A Robust 8-Gene Prognostic Signature for Early-Stage Non-small Cell Lung Cancer

Abstract

Background: The current staging system is imprecise for prognostic prediction of early-stage non–small cell lung cancer (NSCLC). This study aimed to develop a robust prognostic signature for early-stage NSCLC, allowing classification of patients with a high risk of poor outcome and specific treatment decision. Method: In the present study, a comprehensive genome-wide profiling analysis was conducted using a retrospective pool of early-stage NSCLC patient data from the previous datasets of Gene Expression Omnibus (GEO) including GSE31210 , GSE37745 , and GSE50081 and The Cancer Genome Atlas (TCGA). Cox proportional hazards models were implemented to determine the association between gene expression levels and overall patient survival in each dataset. The common genes among all datasets were selected as candidate prognostic genes. A risk score model was developed and validated using four independent datasets and the entire cohort. The Kaplan-Meier with log-rank test was used to assess survival difference. Results: A univariate Cox proportional hazards regression analysis for each dataset showed that a total of 2280 genes in GSE31210, 762 genes in GSE37745, 871 genes in GSE50081, and 666 genes in TCGA were identified as candidate protective genes, while overall 2131 genes in GSE31210, 913 in GSE37745, 1107 in GSE50081, and 997 in TCGA were identified as candidate risky genes. There were 8 common genes associated with overall survival, including 7 mRNA and 1 lncRNA. By using the Step-wise multivariate Cox analysis, an 8-gene prognostic signature (CDCP1, HMMR, TPX2, CIRBP, HLF, KBTBD7, SEC24B-AS1, and SH2B1) for early-stage NSCLC was developed. Patients in the high-risk group had shorter overall survival than those in the low-risk group. Multivariate regression and stratified analysis suggested that the prognostic power of the 8-gene signature was independent of other clinical factors. Furthermore, the 8-gene signature achieved AUC values of 0.726, 0.701, 0.725 and 0.650 in GSE31210, GSE37745, GSE50081 and TCGA, respectively. Moreover, the combination of the 8-gene signature and the stage resulted to a better patient classification for survival prediction and treatment decision. Conclusion: This study developed a robust gene signature with great value for prognostic prediction in early-stage NSCLC, which may contribute to patient classification and personalized treatment decisions.

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

“…Zhang et al found that dissection of the 4th lymph nodes was related to a better prognosis, although this was not recommended by the International Association for the Study of Lung Cancer (IASLC) [32]. There are also scholars who believe that the present staging guide is insufficient for predicting individual-level overall survival because many early-stage patients may experience a later relapse [33,34]. Moreover, Valeria et al suggested abandoning the concept of non-small cell lung cancer because a large body of experimental evidence suggests that LUAD and lung squamous cell carcinoma appear to be distinct tumours at the molecular, pathological and clinical levels [35].…”

Weighted gene expression profiles identify diagnostic and prognostic genes for lung adenocarcinoma and squamous cell carcinoma

“…10 In contrast, a prognostic signature may offer insights into the course of a disease, the prediction of survival rates and the response to a specific treatment. 10,11 As far as NSCLC is concerned, many diagnostic signatures [12][13][14][15] and prognostic signatures [16][17][18][19][20] have been identified. For example, a recent study used 183 AC and 80 SCC patients as a training set and obtained a 42-gene signature to discriminate these two subtypes.…”

Identification of a 4-lncRNA Signature Predicting Prognosis of Patients with Non-Small Cell Lung Cancer: a Multicenter Study in China

“…identi ed a 7-lncRNA signature for predicting the OS of patients with NSCLC after combining lncRNA pro les from 4 GEO datasets and validated the signature in 2 independent datasets (TCGA and GSE31210). Recently, He et al [32] proposed a novel 8-gene signature as a prognostic indicator for patients with early-stage NSCLC after analyzing data from the GEO and TCGA projects. However, the abovementioned prognostic signatures generated by data mining have not been con rmed in patients with NSCLC in a prospective multicenter study.…”