Pan-cancer analysis of the prognostic and immunological role of GJB2: a potential target for survival and immunotherapy

Abstract

Background: GJB2 plays an essential role in the growth and progression of several cancers. However, asystematic pan-cancer analysis of GJB2 is lacking. Therefore, in this study, we performed a comprehensive pan-cancer analysis to determine the potential role of GJB2 in prognostic prediction and cancer immunotherapy response.

Methods: The differential expression of GJB2 in the tumor and adjacent normal tissues of various cancer types was analyzed using the TIMER, GEPIA, and Sangerbox databases. GEPIA and Kaplan-Meier plotter databases were used to analyze the survival outcomes based on GJB2 expression levels in pan-cancer. Furthermore, the association of GJB2 expression with the immune checkpoint (ICP) genes, tumor mutational load (TMB), microsatellite instability (MSI), neoantigens, and tumor infiltration of immune cells was analyzed using via the Sangerbox database. The cBioPortal database was used to determine the characteristics of GJB2 gene alterations in the cancer tissues. The STRING database was used to identify the GJB2-binding proteins. GEPIA database was used to identify the GJB2 co-expressed genes. DAVID was used to perform the functional enrichment analysis of gene ontology (GO) terms and KEGG pathways associated with GJB2. Finally, the mechanistic role of GJB2 in pancreatic adenocarcinoma (PAAD) was analyzed using the LinkedOmics database.

Results: The GJB2 gene was highly expressed in a variety of tumors. Furthermore, GJB2 expression levels showed significant positive or negative association with the survival outcomes in various cancers. GJB2 expression levels cor related with tumor mutational burden, microsatellite instability, neoantigens, and tumor infiltration of immune cells in multiple cancers. This suggested that GJB2 played a critical role in the tumor microenvironment. Functional enrichment analysis showed that the biological role of GJB2 in tumors included modulation of gap junction-mediated intercellular transport, regulation of cell communication by electrical coupling, ion transmembrane transport, autocrine signaling, apoptotic signaling pathway, NOD-like receptor signaling pathway, p53 signaling pathway, and PI3K-Akt signaling pathway.

Conclusions: Our study demonstrated that GJB2 played a significant role in tumorigenesis and tumor immunity in multiple cancers. Furthermore, GJB2 is a potential prognostic biomarker and a promising therapeutic target in multiple types of cancers.

Keywords: ICP; MSI; TMB; immune infiltration; pan-cancer; prognosis.

Figure 1

GJB2 gene expression in different cancers. (A) GJB2 expression levels in the tumor and normal tissues from the TCGA pan-cancer datasets using the TIMER2.0 database. (B) GJB2 expression levels in the paired tumor/normal samples of the pan-cancer datasets from the TCGA and GTEx databases. *P < 0.05, **P < 0.005, ***P < 0.001.

Figure 2

GJB2 protein expression levels in the normal and tumor tissues of the colon, cervical, kidney, breast, lung, and stomach cancer datasets from the HPA database. *P < 0.05.

Figure 3

Survival outcomes of cancer patients based on high and low GJB2 expression in the pan-cancer datasets using the GEPIA2 tool. (A) The analysis of overall survival (OS) based on the level of GJB2 gene expression in the TCGA pan-cancer datasets. (B) The analysis of disease-specific survival (DSS) based on the level of GJB2 gene expression in the TCGA pan-cancer datasets.

Figure 4

Kaplan–Meier survival curve analysis of pan-cancers based on GJB2 gene expression levels. (A–K) The analysis of overall survival (OS) based on the level of GJB2 gene expression in the TCGA pan-cancer datasets; (L–S) The analysis of relapse-free survival (RFS) based on the level of GJB2 gene expression in the TCGA pan-cancer datasets.

Figure 5

Correlation analysis of GJB2 expression with clinical stages and grades of various tumors. (A) GJB2 expression levels show significant correlation with different clinical stages in patients witxh LUAD, COAD, STES, KIPAN, KIRC, PAAD, and TGCT. (B) GJB2 expression levels demonstrate significant association with the grade of CESC, ESCA, STES, KIPAN, HNSC, KIRC, LIHC, and PAAD. ^∗ P < 0.05, ^∗∗ P < 0.01, ^∗∗∗ P < 0.001, ****P < 0.0001, and - P≥0.05.

