Prognostic Value and Potential Regulatory Mechanism of H19 in Stomach Adenocarcinoma

Abstract

The first lncRNA discovered, H19, has been found to participate in the regulation of diverse biological processes, including the pathogenesis of stomach adenocarcinoma. In addition to its oncogenic function in tumor formation, a high level of H19 in tumor tissues has also been reported to be an indicator for poor prognosis. However, although many previous works have investigated the level of H19 as an independent indicator for prognosis, the real value of H19 in predicting survival has rarely been evaluated. In this study, we established a prognostic model and nomogram for stomach adenocarcinoma by combining the expression level of H19 with traditional indices, which showed the value of H19 in predicting the survival rates of patients. In addition, we investigated the mechanism underlying the correlation of the H19 level in cancer tissue with poor prognosis in patients. Our results showed that H19 could function as ceRNA by sponging five miRNAs, which may promote the progression of cancer.

Figure 1

The expression of H19 in STAD and its correlation with prognosis. (a)The volcano plot of DELs between STAD and normal tissues. (b) Normalized transcript counts of significantly differentially expressed lncRNAs between STAD and normal tissues. (c) The expression level of lncRNA H19 in 379 STAD tissues and 26 normal tissues based on the TCGA database analysis. (d) The expression level of lncRNA H19 in 379 STAD tissues from the TCGA database and 174 normal tissues from the GTEx database. (e)The expression status of lncRNA H19 in different cancers and specific cancer subtypes analyzed with TIMER2. (f) The significant correlations of lncRNA H19 expression with outcomes across various cancer types visualized in the heatmap, which shows the normalized coefficient of lncRNA H19 in the Cox model. (g-h). The Kaplan–Meier curves for the DSS (c) and PD (d) of STAD patients stratified by the H19 expression level.

Figure 2

The prognostic value of H19 in STAD. (a) Prognostic nomogram for patients with STAD based on the H19 expression level. (b)Verification of the nomogram by time-dependent ROC curve analysis. (c) Calibration curves for predicting patient survival at each time point. (d–f) DCA curves showing the benefit gained from incorporation of H19 in predicting 1 (d), 2 (e), and 4 (f) year survival outcomes.

Figure 3

Differential expression analysis of samples with high and low H19 expression. (a) The volcano plot of DEGs between the H19-high and H19-low groups. (b–e) KEGG/GO enrichment analyses performed based on the 596 upregulated DEGs (b) and 173 downregulated DEGs (d); MF, BP, CC, and KEGG pathway analyses conducted and the corresponding networks based on the KEGG and GO analysis results constructed (c and e). (f) GSEA showing different gene expression patterns between the H19-high and H19-low groups.

Figure 4

H19 promoted migration, invasion, and survival of STAD cells by binding to five miRNAs. (a) Bioinformatics analysis of 9 miRNAs potentially binding to lncRNA H19. (b) RNA pulldown assay verified the binding of 5 of the 9 miRNAs to lncRNA H19 (n = 3). (c) Relative luciferase activity determined to verify the binding of 5 miRNAs to H19 (n = 3). (d) Scratch assay showing the migration of MKN-45 cells with H19 knockdown or miRNA inhibition (n = 3). (e) Quantitative analysis of the wound closure rate (n = 3). (f) Transwell assays showing the invasion of MKN-45 cells after H19 knockdown or miRNA inhibition (n = 3). (g) Quantitative analysis of the number of migrated cells (n = 3). (h) Flow cytometric analysis of apoptosis induced by gemcitabine in MKN-45 cells transfected with H19 siRNA (n = 3). (i) Quantitative analysis of the percentage of apoptotic cells (n = 3). (j-n) Analysis of potential downstream genes regulated by five miRNAs binding to H19.