysis, we identified candidate genes for model development and validation. The robustness of the model was further examined by comparing the model with three established gene models. RESULTS:Gene expression data from a total of 524 lung adenocarcinoma patients from TCGA were used for model development. We identified four biomarkers (MAP3K8, CCL20, VEGFC, and ANGPTL4) that could predict overall survival in lung adenocarcinoma (HR?=?1.98, 95% CI 1.48 to 2.64, P?=?4.19e-06) and this model could be used as a classifier for the evaluation of low-risk and high-risk groups. This model was validated with independent microarray data and was highly comparable with previously reported gene expression signatures for lung adenocarcinoma prognosis. CONCLUSIONS:In this study, we identified a practical and robust four-gene prognostic model based on an immune gene dataset with cross-platform compatibility. This model has potential value in improving TNM staging for survival predictions in patients with lung adenocarcinoma. IMPACT:The study provides a method of immune relevant gene prognosis model and the identification of immune gene classifier for the prediction of lung adenocarcinoma prognosis with RNA sequencing and microarray compatibility.

SUBMITTER: Shi X

PROVIDER: S-EPMC7001261 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis.

Shi Xiaoshun X Li Ruidong R Dong Xiaoying X Chen Allen Menglin AM Liu Xiguang X Lu Di D Feng Siyang S Wang He H Cai Kaican K

Journal of translational medicine 20200204 1

<h4>Background</h4>Tumour cells interfere with normal immune functions by affecting the expression of some immune-related genes, which play roles in the prognosis of cancer patients. In recent years, immunotherapy for tumours has been widely studied, but a practical prognostic model based on immune-related genes in lung adenocarcinoma comparable to existing model has not been established and reported.<h4>Methods</h4>We first obtained publicly accessible lung adenocarcinoma RNA expression data fr ...[more]

PMID: 32019546

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Project description:Background: Lung adenocarcinoma (LUAD) is an exceedingly diverse disease, making prognostication difficult. Inflammatory responses in the tumor or the tumor microenvironment can alter prognosis in the process of the ongoing cross-talk between the host and the tumor. Nonetheless, Inflammatory response-related genes' prognostic significance in LUAD, on the other hand, has yet to be determined. Materials and Methods: The clinical data as well as the mRNA expression patterns of LUAD patients were obtained from a public dataset for this investigation. In the TCGA group, a multigene prognostic signature was built utilizing LASSO Cox analysis. Validation was executed on LUAD patients from the GEO cohort. The overall survival (OS) of low- and high-risk cohorts was compared utilizing the Kaplan-Meier analysis. The assessment of independent predictors of OS was carried out utilizing multivariate and univariate Cox analyses. The immune-associated pathway activity and immune cell infiltration score were computed utilizing single-sample gene set enrichment analysis. GO keywords and KEGG pathways were explored utilizing gene set enrichment analysis. Results: LASSO Cox regression analysis was employed to create an inflammatory response-related gene signature model. The high-risk cohort patients exhibited a considerably shorter OS as opposed to those in the low-risk cohort. The prognostic gene signature's predictive ability was demonstrated using receiver operating characteristic curve analysis. The risk score was found to be an independent predictor of OS using multivariate Cox analysis. The functional analysis illustrated that the immune status and cancer-related pathways for the two-risk cohorts were clearly different. The tumor stage and kind of immune infiltrate were found to be substantially linked with the risk score. Furthermore, the cancer cells' susceptibility to anti-tumor medication was substantially associated with the prognostic genes expression levels. Conclusion: In LUAD, a new signature made up of 8 inflammatory response-related genes may be utilized to forecast prognosis and influence immunological state. Inhibition of these genes could also be used as a treatment option.

