A Key mRNA-miRNA-lncRNA Competing Endogenous RNA Triple Sub-network Linked to Diagnosis and Prognosis of Hepatocellular Carcinoma

Abstract

Growing evidence has illustrated critical roles of competing endogenous RNA (ceRNA) regulatory network in human cancers including hepatocellular carcinoma. In this study, we aimed to find promising diagnostic and prognostic biomarkers for patients with hepatocellular carcinoma. Three novel unfavorable prognosis-associated genes (CELSR3, GPSM2, and CHEK1) was first identified. We also demonstrated that these genes were significantly upregulated in hepatocellular carcinoma cell lines and tissues. Next, 154 potential miRNAs of CELSR3, GPSM2, and CHEK1 were predicted. CHEK1-hsa-mir-195-5p/hsa-mir-497-5p and GPSM2-hsa-mir-122-5p axes were defined as two key pathways in carcinogenesis of hepatocellular carcinoma by combination of in silico analysis and experimental validation. Subsequently, lncRNAs binding to hsa-mir-195-5p, hsa-mir-497-5p, and hsa-mir-122-5p were predicted via starBase and miRNet databases. After performing expression analysis and survival analysis for these predicted lncRNAs, we showed that nine lncRNAs (SNHG1, SNHG12, LINC00511, HCG18, FGD5-AS1, CERS6-AS1, NUTM2A-AS1, SNHG16, and ASB16-AS1) were markedly increased in hepatocellular carcinoma and their upregulation indicated poor prognosis. Moreover, a similar mRNA-miRNA-lncRNA analysis for six "known" genes (CLEC3B, DNASE1L3, PTTG1, KIF2C, XPO5, and UBE2S) was performed. Subsequently, a comprehensive mRNA-miRNA-lncRNA triple ceRNA network linked to prognosis of patients with hepatocellular carcinoma was established. Moreover, all RNAs in this network exhibited significantly diagnostic values for patients with hepatocellular carcinoma. In summary, the current study constructed a mRNA-miRNA-lncRNA ceRNA network associated with diagnosis and prognosis of hepatocellular carcinoma.

Keywords: competing endogenous RNA (ceRNA); diagnosis; hepatocellular carcinoma; log non-coding RNA (lncRNA); microRNA (miRNA); prognosis.

Figure 1

Identification of novel genes significantly associated with prognosis of hepatocellular carcinoma. (A) Identification of CELSR3, GPSM2, and CHEK1 as three novel genes linked to prognosis of hepatocellular carcinoma by intersecting the genes significantly associated with overall survival (OS) and disease-free survival (RFS) and excluding those genes previously studied; high expression of CELSR3 indicated poor OS (B) and RFS (C) of hepatocellular carcinoma; high expression of GPSM2 indicated poor OS (D) and RFS (E) of hepatocellular carcinoma; high expression of CHEK1 indicated poor OS (F) and RFS (G) of hepatocellular carcinoma. Logrank P < 0.05 was considered as statistically significant.

Figure 2

The mRNA expression levels of CELSR3, GPSM2, and CHEK1 in hepatocellular carcinoma. mRNA expression of CELSR3 (A), GPSM2 (B), and CHEK1 (C) were significantly increased in tumor tissues compared to normal controls determined by UALCAN database; mRNA expression of CELSR3 (D), GPSM2 (E), and CHEK1 (F) were significantly increased in tumor tissues compared to normal controls determined by Oncomine database. CELSR3 (G), GPSM2 (H), and CHEK1 (I) were markedly upregulated in hepG2 and LM3 when compared with HL7702; mRNA expression of CELSR3 (J), GPSM2 (K), and CHEK1 (L) were obviously higher in tumor tissues than that in adjacent normal tissues; normalization was done relative to GAPDH. *P < 0.05.

Figure 3

Construction of CELSR3/GPSM2/CHEK1-miRNA network by miRNet database and Cytoscape software.

Figure 4

Significantly correlated mRNA-miRNA pairs determined by starBase database. (A) Expression of hsa-mir-30a-5p was negatively associated with CELSR3 expression; (B) expression of hsa-mir-4646-3p was negatively associated with CELSR3 expression; (C) expression of hsa-mir-195-5p was negatively associated with CHEK1 expression; (D) expression of hsa-mir-193b-3p was negatively associated with CHEK1 expression; (E) expression of hsa-mir-497-5p was negatively associated with CHEK1 expression; (F) expression of hsa-mir-139-3p was negatively associated with CHEK1 expression; (G) expression of hsa-mir-122-5p was negatively associated with GPSM2 expression; (H) expression of hsa-mir-378a-5p was negatively associated with GPSM2 expression.

Figure 5

Identification of the most potential miRNAs associated with prognosis of hepatocellular carcinoma. Expression and prognostic value of hsa-mir-30a-5p (A), hsa-mir-4646-3p (B), hsa-mir-195-5p (C), hsa-mir-193b-3p (D), hsa-mir-497-5p (E), hsa-mir-139-3p (F), hsa-mir-122-5p (G), and hsa-mir-378a-5p (H) in hepatocellular carcinoma; (I) after overexpression of hsa-mir-195-5p, mRNA expression of CHEK1 was significantly decreased in hepG2 and LM3; (J) after overexpression of hsa-mir-497-5p, mRNA expression of CHEK1 was significantly decreased in hepG2 and LM3; (K) after overexpression of hsa-mir-139-3p, no significant downregulation of CHEK1 mRNA expression was observed in hepG2 and LM3; (L) after overexpression of hsa-mir-122-5p, mRNA expression of GPSM2 was significantly decreased in hepG2 and LM3. *P < 0.05.

