ObjectiveTo investigate the association of the genetic variation of the long non-coding RNA HOX transcript antisense RNA (HOATIR) and gene-environment interaction with prognosis-related clinical features of liver cancer. MethodsA total of 923 patients with primary liver cancer Shunde Hospital of Southern Medical University were admitted to a hospital from October 2010 to October 2016 were enrolled in this study. TaqMan quantitative PCR was used to detect HOTAIR rs17105613 T＞C, rs12427129 C＞T, and rs3816153 G＞T genotypes. The chi-square test was used to analyze the difference in the distribution of clinical features of liver cancer, and the logistic regression model was used to analyze the influence of the genetic variation of HOTAIR on the TNM stage of liver cancer, portal vein tumor thrombus, and age of onset. ResultsAfter the adjustment for environmental factors, rs17105613 and rs3816153 were significantly associated with TNM stage in the recessive mode (P＜005), and there was a statistically significant multiplicative interaction between rs12427129 and smoking on the age of onset of liver cancer (P=0.029), as well as an additive interaction with critical statistical significance (P=0.092). ConclusionHOTAIR rs17105613 and rs3816153 may be associated with TNM stage of liver cancer. The interaction between rs12427129 and smoking may influence the age of onset of liver cancer. Therefore, the genetic variation of HOTAIR may promote the invasion and metastasis of hepatoma cells.

Objective:To analyze the association between low-frequency variants of ARID1A gene and primary liver cancer using latent category model.Methods:The low-frequency variants of ARID1A gene was combined according to different functional areas, and the combined variables were analyzed by using the latent class model to obtain the latent variables. Then the logistic regression was used to analyze the association between low-frequency variants of ARID1A gene and primary liver cancer.Results:The low-frequency variants of ARID1A gene were divided into three categories by the latent class model. The class 1 was mainly unmutated population, the proportion was 94.2% (2 454/2 603). The class 2 was mainly transcriptional regulatory domain mutation, take 4.8% (124/2 603). The class 3 was dominantly exon mutation, about 1.0% (27/2 603). Using class 1 as a reference, it was found that mutations in the transcriptional regulatory domain could reduce the risk of liver cancer ( OR=0.601, 95% CI=0.364-0.992, P=0.046). Conclusion:The latent class model can identify low-frequency variants of gene associated with liver cancer and can be extended to more genetic association studies of low-frequency variants related to complex diseases.

Objective:To analyze the association between low-frequency variants of ARID1A gene and primary liver cancer using latent category model.Methods:The low-frequency variants of ARID1A gene was combined according to different functional areas, and the combined variables were analyzed by using the latent class model to obtain the latent variables. Then the logistic regression was used to analyze the association between low-frequency variants of ARID1A gene and primary liver cancer.Results:The low-frequency variants of ARID1A gene were divided into three categories by the latent class model. The class 1 was mainly unmutated population, the proportion was 94.2% (2 454/2 603). The class 2 was mainly transcriptional regulatory domain mutation, take 4.8% (124/2 603). The class 3 was dominantly exon mutation, about 1.0% (27/2 603). Using class 1 as a reference, it was found that mutations in the transcriptional regulatory domain could reduce the risk of liver cancer ( OR=0.601, 95% CI=0.364-0.992, P=0.046). Conclusion:The latent class model can identify low-frequency variants of gene associated with liver cancer and can be extended to more genetic association studies of low-frequency variants related to complex diseases.