In:
Japanese Journal of Clinical Oncology, Oxford University Press (OUP), Vol. 51, No. 8 ( 2021-08-01), p. 1277-1286
Abstract:
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 & lt; 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 & lt; 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 & lt; 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.
Type of Medium:
Online Resource
ISSN:
1465-3621
DOI:
10.1093/jjco/hyab075
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2021
detail.hit.zdb_id:
1494610-5
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