Figure 6

Correlation analysis between GJB2 expression levels and se in the pan-cancer datasets. As shown, GJB2 expression significantly correlates with sex in STES, KIRP, HNSC, KIRC, and READ. *P < 0.05, **P <0.01, and ****P < 0.0001.

Figure 7

Correlation analysis between GJB2 expression levels and age in the pan-cancer datasets. As shown, GJB2 expression positively correlates with age in GBMLGG, KIRP, KIPAN, KIRC, THYM, and KICH, and negatively correlates with age in ESCA, STES, and TGCT. Note: The different color codes indicate the size of different p-values; the direction and length of the vertical axis indicates positive or negative correlation between GJB2 and age; Cor represents correlation efficient; size of the circles indicates sample size.

Figure 8

Mutational features of the GJB2 gene in various cancers. (A) The mutational frequency and mutation type of the GJB2 gene in various cancers. (B) The mutation counts of the GJB2 gene in various cancer types from the TCGA database. The mutational types are represented by differentially colored. (C) 3D structure of GJB2 with L36F, which represents the site with the highest mutational frequency among all cancers. (D) General mutation count of the GJB2 gene in various cancer types from the TCGA database.

Figure 9

Association between GJB2 expression and tumor immunity biomarkers. (A) The relationship between GJB2 expression and immune checkpoint (ICP) genes [inhibitory (24) and stimulatory (36)] in pan-cancer. Each small rectangular module represents co-expression of immune-related genes and GJB2 in various cancers; color in the upper left corner represents the correlation coefficient (Cor); the asterisk and color in the lower right corner represents the P value. (B–D) The relationship of GJB2 expression levels with (B) TMB, (C) MSI, and (D) neoantigens. The different colors represent the P-value. The horizontal axis represents the positive/negative correlation, including the magnitude of the correlation between GJB2 expression and age in pan-cancer. (B) GJB2 expression shows significant positive association with TMB in LUAD, COAD, COADREAD, KIRP, KIPAN, and STAD; GJB2 expression shows significant negative association with TMB in PRAD, HNSC, and THCA. (C) GJB2 expression shows positive correlation with MSI in COADREAD and STAD; GJB2 expression shows negative correlation with MSI in GBMLGG, KIPAN, PRAD, HNSC, and THCA. (D) GJB2 expression shows positive correlation with neoantigens in HNSC. ^∗ P < 0.05, ^∗∗ P < 0.01, and ^∗∗∗ P < 0.001.

Figure 10

Correlation analysis between GJB2 expression and ESTIMATE scores in pan-cancer using the ESTIMATE algorithm.

Figure 11

MCPCOUNTER analysis results show significant correlation between GJB2 expression levels and the infiltration levels of various immune cells. *P <0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Figure 12

IPS analysis results show significant correlation between GJB2 expression levels and the infiltration levels of various immune cells. *P < 0.05, **P <0.01, ***P < 0.001, and ****P < 0.0001.

Figure 13

EPIC analysis results show significant correlation between the GJB2 expression levels and the infiltration levels of various immune cells. *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001.

Figure 14

Functional enrichment analysis of GJB2-associated genes. (A) STRING database analysis shows identification of 50 potential GJB2-binding proteins. (B) GEPIA2 analysis shows identification of 50 GJB2-related genes from the TCGA database. GJB2 shows significant correlation with GJB6, KRT6A, KRT6B, KRT14, and IVL. (C) Heatmap shows the expression of GJB2-correlated genes in various cancer types. (D) Venn diagram shows the intersection between GJB2-binding genes and GJB2-related genes. (E–G) GO enrichment analysis results of the GJB2-binding and GJB2-related genes. (H) KEGG pathway analysis results of the GJB2-binding and GJB2-related genes.

Figure 15

LinkedOmics database analysis of GJB2 co-expression genes in PAAD. (A) Pearson’s correlation test results show genes with significantly high correlation with GJB2 in the PAAD cohort. (B, C) Heatmaps of the top 50 genes that show (B) positive and (C) negative correlations with GJB2 in the PAAD cohort. (D) Directed no-loop plots for the GO analysis of GJB2-related genes in the PAAD cohort. (E) Volcano map shows the KEGG pathway analysis of GJB2-related genes in the PAAD cohort.