Project description:ObjectiveLung cancer is the most common malignancy worldwide and exhibits both high morbidity and mortality. In recent years, scientists have made substantial breakthroughs in the early diagnosis and treatment of lung adenocarcinoma (LUAD), however, patient prognosis still shows vast individual differences. In this study, bioinformatics methods were used to identify and analyze ferroptosis-related genes to establish an effective signature for predicting prognosis in LUAD patients.MethodsThe gene expression profiles of LUAD patients with complete clinical and follow-up information were downloaded from two public databases, univariate Cox regression and multivariate Cox regression analysis were used to obtain ferroptosis-related genes for constructing the prognos tic risk model, AUC and calibration plot were used to evaluate the predictive accuracy of the FRGS and nomogram.ResultsA total of 74 ferroptosis-related differentially expressed genes (DEGs) were identi fied between LUAD and normal tissues from The Cancer Genome Atlas (TCGA) database. A five-gene panel for prediction of LUAD prognosis was established by multivariate regression and was verified using the GSE68465 cohort from the Gene Expression Omnibus (GEO) database. Patients were divided into two different risk groups according to the median risk score of the five genes. Based on Kaplan-Meier (KM) analysi, the OS rate of the high-risk group was markedly worse than that of the low-risk group. We also found that risk score was an independent prognostic indicator. The receiver operating characteristic ROC curve showed that the proposed model had good prediction ability. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional analyses indicated that risk score was prominently enriched in ferroptosis processes. Moreover, at the score of immune-associated gene sets, significant differences were found between the two risk groups.ConclusionsThis study demonstrated that ferroptosis-related gene signatures can be used as a potential predictor for the prognosis of LUAD, thus providing a novel strategy for individualized treatment in LUAD patients.

Project description:AbstractTo screen the prognosis-related autophagy genes of female lung adenocarcinoma by the transcriptome data and clinical data from The Cancer Genome Atlas (TCGA) database.In this study, screen meaningful female lung adenocarcinoma differential genes in TCGA, use univariate Cox proportional regression model to select genes related to prognosis, and establish the best risk model. In this study, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were applied for carrying out bioinformatics analysis of gene function.The gene expression and clinical data of 264 female lung adenocarcinoma patient samples were downloaded from TCGA. Twelve down-regulated genes: NRG3, DLC1, NLRC4, DAPK2, HSPB8, PPP1R15A, FOS, NRG1, PRKCQ, GRID1, MAP1LC3C, GABARAPL1. Up-regulated 15 genes: PARP1, BNIP3, P4HB, ATIC, IKBKE, ITGB4, VMP1, PTK6, EIF4EBP1, GAPDH, ATG9B, ERO1A, TMEM74, CDKN2A, BIRC5. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed that these genes were significantly associated with autophagy and mitochondria (animals). Multifactor Cox analysis of autophagy-related genes showed that ITGA6, ERO1A, FKBP1A, BAK1, CCR2, FADD, EDEM1, ATG10, ATG4A, DLC1, VAMP7, ST13 were identified as independent prognostic indicators. According to the multivariate Cox proportional hazard regression model, there was a significant difference in the survival rate observed between the high-risk group (n = 124) and the low-risk group (n = 126) during the 10-year follow-up (P < .05). Univariate Cox analysis showed that tumor stage, T, M, and N stages, and risk score were all related to the survival rate of female lung adenocarcinoma patients. Multivariate Cox analysis found that autophagy-related risk scores were independent predictors, with an area under curve (AUC) value of 0.842. At last, there is autophagy genes differentially expressed among various clinicopathological parameters: ATG4A, BAK1, CCR2, DLC1, ERO1A, FKBP1A, ITGA6.The risk score can be used as an independent prognostic indicator for female patients with lung adenocarcinoma. The autophagy genes ITGA6, ERO1A, FKBP1A, BAK1, CCR2, FADD, EDEM1, ATG10, ATG4A, DLC1, VAMP7, ST13 were identified as prognostic genes in female lung adenocarcinoma, which may be the targets of treatment in the future.

Dataset Information

IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis.

Publications

IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis.

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