The effect of SNPs in lncRNA as ceRNA on the risk and prognosis of hepatocellular carcinoma

Abstract

Background: Most susceptible loci of hepatocellular carcinoma (HCC) identified by genome-wide association studies (GWAS) are located in non-coding regions, and the mechanism of action remains unclear. The objective of this study was to explore the association of single nucleotide polymorphisms (SNPs) on long non-coding RNAs (lncRNAs) that affect competing endogenous RNAs (ceRNA) regulation mechanism with the risk and prognosis of HCC.

Methods: Based on a set of bioinformatics strategies, eight lncRNA genes that affect HCC through the mechanism of lncRNA-mediated ceRNA were systematically screened, and 15 SNPs that affect microRNA (miRNA) binding in these lncRNA genes were annotated. Genotyping was performed in 800 HCC cases and 801 healthy controls to examine associations of these SNPs with HCC in a northeastern Chinese Han population.

Results: The GG, GC and GG + GC genotypes of HOTAIR rs7958904 were associated with a 0.65, 0.59 and 0.63-fold decreased HCC risk, respectively. In addition, HCC patients with PVT1 rs3931282 AA + GA genotypes were less prone to develop late-stage cancers in a stratified analysis of clinical characteristics. When stratified by clinical biochemical indexes, rs1134492 and rs10589312 in PVT1 and rs84557 in EGFR-AS1 showed significant associations with aspartate aminotransferase (AST), alanine aminotransferase (ALT) or AST/ALT ratio in HCC patients. Furthermore, we constructed potential ceRNA regulatory axes that might be affected by five positive SNPs to explain the causes of these genetic associations.

Conclusions: HOTAIR rs7958904, PVT1 rs3931282, rs1134492 and rs10589312, and EGFR-AS1 rs84557 might be predictors for HCC risk or prognosis. Our results provide new insights into how SNPs on lncRNA-mediated ceRNAs confer interindividual differences to occurrence and progression of HCC.

Keywords: EGFR-AS1; HCC; HOTAIR; PVT1; Prognosis; Risk; SNP; ceRNA.

Fig. 1

Screening results of lncRNA related SNPs. A Clustering heatmap of DElncRNAs. B Clustering heatmap of DEmRNAs. C Clustering heatmap of DEmiRNAs. D Flow chart of screening lncRNA related SNPs. In the heat map, red represents high expression and blue represents low expression; pink represents the paracancerous control group and blue represents the HCC case group. E Genomic visualization of 8 lncRNAs and 22 candidate SNPs. F Related lncRNA-SNP-miRNA network diagram. Yellow rectangles are SNPs, red is high expression, green is low expression, ellipses are miRNAs, and diamonds are lncRNAs

Fig. 2

The genotype distribution and association analysis of 15 lncRNA-SNPs with HCC risk. A The genotype distribution, HWE test, and association analysis under the codominant model 1, codominant model 2 and dominant model. 1 is the TCTTGC/TCTTGC genotype of rs10589312, 2 is the TCTTGC/T genotype of rs10589312, 1 + 2 is the TCTTGC/TCTTGC + TCTTGC/T genotype of rs10589312, and 3 is the T/T genotype of rs10589312. The values in red italics are statistically significant. B Association analysis of SNP rs7958904 with HCC risk after adjusted. OR and P values were adjusted for age, gender, smoking and drinking by logistic regression. The values in red italics are statistically significant

Fig. 3

Association analysis of lncRNA-SNPs with clinical characteristics or clinical test indexes in HCC patients. A Association analysis of rs3931282 with clinical characteristics in HCC patients. B Significant results of association analysis between 14 SNPs and clinical test indicators. Model 1 is the TCTTGC/TCTTGC + TCTTGC/T genetic model of rs10589312. Model 2 is the CC + C/CA genetic model of rs11438260. OR and P values were adjusted for age, gender, smoking, drinking, HBsAg and anti-HCV by logistic regression. The values in red italics are statistically significant. The size of the clinical test indexes is represented by the median value (inter quartile range)

Fig. 4

Bioinformatics analysis of positive SNPs-related miRNAs. A-C Bioinformatics analysis of HOTAIR-rs7958904-miR-615-3p. D-F Bioinformatics analysis of PVT1-rs3931282-miR-205-5p. G-I Bioinformatics analysis of PVT1-rs1134492-miR-34b-5p. J-L Bioinformatics analysis of PVT1-rs10589312-miR-183-3p. M–O Bioinformatics analysis of PVT1-rs10589312-miR-31-5p. P-R Bioinformatics analysis of EGFR-AS1-rs84557-miR-33b-5p. Each node in enrichment analysis of functional modular genes represents a term, the connection between the node and gene reflects the existence of correlation, and the color reflects the enrichment classification of node and gene

Fig. 5

Six potential ceRNA regulatory axes affected by five positive SNPs