Identification of the KCNQ1OT1/ miR-378a-3p/ RBMS1 Axis as a Novel Prognostic Biomarker Associated With Immune Cell Infiltration in Gastric Cancer

Abstract

Background: Gastric cancer (GC) is the second leading cause of cancer-related mortality and the fifth most common cancer worldwide. However, the underlying mechanisms of competitive endogenous RNAs (ceRNAs) in GC are unclear. This study aimed to construct a ceRNA regulation network in correlation with prognosis and explore a prognostic model associated with GC. Methods: In this study, 1,040 cases of GC were obtained from TCGA and GEO datasets. To identify potential prognostic signature associated with GC, Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression were employed. The prognostic value of the signature was validated in the GEO84437 training set, GEO84437 test set, GEO15459 set, and TCGA-STAD. Based on the public databases, TargetScan and starBase, an mRNA-miRNA-lncRNA regulatory network was constructed, and hub genes were identified using the CytoHubba plugin. Furthermore, the clinical outcomes, immune cell infiltration, genetic variants, methylation, and somatic copy number alteration (sCNA) associated with the ceRNA network were derived using bioinformatics methods. Results: A total of 234 prognostic genes were identified. GO and GSEA revealed that the biological pathways and modules related to immune response and fibroblasts were considerably enriched in GC. A nomogram was generated to provide accurate prognostic outcomes and individualized risk estimates, which were validated in the training, test dataset, and two independent validation datasets. Thereafter, an mRNA-miRNA-lncRNA regulatory network containing 4 mRNAs, 22 miRNAs, 201 lncRNAs was constructed. The KCNQ1OT1/hsa-miR-378a-3p/RBMS1 ceRNA network associated with the prognosis was obtained by hub gene analysis and correlation analysis. Importantly, we found that the KCNQ1OT1/miR-378a-3p/RBMS1 axis may play a vital role in the diagnosis and prognosis of GC patients based on Cox regression analyses. Furthermore, our findings demonstrated that mutations and sCNA of the KCNQ1OT1/miR-378a-3p/RBMS1 axis were associated with increased immune infiltration, while the abnormal upregulation of the axis was primarily a result of hypomethylation. Conclusion: Our findings suggest that the KCNQ1OT1/miR-378a-3p/RBMS1 axis may be a potential prognostic biomarker and therapeutic target for GC. Moreover, such findings provide insights into the molecular mechanisms of GC pathogenesis.

Keywords: SCNA; ceRNA; genetic variants; immune microenvironment; methylation.

FIGURE 1

Flowchart of the study design, including data collection and analysis.

FIGURE 2

The results of differential expression analysis. (A) Heat map of the top 50 differentially expressed genes. (B) Venn diagram showing the intersection of TCGA-STAD and GSE84437. (C) Volcano plot showing the results of differentially expressed genes. (D) GO and KEGG enrichment of differentially expressed genes.

FIGURE 3

Identification and analysis of prognostic genes. (A) GSEA of GC patients and non-GC patients. (B, C) LASSO regression analysis of 234 candidate genes. (D) Heat map showing the risk scores of the high and low-risk groups for the four prognostic genes in all three datasets. (E) Risk scores for the high and low-risk groups for four prognostic genes in the GSE84437 dataset. (F) Risk scores for the high and low-risk groups for four prognostic genes in the GSE15459 dataset. (G) Risk scores for the high and low-risk groups for four prognostic genes in the TCGA dataset.

FIGURE 4

Identification and application of the multigene signature. (A) Nomogram predicting the probability of survival for GC patients at 1, 3 and 5 years (B–E) Kaplan-Meier curves of the training and validation datasets on overall survival. (F–I) Time-dependent ROC curves of nomogram in the training and validation datasets. (J) Calibration curve of predicted results compared to actual observation results. (K) DCA demonstrating the net benefit of nomogram, clinical features model, and multigene signature.

FIGURE 5

Correlation analysis between four prognostic genes. (A–F) Scatter plots showing the correlation between four prognostic genes. (G,H) Chord diagram and heatmap visualizing the correlation between four prognostic genes. (I) Immunohistochemical analysis of four prognostic genes.

FIGURE 6

Construction of the mRNA-miRNA-lncRNA regulatory network. (A) Construction of the ceRNA network associated with GC. (B) Sankey diagram for ceRNA network visualization. (C) Identification of the hub network for further analysis. (D) Box plots showing the expression levels of miRNAs and lncRNAs in hub genes. (E–L) Kaplan-Meier curves showing the overall survival based on the top hub genes.

FIGURE 7

Identification of the KCNQ1OT1/miR-378a-3p/RBMS1 axis. (A–D) Scatter plots showing miRNA-target co-expression of hub genes associated with prognosis. (E) Binding site prediction for the KCNQ1OT1/miR-378a-3p/RBMS1 axis. (F–H) Correlation analysis of the KCNQ1OT1/miR-378a-3p/RBMS1 axis with infiltrating immune cells.

FIGURE 8

Association between immune cell infiltration and genomic data of the KCNQ1OT1/RBMS1 axis. (A–C) Association between immune cell infiltration and RBMS1 mutation. (D–K) Relationship between immune cell infiltration and sCNA of RBMS1. (L) Relationship between immune cell infiltration and sCNA of KCNQ1OT1.

FIGURE 9

Correlation between methylation and the KCNQ1OT1/RBMS1 axis. (A,B) Methylation levels and protein expression of RBMS1 in GC and normal tissues. (C) Methylation levels of KCNQ1OT1 in GC and normal tissues. (D–L) Correlation between methylation sites and the KCNQ1OT1/RBMS1 axis.