Dataset Information

Development and Validation of a Seven-Gene Signature for Predicting the Prognosis of Lung Adenocarcinoma.

ABSTRACT:

Background

Prognosis is a main factor affecting the survival of patients with lung adenocarcinoma (LUAD), yet no robust prognostic model of high effectiveness has been developed. This study is aimed at constructing a stable and practicable gene signature-based model via bioinformatics methods for predicting the prognosis of LUAD sufferers.

Methods

The mRNA expression data were accessed from the TCGA-LUAD dataset, and paired clinical information was collected from the GDC website. R package "edgeR" was employed to select the differentially expressed genes (DEGs), which were then used for the construction of a gene signature-based model via univariate COX, Lasso, and multivariate COX regression analyses. Kaplan-Meier and ROC survival analyses were conducted to comprehensively evaluate the performance of the model in predicting LUAD prognosis, and an independent dataset GSE26939 was accessed for further validation.

Results

Totally, 1,655 DEGs were obtained, and a 7-gene signature-based risk score was developed and formulated as risk_score = 0.000245∗NTSR1 + (7.13E - 05)∗RHOV + 0.000505∗KLK8 + (7.01E - 05)∗TNS4 + 0.000288∗C1QTNF6 + 0.00044∗IVL + 0.000161∗B4GALNT2. Kaplan-Meier survival curves revealed that the survival rate of patients in the high-risk group was lower in both the TCGA-LUAD dataset and GSE26939 relative to that of patients in the low-risk group. The relationship between the risk score and clinical characteristics was further investigated, finding that the model was effective in prognosis prediction in the patients with different age (age > 65, age < 65) and TNM stage (N0&N1, T1&T2, and tumor stage I/II). In sum, our study provides a robust predictive model for LUAD prognosis, which boosts the clinical research on LUAD and helps to explore the mechanism underlying the occurrence and progression of LUAD.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC7641279 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Development and Validation of a Seven-Gene Signature for Predicting the Prognosis of Lung Adenocarcinoma.

Zhang Yingqing Y Zhang Xiaoping X Lv Xiaodong X Zhang Ming M Gao Xixi X Liu Jialiang J Xu Yufen Y Fang Zhixian Z Chen Wenyu W

BioMed research international 20200817

<h4>Background</h4>Prognosis is a main factor affecting the survival of patients with lung adenocarcinoma (LUAD), yet no robust prognostic model of high effectiveness has been developed. This study is aimed at constructing a stable and practicable gene signature-based model via bioinformatics methods for predicting the prognosis of LUAD sufferers.<h4>Methods</h4>The mRNA expression data were accessed from the TCGA-LUAD dataset, and paired clinical information was collected from the GDC website. ...[more]

PMID: 33195688

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Project description:Background: Lung adenocarcinoma (LUAD) is one of the main types of lung cancer. Because of its low early diagnosis rate, poor late prognosis, and high mortality, it is of great significance to find biomarkers for diagnosis and prognosis. Methods: Five hundred and twelve LUADs from The Cancer Genome Atlas were used for differential expression analysis and short time-series expression miner (STEM) analysis to identify the LUAD-development characteristic genes. Survival analysis was used to identify the LUAD-unfavorable genes and LUAD-favorable genes. Gene set variation analysis (GSVA) was used to score individual samples against the two gene sets. Receiver operating characteristic (ROC) curve analysis and univariate and multivariate Cox regression analysis were used to explore the diagnostic and prognostic ability of the two GSVA score systems. Two independent data sets from Gene Expression Omnibus (GEO) were used for verifying the results. Functional enrichment analysis was used to explore the potential biological functions of LUAD-unfavorable genes. Results: With the development of LUAD, 185 differentially expressed genes (DEGs) were gradually upregulated, of which 84 genes were associated with LUAD survival and named as LUAD-unfavorable gene set. While 237 DEGs were gradually downregulated, of which 39 genes were associated with LUAD survival and named as LUAD-favorable gene set. ROC curve analysis and univariate/multivariate Cox proportional hazards analyses indicated both of LUAD-unfavorable GSVA score and LUAD-favorable GSVA score were a biomarker of LUAD. Moreover, both of these two GSVA score systems were an independent factor for LUAD prognosis. The LUAD-unfavorable genes were significantly involved in p53 signaling pathway, Oocyte meiosis, and Cell cycle. Conclusion: We identified and validated two LUAD-development characteristic gene sets that not only have diagnostic value but also prognostic value. It may provide new insight for further research on LUAD.

Project description:BackgroundThe high case-fatality rate of patients with lung adenocarcinoma (LUAD) emphasizes the importance of identifying a robust and reliable prognostic signature for LUAD patients. Endoplasmic reticulum (ER) stress results from protein misfolding imbalance and has been shown to participate in the development of cancer. We aimed to develop and validation a reliable and robust ER stress-related prognostic signature to accurately predict prognosis for patients with LUAD.MethodsThe mRNA expressions data and the clinical information were downloaded from The Cancer Genome Atlas (TCGA) as training set. The data of external validation sets were downloaded from GEO database with the accession number GSE 30219, GSE 31210, GSE 50081 and GSE 37745. Univariate Cox regression analyses was performed to identify mRNAs associated with overall survival (OS) in LUAD. ER-associated genes were retrieved using GeneCards database. Next, we construct the best risk score model by the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation. Subsequently, predictive models and risk scores were developed in the TCGA training dataset. Cox proportional hazards regression models were used for univariate and multivariate analysis of risk score and clinicopathologic characteristics. As a validation set GSE30219, GSE31210 and (GSE50081+GSE37745) were used to validate the predictive performance of the model in TCGA. Finally, functional enrichment analysis, including the gene ontology (GO) enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene set enrichment analysis (GSEA) were performed to further explore function and mechanisms.ResultsA prognostic prediction model based on eight genes was developed in the TCGA training dataset. As expected, in validation sets, patients with higher risk scores were found to have worse prognosis. Time-dependent ROC curve analyses demonstrated that the risk score model was reliable. The nomograms showed excellent predictive ability. Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for LUAD. Additionally, functional enrichment analysis showed that the relevant biomarkers were enriched in cell cycle and glycolysis related signaling pathways.ConclusionsThe 8-gene signature may enable improved the prediction of clinical events and decisions about management of LUAD.

Dataset Information

Development and Validation of a Seven-Gene Signature for Predicting the Prognosis of Lung Adenocarcinoma.

Background

Methods

Results

Publications

Development and Validation of a Seven-Gene Signature for Predicting the Prognosis of Lung Adenocarcinoma.

Similar Datasets

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