Emerging evidence indicates that FHIT is a candidate tumor suppressor in many types of tumors including non-small-cell lung carcinoma (NSCLC). However, the prognostic value and correlation between FHIT hypermethylation and clinicopathological characteristics of NSCLC remains unclear. In this report, we performed a meta-analysis to evaluate the effects of FHIT hypermethylation on the incidence of NSCLC and clinicopathological characteristics of human NSCLC patients. Final analysis of 1,801 NSCLC patients from 18 eligible studies was performed. FHIT hypermethylation was found to be significantly higher in NSCLC than in normal lung tissue. The pooled odds ratio (OR) from ten studies included 819 NSCLC and 792 normal lung tissues (OR =7.51, 95% confidence interval [CI] =2.98–18.91, P<0.0001). Subgroup analysis based on ethnicity implied that FHIT hypermethylation level was higher in NSCLC tissues than in normal tissues in both Caucasians (P=0.02) and Asians (P<0.0001), indicating that the difference in Asians was much more significant. FHIT hypermethylation was also correlated with sex status, smoking status, as well as pathological types. In addition, patients with FHIT hypermethylation had a lower survival rate than those without (hazard ratio =1.73, 95% CI =1.10–2.71, P=0.02). The results of this meta-analysis suggest that FHIT hypermethylation is associated with an increased risk and poor survival in NSCLC patients. FHIT hypermethylation, which induces the inactivation of FHIT gene, plays an important role in the carcinogenesis and clinical outcome and may serve as a potential diagnostic marker and drug target of NSCLC.
The clinicopathological significance and ethnic difference of FHIT hypermethylation in non-small-cell lung carcinoma: a meta-analysis and literature review
Xiao-yu Wu,Guannan Wu,Xue-Quan Yao,Gang Hou,Feng Jiang
Published 2016 in Drug Design, Development and Therapy
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- Publication year
2016
- Venue
Drug Design, Development and Therapy
- Publication date
2016-02-15
- Fields of study
Biology, Medicine
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Semantic Scholar, PubMed
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