Dataset Information

A Seven-Gene Signature with Close Immune Correlation Was Identified for Survival Prediction of Lung Adenocarcinoma.

ABSTRACT: BACKGROUND Lung adenocarcinoma is the most life-threatening malignancy with high incidence and poor long-term survival. The crucial role of tumor immunity reveals the significance of exploring immune-related prognostic predictors in lung adenocarcinoma. MATERIAL AND METHODS Immune-related genes were screened out applying the ESTIMATE algorithm. The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) dataset was trained for the construction of Cox proportional hazard model. Univariate Cox and Lasso regression analysis was conducted to reduce the overfitting of model. Nomogram integrated the signature and clinicopathological characteristics was established for prognosis prediction of LUAD. The GSE30219, GSE41271, and GSE42127 datasets were analyzed for external validation. LUAD patients were separated into low-risk and high-risk subgroups based on the optimum cutoff threshold of calculated risk score. The predictive value of the signature was evaluated using Kaplan-Meier survival analysis, Harrell's C-index, and receiver operating characteristic (ROC) curve analysis and calibration curve. Clinical- and immune-correlation of the signature was further performed. Gene Set Enrichment Analysis (GSEA) was performed for functional exploration. RESULTS An immune-related signature containing 7 genes was identified. The signature exhibited reliable performance in the prediction of overall survival for LUAD with the C-index being 0.72. The areas under the curve (AUCs) of the model in 1-year risk prediction were 0.781, 0.797, 0.659, and 0.822 for TCGA-LUAD, GSE30219, GSE41271, and GSE42127 datasets, respectively. In all datasets, the signature proved to an independent risk factor for LUAD. Correlation analyses and GSEA further revealed the close relationship between the predictive biomarker and tumor immunity. CONCLUSIONS A Cox proportional hazard model consisting of 7 genes was identified for prognostic prediction of LUAD. The signature was highly correlated with immunity and deserves further exploration.

SUBMITTER: Zou X

PROVIDER: S-EPMC7350533 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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A Seven-Gene Signature with Close Immune Correlation Was Identified for Survival Prediction of Lung Adenocarcinoma.

Zou Xuan X Hu Zhihuang Z Huang Changjing C Chang Jianhua J

Medical science monitor : international medical journal of experimental and clinical research 20200702

BACKGROUND Lung adenocarcinoma is the most life-threatening malignancy with high incidence and poor long-term survival. The crucial role of tumor immunity reveals the significance of exploring immune-related prognostic predictors in lung adenocarcinoma. MATERIAL AND METHODS Immune-related genes were screened out applying the ESTIMATE algorithm. The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) dataset was trained for the construction of Cox proportional hazard model. Univariate Cox and Las ...[more]

PMID: 32613949

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Project description:BackgroundLung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.MethodsRaw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.ResultsA prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041-39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779-3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.ConclusionOur study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Project description:We investigated the local immune status and its prognostic value in lung adenocarcinoma. In total, 513 lung adenocarcinoma samples from TCGA and ImmPort databases were collected and analyzed. The R package coxph was employed to mine immune-related genes that were significant prognostic indicators using both univariate and multivariate analyses. The R software package glmnet was then used for Lasso Cox regression analysis, and a prognosis prediction model was constructed for lung adenocarcinoma; clusterProfiler was selected for functional gene annotations and KEGG enrichment analysis. Finally, correlations between the RiskScore and clinical features or signaling pathways were established. Sixty-four immune-related genes remarkably correlated with patient prognosis and were further applied. Samples were hierarchically clustered into two subgroups. Accordingly, the LASSO regression algorithm was employed to screen the 14 most representative immune-related genes (PSMD11, PPIA, MIF, BMP5, DKK1, PDGFB, ANGPTL4, IL1R2, THRB, LTBR, TNFRSF1, TNFRSF17, IL20RB, and MC1R) with respect to patient prognosis. Then, the prognosis prediction model for lung adenocarcinoma patients (namely, the RiskScore equation) was constructed, and the training set samples were incorporated to evaluate the efficiency of this model to predict and classify patient prognosis. Subsequently, based on functional annotations and KEGG pathway analysis, the 14 immune-related genes were mainly enriched in pathways closely associated with lung adenocarcinoma and its immune microenvironment, such as cytokine-cytokine receptor interaction and human T-cell leukemia virus 1 infection. Furthermore, correlations between the RiskScore and clinical features of the training set samples and signaling pathways (such as p53, cell cycle, and DNA repair) were also demonstrated. Finally, the test set sample data were employed for independent testing and verifying the model. We established a prognostic prediction RiskScore model based on the expression profiles of 14 immune-related genes, which shows high prediction accuracy and stability in identifying immune features. This could provide clinical guidance for the diagnosis and prognosis of different immunophenotypes, and suggest multiple targets for precise advanced lung adenocarcinoma therapy based on subtype-specific immune molecules.

Dataset Information

A Seven-Gene Signature with Close Immune Correlation Was Identified for Survival Prediction of Lung Adenocarcinoma.

Publications

A Seven-Gene Signature with Close Immune Correlation Was Identified for Survival Prediction of Lung Adenocarcinoma.

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