Abstract
The abundant expression of collagen type VI α5 (COL6A5) exists in lung tissue, and its role in lung cancer is still unknown. We performed a genetic association study with an attempt to detect the relationships between single nucleotide polymorphisms (SNPs) in COL6A5 and lung cancer predisposition in Chinese Han population. We finally selected six tag-SNPs to determine their genotypes among 510 lung cancer patients and 495 healthy controls with the MassARRAY platform. The associations of SNPs and lung cancer risk were estimated by logistic regression method with adjustment for confounding factors. Two available databases were used for gene expression and prognosis analysis. COL6A5 rs13062453, rs1497305, and rs77123808 were significantly associated with the risk of lung cancer in the whole population or stratified subgroups (p < 0.05). Among them, COL6A5 rs13062453 and rs1497305 were also linked to the susceptibility of lung adenocarcinoma. Additionally, rs1497305 was found to be strongly related to the TNM staging under five genetic models (p < 0.05). Results from databases suggested the important role of COL6A5 in lung cancer development. COL6A5 polymorphisms rs13062453, rs1497305 and rs77123808 were associated with lung cancer risk in Chinese Han population. These findings first yield new insight of COL6A5 in lung cancer.
Highlights
The abundant expression of collagen type VI α5 (COL6A5) exists in lung tissue, and its role in lung cancer is still unknown
We hypothesized that the single nucleotide polymorphisms (SNPs) in COL6A5 might be associated with lung cancer risk, and performed a genetic association study to investigate the relationships between the COL6A5 variations and lung cancer predisposition among Chinese Han population
Prognosis analysis indicated that patients with low expression of COL6A5 had worse overall survival (Fig. 3, hazard ratio (HR) = 0.67, 95% confidence intervals (CIs) = 0.56–0.79, p < 0.001)
Summary
The abundant expression of collagen type VI α5 (COL6A5) exists in lung tissue, and its role in lung cancer is still unknown. The present study was carried out based on two groups of sample: 510 patients with diagnosed lung cancer and 495 healthy individuals. Logistic regression analysis with adjustment was conducted to estimate the associations of the candidate SNPs with lung cancer susceptibility in genetic models (allele model, genotype model, dominant model, recessive model, and additive model) via available odds ratio (OR) values and 95% confidence intervals (CIs).
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