Genetic Variants in One-Carbon Metabolism Pathway Predict Survival Outcomes of Early-Stage Non-Small Cell Lung Cancer

Lung Cancer
14/08/2020

Oncology. 2020 Aug 13:1-8. doi: 10.1159/000509658. Online ahead of print.

ABSTRACT

BACKGROUND: This study was conducted to investigate the association between genetic variants in one-carbon metabolism and survival outcomes of surgically resected non-small cell lung cancer (NSCLC).

METHODS: We genotyped 41 potentially functional variants of 19 key genes in the one-carbon metabolism pathway among 750 NSCLC patients who underwent curative surgery. The association between genetic variants and overall survival (OS)/disease-free survival (DFS) were analyzed.

RESULTS: Among the 41 single-nucleotide polymorphisms (SNPs) analyzed, 4 SNPs (MTHFD1L rs6919680T>G and rs3849794T>C, MTR rs2853523C>A, and MTHFR rs4846049G>T) were significantly associated with survival outcomes. MTHFD1L rs6919680T>G and MTR rs2853523C>A were significantly associated with better OS (adjusted hazard ratio [aHR] = 0.73, 95% confidence interval [CI] = 0.54-0.99, p = 0.04) and worse OS (aHR = 2.14, 95% CI = 1.13-4.07, p = 0.02), respectively. MTHFD1L rs3849794T>C and MTHFR rs4846049G>T were significantly associated with worse DFS (aHR = 1.41, 95% CI = 1.08-1.83, p = 0.01; and aHR = 1.97, 95% CI = 1.10-3.53, p = 0.02, respectively). When the patients were divided according to histology, the associations were significant only in squamous cell carcinoma (SCC), but not in adenocarcinoma (AC). In SCC, MTHFD1L rs6919680T>G and MTR rs2853523C>A were significantly associated with better OS (aHR = 0.64, 95% CI = 0.41-1.00, p = 0.05) and worse OS (aHR = 2.77, 95% CI = 1.11-6.91, p = 0.03), respectively, and MTHFD1L rs3849794T>C and MTHFR rs4846049G>T were significantly associated with worse DFS (aHR = 1.73, 95% CI = 1.17-2.56, p = 0.01; and aHR = 2.78, 95% CI = 1.12-6.88, p = 0.03, respectively).

CONCLUSIONS: Our results suggest that the genetic variants in the one-carbon metabolism pathway could be used as biomarkers for predicting the clinical outcomes of patients with early-stage NSCLC.