Development and validation of a novel mRNA signature for predicting early relapse in non-small cell lung cancer. (25th May 2021)

• Record Type: Journal Article
• Authors: Lin, Jingping; Weng, Jinsen; Lin, Shaofeng; Lin, Cuibo; Huang, Jieping; Zhang, Chunxia; Zhang, Shen; Dong, Chuanpeng; Ji, Haizhou; Ke, Xi
• Is Part Of: Japanese journal of clinical oncology. Volume 51:Number 8(2021)
• Page Start: 1277
• Page End: 1286
• Publication Date: 2021-05-25
• Subjects: early recurrence -- signature -- lung surgery -- lung medicine -- prognostic factors; Oncology -- Periodicals; Cancer -- Periodicals; 616.994005
• DOI: 10.1093/jjco/hyab075
• Languages: English
• ISSNs: 0368-2811

Abstract

Abstract: Background: Recurrence after initial primary resection is still a major and ultimate cause of death for non-small cell lung cancer patients. We attempted to build an early recurrence associated gene signature to improve prognostic prediction of non-small cell lung cancer. Methods: Propensity score matching was conducted between patients in early relapse group and long-term survival group from The Cancer Genome Atlas training series (N = 579) and patients were matched 1:1. Global transcriptome analysis was then performed between the paired groups to identify tumour-specific mRNAs. Finally, using LASSO Cox regression model, we built a multi-gene early relapse classifier incorporating 40 mRNAs. The prognostic and predictive accuracy of the signature was internally validated in The Cancer Genome Atlas patients. Results: A total of 40 mRNAs were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into a high-risk group and a low-risk group. Relapse-free survival was significantly different between the two groups in both discovery (HR: 3.244, 95% CI: 2.338-4.500, P < 0.001) and internal validation series (HR 1.970, 95% CI 1.181-3.289, P = 0.009). Further analysis revealed that the prognostic value of this signature was independent of tumour stage, histotype and epidermal growth factor receptor mutation ( P < 0.05). Time-dependent receiver operating characteristic analysis showed that the area under receiver operating characteristic curve of this signature was higher than TNM stage alone (0.771 vs 0.686, P < 0.05). Further, decision curve analysis curves analysis at 1 year revealed the considerable clinical utility of this signature in predicting early relapse. Conclusions: We successfully established a reliable signature for predicting early relapse in stage I–III non-small cell lung cancer. Abstract : Relapse after initial primary resection is still a major and ultimate cause of death for non-small cell lung cancer. The developed mRNA signature in this article is reliable in predicting early relapse.