A robust six-gene prognostic signature for prediction of both disease-free and overall survival in non-small cell lung cancer

Abstract

Background The high mortality of patients with non-small cell lung cancer (NSCLC) emphasizes the necessity of identifying a robust and reliable prognostic signature for NSCLC patients. This study aimed to identify and validate a prognostic signature for the prediction of both disease-free survival (DFS) and overall survival (OS) of NSCLC patients by integrating multiple datasets. Methods We firstly downloaded three independent datasets under the accessing number of GSE31210, GSE37745 and GSE50081, and then performed an univariate regression analysis to identify the candidate prognostic genes from each dataset, and identified the gene signature by overlapping the candidates. Then, we built a prognostic model to predict DFS and OS using a risk score method. Kaplan–Meier curve with log-rank test was used to determine the prognostic significance. Univariate and multivariate Cox proportional hazard regression models were implemented to evaluate the influences of various variables on DFS and OS. The robustness of the prognostic gene signature was evaluated by re-sampling tests based on the combined GEO dataset (GSE31210, GSE37745 and GSE50081). Furthermore, a The Cancer Genome Atlas (TCGA)-NSCLC cohort was utilized to validate the prediction power of the gene signature. Finally, the correlation of the risk score of the gene signature and the Gene set variation analysis (GSVA) score of cancer hallmark gene sets was investigated. Results We identified and validated a six-gene prognostic signature in this study. This prognostic signature stratified NSCLC patients into the low-risk and high-risk groups. Multivariate regression and stratification analyses demonstrated that the six-gene signature was an independent predictive factor for both DFS and OS when adjusting for other clinical factors. Re-sampling analysis implicated that this six-gene signature for predicting prognosis of NSCLC patients is robust. Moreover, the risk score of the gene signature is correlated with the GSVA score of 7 cancer hallmark gene sets. Conclusion This study provided a robust and reliable gene signature that had significant implications in the prediction of both DFS and OS of NSCLC patients, and may provide more effective treatment strategies and personalized therapies. Electronic supplementary material The online version of this article (10.1186/s12967-019-1899-y) contains supplementary material, which is available to authorized users.

Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma

“…To the best of our knowledge, previous studies have focused on NSCLC survival prediction, but few have examined early-stage LUAD survival prediction. However, most current prediction models are derived from clinical trials with small sample sizes [20] or single omics data [21–23]. To compare the value of our proposed model, we performed a systematic review of published literature by querying PubMed using the following search terms: “((early stage) OR (stage I) OR (stage II)) AND (lung adenocarcinoma) AND (prognosis) AND ((prediction) OR (AUC) OR (c-index))” through April 29, 2019.…”

Identification of an Autophagy-Related Signature Predicting Overall Survival for Papillary Thyroid Carcinoma

“…Recently, several studies indicated that gene signature can well predict the prognosis of cancer. 12,13 However, no prior prognostic models for papillary thyroid carcinoma were established based on autophagy-related genes (ARGs). Therefore, the current study aimed to identify differentially expressed ARGs and build a novel prognostic signature to predict the prognosis of patients with papillary thyroid carcinoma.…”

A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining

“…Differentially expressed genes (DEGs) that regulated by gene transcription are implicated in diverse biological processes. Gene-expression profiling analysis made some progresses in predicting overall survival (OS) in NSCLC [ 1 , 6 , 7 ]. Mascaux et al showed that immune activation and immune escape in tumor microenvironment (TME) occurred before lung cancer invasion [ 7 ].…”

“…Although several genes or long noncoding RNA expression signatures, including programmed death-ligand 1(PD-L1), have been recently proposed for predicting the OS in NSCLC [ 6 , 8 – 10 ], the prognostic value of an effective and new biomarker of gene profile is still limited. DEG signatures identification related to patient OS in standard clinical samples may promote the development of molecular drug subtypes and potential therapy targets.…”