Dataset Information

Development and validation of a survival model for lung adenocarcinoma based on autophagy-associated genes.

ABSTRACT:

Background

Given that abnormal autophagy is involved in the pathogenesis of cancers, we sought to explore the potential value of autophagy-associated genes in lung adenocarcinoma (LUAD).

Methods

RNA sequencing and clinical data on tumour and normal samples were acquired from The Cancer Genome Atlas (TCGA) database and randomly assigned to training and testing groups. Differentially expressed autophagy-associated genes (AAGs) were screened. Within the training group, Cox regression and Lasso regression analyses were conducted to screen five prognostic AAGs, which were used to develop a model. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were plotted to determine the performance of the model in both groups. Immunohistochemistry was used to demonstrate the differential expression of AAGs in tumour and normal tissues at the protein level. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were utilized to further elucidate the roles of AAGs in LUAD.

Results

The data from the TCGA database included 497 tumour and 54 normal samples, within which 30 differentially expressed AAGs were screened. Using Cox regression and Lasso regression analyses for the training group, 5 prognostic AAGs were identified and the prognostic model was constructed. Patients with low risk had better overall survival (OS) in the training group (3-year OS, 73.0% vs 48.0%; 5-year OS, 45.0% vs 33.8%; P?=?1.305E-04) and in the testing group (3-year OS, 66.8% vs 41.2%; 5-year OS, 31.7% vs 25.8%; P?=?1.027E-03). The areas under the ROC curves (AUC) were significant for both the training and testing groups (3-year AUC, 0.810 vs 0.894; 5-year AUC, 0.792 vs 0.749).

Conclusions

We developed a survival model for LUAD and validated the performance of the model, which may provide superior outcomes for the patients.

SUBMITTER: Wang X

PROVIDER: S-EPMC7115085 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Publications

Development and validation of a survival model for lung adenocarcinoma based on autophagy-associated genes.

Wang Xiaofei X Yao Shuang S Xiao Zengtuan Z Gong Jialin J Liu Zuo Z Han Baoai B Zhang Zhenfa Z

Journal of translational medicine 20200401 1

<h4>Background</h4>Given that abnormal autophagy is involved in the pathogenesis of cancers, we sought to explore the potential value of autophagy-associated genes in lung adenocarcinoma (LUAD).<h4>Methods</h4>RNA sequencing and clinical data on tumour and normal samples were acquired from The Cancer Genome Atlas (TCGA) database and randomly assigned to training and testing groups. Differentially expressed autophagy-associated genes (AAGs) were screened. Within the training group, Cox regression ...[more]

PMID: 32238163

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Project description:BackgroundAutophagy related genes (ARGs) regulate lysosomal degradation to induce autophagy, and are involved in the occurrence and development of a variety of cancers. The expression of ARGs in tumor tissues has a great prospect in predicting the survival of patients. The aim of this study was to construct a prognostic risk score model for lung adenocarcinoma (LUAD) based on ARGs.Methods5,786 ARGs were obtained from GeneCards database. Gene expression profiles and clinical data of 395 LUAD patients were collected from The Cancer Genome Atlas (TCGA) database. All ARGs expression data were extracted, and The ARGs differentially expressed were identified by R software. Survival analysis of differentially expressed ARGs was performed to screen for ARGs with prognostic value, and functional enrichment analysis was performed. The least absolute selection operator (LASSO) regression and Cox regression model were used to construct a prognostic risk scoring model for ARGs. The receiver operating characteristic (ROC) curve was drawn to obtain the optimal cut-off value of risk score. According to the cut-off value, the patients were divided into high-risk group and low-risk group. The area under curve (AUC) and the Kaplan-Meier survival curve was plotted to evaluate the model performance, which was verified in external data sets. Finally, univariate and multivariate Cox regression analysis was applied to evaluate the independent prognostic value of the model, and its clinical relevance was analyzed.ResultsSurvival analysis, Lasso regression and Cox regression analysis were used to construct a LUAD prognostic risk score model with five ARGs (ADAM12, CAMP, DKK1, STRIP2 and TFAP2A). The survival time of patients with low-risk score in this model was significantly better than that of patients with high-risk score (P<0.001). The model showed good prediction performance for LUAD in both the training set (AUCmax=0.78) and two external validation sets (AUCmax=0.88). Risk score was significantly associated with the prognosis of LUAD patients in univariate and multivariate Cox regression analyses, suggested that risk score could be a potential independent prognostic factor for LUAD. Correlation analysis of clinical characteristic showed that high risk score was closely associated with high T stage, high tumor stage and poor prognosis.ConclusionsWe constructed a LUAD risk score model consisting of five ARGs, which can provide a reference for predicting the prognosis of LUAD patients, and may be used in combination with tumor node metastasis (TNM) staging for prognosis prediction of LUAD patients in the future.

Dataset Information

Development and validation of a survival model for lung adenocarcinoma based on autophagy-associated genes.

Background

Methods

Results

Conclusions

Publications

Development and validation of a survival model for lung adenocarcinoma based on autophagy-associated genes.

Similar Datasets

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