Zhendong Gao1,2,3, Han Han1,2,3, Yue Zhao1,2,3, Hui Yuan1,2,3, Shanbo Zheng1,2,3, Yang Zhang1,2,3, Haiquan Chen4,5,6. 1. Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudand University Shanghai Cancer Center, 270 Dong-An Road, Shanghai, 200032, China. 2. Institute of Thoracic Oncology, Fudan University, Shanghai, 200032, China. 3. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China. 4. Department of Thoracic Surgery and State Key Laboratory of Genetic Engineering, Fudand University Shanghai Cancer Center, 270 Dong-An Road, Shanghai, 200032, China. hqchen1@yahoo.com. 5. Institute of Thoracic Oncology, Fudan University, Shanghai, 200032, China. hqchen1@yahoo.com. 6. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China. hqchen1@yahoo.com.
Abstract
OBJECTIVES: Postoperative early relapse of early-stage lung adenocarcinoma is implicated in poor prognosis. The purpose of our study was to develop an integrated mRNA and non-coding RNA (ncRNA) signature to identify patients at high risk of early relapse in stage I-II lung adenocarcinoma who underwent complete resection. METHODS: Early-stage lung adenocarcinoma data from Gene Expression Omnibus database were divided into training set and testing set. Propensity score matching analysis was performed between patients in early relapse group and long-term nonrelapse group from training set. Transcriptome analysis, random survival forest and LASSO Cox regression model were used to build an early relapse-related multigene signature. The robustness of the signature was evaluated in testing set and RNA-Seq dataset from The Cancer Genome Atlas (TCGA). The chemotherapy sensitivity, tumor microenvironment and mutation landscape related to the signature were explored using bioinformatics analysis. RESULTS: Twelve mRNAs and one ncRNA were selected. The multigene signature achieved a strong power for early relapse prediction in training set (HR 3.19, 95% CI 2.16-4.72, P < 0.001) and testing set (HR 2.91, 95% CI 1.63-5.20, P = 0.002). Decision curve analyses revealed that the signature had a good clinical usefulness. Groups divided by the signature exhibited different chemotherapy sensitivity, tumor microenvironment characteristics and mutation landscapes. CONCLUSIONS: Our results indicated that the integrated mRNA-ncRNA signature may be an innovative biomarker to predict early relapse of early-stage lung adenocarcinoma, and may provide more effective treatment strategies.
OBJECTIVES: Postoperative early relapse of early-stage lung adenocarcinoma is implicated in poor prognosis. The purpose of our study was to develop an integrated mRNA and non-coding RNA (ncRNA) signature to identify patients at high risk of early relapse in stage I-II lung adenocarcinoma who underwent complete resection. METHODS: Early-stage lung adenocarcinoma data from Gene Expression Omnibus database were divided into training set and testing set. Propensity score matching analysis was performed between patients in early relapse group and long-term nonrelapse group from training set. Transcriptome analysis, random survival forest and LASSO Cox regression model were used to build an early relapse-related multigene signature. The robustness of the signature was evaluated in testing set and RNA-Seq dataset from The Cancer Genome Atlas (TCGA). The chemotherapy sensitivity, tumor microenvironment and mutation landscape related to the signature were explored using bioinformatics analysis. RESULTS: Twelve mRNAs and one ncRNA were selected. The multigene signature achieved a strong power for early relapse prediction in training set (HR 3.19, 95% CI 2.16-4.72, P < 0.001) and testing set (HR 2.91, 95% CI 1.63-5.20, P = 0.002). Decision curve analyses revealed that the signature had a good clinical usefulness. Groups divided by the signature exhibited different chemotherapy sensitivity, tumor microenvironment characteristics and mutation landscapes. CONCLUSIONS: Our results indicated that the integrated mRNA-ncRNA signature may be an innovative biomarker to predict early relapse of early-stage lung adenocarcinoma, and may provide more effective treatment strategies.
Authors: Vural Tagal; Shuguang Wei; Wei Zhang; Rolf A Brekken; Bruce A Posner; Michael Peyton; Luc Girard; TaeHyun Hwang; David A Wheeler; John D Minna; Michael A White; Adi F Gazdar; Michael G Roth Journal: Nat Commun Date: 2017-01-19 Impact factor: 14.919