Figure 6

Prediction of upstream lncRNAs potentially binding to hsa-mir-195-5p, hsa-mir-497-5p, and hsa-mir-122-5p. (A) Potential lncRNAs of hsa-mir-195-5p predicted by miRNet and starBase databases; (B) potential lncRNAs of hsa-mir-497-5p predicted by miRNet and starBase databases; (C) potential lncRNAs of hsa-mir-122-5p predicted by miRNet and starBase databases; (D) establishment of miRNA-lncRNA network using Cytoscape software.

Figure 7

Expression analysis and survival analysis of potential lncRNAs in hepatocellular carcinoma. (A) SNHG1, SNHG12, LINC00511, HCG18, FGD5-AS1, CERS6-AS1, NUTM2A-AS1, SNHG16, and ASB16-AS1 were identified as key lncRNAs in hepatocellular carcinoma by combination of their expression levels and prognostic values; (B) SNHG1 was significantly upregulated in tumor tissues and linked to poor prognosis of hepatocellular carcinoma; (C) SNHG12 was significantly upregulated in tumor tissues and linked to poor prognosis of hepatocellular carcinoma; (D) LINC00511 was significantly upregulated in tumor tissues and linked to poor prognosis of hepatocellular carcinoma; (E) HCG18 was significantly upregulated in tumor tissues and linked to poor prognosis of hepatocellular carcinoma; (F) FGD5-AS1 was significantly upregulated in tumor tissues and linked to poor prognosis of hepatocellular carcinoma; (G) CERS6-AS1 was significantly upregulated in tumor tissues and linked to poor prognosis of hepatocellular carcinoma; (H) NUTM2A-AS1 was significantly upregulated in tumor tissues and linked to poor prognosis of hepatocellular carcinoma; (I) SNHG16 was significantly upregulated in tumor tissues and linked to poor prognosis of hepatocellular carcinoma; (J) ASB16-AS1 was significantly upregulated in tumor tissues and linked to poor prognosis of hepatocellular carcinoma. *P < 0.05.

Figure 8

Prediction and validation of upstream miRNAs and lncRNAs of six “known” genes. Expression and prognostic value of hsa-mir-101-3p (A), hsa-mir-148a-3p (B), hsa-mir-4524a-3p (C), hsa-mir-122-5p (D). Potential lncRNAs of hsa-mir-101-3p (E), hsa-mir-148a-3p (F), hsa-mir-122-5p (G), predicted by miRNet and starBase. (H) Expression of SNHG6 in hepatocellular carcinoma analyzed by GEPIA. (I) The prognostic value of SNHG6 in hepatocellular carcinoma assessed by GEPIA database.

Figure 9

Correlation analysis of potential mRNA-lncRNA pairs in hepatocellular carcinoma determined by starBase. (A) SNHG1 expression was significantly positively correlated with CHEK1 expression in hepatocellular carcinoma; (B) LINC00511 expression was significantly positively correlated with CHEK1 expression in hepatocellular carcinoma; (C) HCG18 expression was significantly positively correlated with CHEK1 expression in hepatocellular carcinoma; (D) NUTM2A-AS1 expression was significantly positively correlated with CHEK1 expression in hepatocellular carcinoma; (E) SNHG16 expression was significantly positively correlated with CHEK1 expression in hepatocellular carcinoma; (F) ASB16-AS1 expression was significantly positively correlated with GPSM2 expression in hepatocellular carcinoma; (G) ASB16-AS1 expression was significantly positively correlated with XPO5 expression in hepatocellular carcinoma; (H) SNHG1 expression was significantly positively correlated with KIF2C expression in hepatocellular carcinoma; (I) SNHG6 expression was significantly positively correlated with KIF2C expression in hepatocellular carcinoma; (J) HCG18 expression was significantly positively correlated with KIF2C expression in hepatocellular carcinoma; (K) NUTM2A-AS1 expression was significantly positively correlated with KIF2C expression in hepatocellular carcinoma.

Figure 10

The established mRNA-miRNA-lncRNA competing endogenous RNA (ceRNA) triple network associated with progression and prognosis of hepatocellular carcinoma.

Figure 11

The diagnostic values of 16 potential molecules in hepatocellular carcinoma. (A) CHEK1 ROC analysis in hepatocellular carcinoma; (B) GPSM2 ROC analysis in hepatocellular carcinoma; (C) KIF2C ROC analysis in hepatocellular carcinoma; (D) XPO5 ROC analysis in hepatocellular carcinoma; (E) hsa-mir-195-5p ROC analysis in hepatocellular carcinoma; (F) hsa-mir-497-5p ROC analysis in hepatocellular carcinoma; (G) hsa-mir-122-5p ROC analysis in hepatocellular carcinoma; (H) hsa-mir-101-3p ROC analysis in hepatocellular carcinoma; (I) hsa-mir-148a-5p ROC analysis in hepatocellular carcinoma; (J) SNHG1 ROC analysis in hepatocellular carcinoma; (K) HCG18 ROC analysis in hepatocellular carcinoma; (L) NUTM2A-AS1 ROC analysis in hepatocellular carcinoma; (M) SNHG16 ROC analysis in hepatocellular carcinoma; (N) LINC00511 ROC analysis in hepatocellular carcinoma; (O) ASB16-AS1 ROC analysis in hepatocellular carcinoma. (P) SNHG6 ROC analysis in hepatocellular carcinoma. ROC, receiver operating characteristic. P < 0.05 was considered as statistically significant.