American Association for Cancer Research
10559965epi140389-sup-129831_1_supp_2567118_n88n08.pdf (2.16 MB)

Supplementary Figure 1 from Polymorphisms in MicroRNAs Are Associated with Survival in Non–Small Cell Lung Cancer

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journal contribution
posted on 2023-03-31, 13:21 authored by Yang Zhao, Qingyi Wei, Lingming Hu, Feng Chen, Zhibin Hu, Rebecca S. Heist, Li Su, Christopher I. Amos, Hongbing Shen, David C. Christiani

Supplementary Figure 1: The 3 plots provided the Kaplan-Meier curves of chr7:129197463, rs11048315 and rs7522956 for early-stage NSCLC patients.



Background: MicroRNAs (miRNA) play important roles in the regulation of eukaryotic gene expression and are involved in human carcinogenesis. Single-nucleotide polymorphisms (SNP) in miRNA sequence may alter miRNA functions in gene regulation, which, in turn, may affect cancer risk and disease progression.Methods: We conducted an analysis of associations of 142 miRNA SNPs with non–small cell lung cancer (NSCLC) survival using data from a genome-wide association study (GWAS) in a Caucasian population from the Massachusetts General Hospital (Boston, MA) including 452 early-stage and 526 late-stage NSCLC cases. Replication analyses were further performed in two external populations, one Caucasian cohort from The University of Texas MD Anderson Cancer Center (Houston, TX) and one Han Chinese cohort from Nanjing, China.Results: We identified seven significant SNPs in the discovery set. Results from the independent Caucasian cohort demonstrated that the C allele of rs2042253 (hsa-miRNA-5197) was significantly associated with decreased risk for death among the patients with late-stage NSCLC (discovery set: HR, 0.80; P = 0.007; validation set: HR, 0.86; P = 0.035; combined analysis: HR, 0.87; P = 0.007).Conclusions: These findings provide evidence that some miRNA SNPs are associated with NSCLC survival and can be used as predictive biomarkers.Impact: This study provided an estimate of outcome probability for survival experience of patients with NSCLC, which demonstrates that genetic factors, as well as classic nongenetic factors, may be used to predict individual outcome. Cancer Epidemiol Biomarkers Prev; 23(11); 2503–11. ©2014 AACR.

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