Identification and validation of a gap junction protein related signature for predicting the prognosis of renal clear cell carcinoma

Abstract

Background: Gap junction proteins (GJPs) are a class of channel proteins that are closely related to cell communication and tumor development. The objective of this study was to screen out GJPs related prognostic signatures (GRPS) associated with clear cell renal cell carcinoma (ccRCC).

Materials and methods: GJPs microarray data for ccRCC patients were obtained from The Gene Expression Omnibus (GEO) database, along with RNA sequencing data for tumor and paired normal tissues from The Cancer Genome Atlas (TCGA) database. In the TCGA database, least absolute shrinkage and selection Operator (LASSO) and Cox regression models were used to identify GJPs with independent prognostic effects as GRPS in ccRCC patients. According to the GRPS expression and regression coefficient from the multivariate Cox regression model, the risk score (RS) of each ccRCC patient was calculated, to construct the RS prognostic model to predict survival. Overall survival (OS) and progression-free survival (PFS) analyses; gene pan-cancer analysis; single gene survival analysis; gene joint effect analysis; functional enrichment analysis; tumor microenvironment (TME) analysis; tumor mutational burden (TMB) analysis; and drug sensitivity analysis were used to explore the biological function, mechanism of action and clinical significance of GRPS in ccRCC. Further verification of the genetic signature was performed with data from the GEO database. Finally, the cytofunctional experiments were used to verify the biological significance of GRPS associated GJPs in ccRCC cell lines.

Results: GJA5 and GJB1, which are GRPS markers of ccRCC patients, were identified through LASSO and Cox regression models. Low expression of GJA5 and GJB1 is associated with poor patient prognosis. Patients with high-RS had significantly shorter OS and PFS than patients with low-RS (p< 0.001). The risk of death for individuals with high-RS was 1.695 times greater than that for those with low-RS (HR = 1.695, 95%CI= 1.439-1.996, p< 0.001). Receiver Operating Characteristic (ROC) curve showed the great predictive power of the RS prognostic model for the survival rate of patients. The area under curve (AUC) values for predicting 1-year, 3-year and 5-year survival rates were 0.740, 0.781 and 0.771, respectively. The clinical column chart was also reliable for predicting the survival rate of patients, with AUC values of 0.859, 0.846 and 0.796 for predicting 1-year, 3-year and 5-year survival, respectively. The GRPS was associated with immune cell infiltration, the TME, the TMB, and sensitivity to chemotherapy drugs. Further in vitro experiments showed that knockdown of GJA5 or GJB1 could promote the proliferation, migration and epithelial-mesenchymal transition (EMT) and inhibit apoptosis of ccRCC cells.

Conclusion: GJA5 and GJB1 could be potential biological markers for predicting survival in patients with ccRCC.

Keywords: biomarkers; cellular verification; clear cell renal cell carcinoma; gap junction protein; prognostic model.

Figure 1

Workflow chart.

Figure 2

LASSO regression model of OR-GJPs: (A) Coefficient distribution diagram. (B) Parameter change diagram.

Figure 3

(A) In the TCGA database, differential expression of GJA5 in tumor tissues and paired normal tissues. (B) In the TCGA database, differential expression of GJB1 in tumor tissues and paired normal tissues. (C) Differential expression of GJA5 in ccRCC in the GEO database. (D) Differential expression of GJB1 in ccRCC in the GEO database. (E) KM analysis of GJA5 in ccRCC in the TCGA database. (F) KM analysis of GJB1 in ccRCC in the TCGA database. (G) Analysis of the combined effect of GJA5 and GJB1 in ccRCC in the TCGA database.

Figure 4

(A) OS analysis between the high-RS score group and low-RS score group. (B) PFS analysis between the high-RS score group and low-RS score group. (C) RS prognosis model for TS (including the RS curve, survival time and survival status of patients, and GRPS gene expression). (D, E) ROC curve of the RS prognosis model of TS.

Figure 5

(A) OS analysis results in the training set. (B) PFS analysis results in the training set. (C) RS prognostic model in the training set. (D) ROC curve analysis of the RS prognostic model in the training set. (E) OS analysis results in the testing set. (F) PFS analysis results of testing set. (G) RS prognostic model of testing set. (H) ROC curve analysis of the RS Prognostic model in the testing Set.

Figure 6

(A) Clinical nomogram. (B) The calibration curve for predicting 1-year, 3-year and 5-year survival rates with a nomograph. (C) ROC curve and DCA curve of the nomogram.

Figure 7

(A) GO enrichment analysis. (B) KEGG enrichment analysis.

Figure 8

(A) Analysis of the difference in TMB between high-RS and low-RS groups. (B) Analysis of the Difference in Immune Score between high-RS and low-RS Groups. (C) Analysis of the Difference in Stromal Score between high-RS and low-RS Groups. (D) Differential expression of immune cells between high-RS and low-RS groups (Immune cell content in the low-RS group is represented in red, and that in the high-RS group is represented in blue. (*p< 0.05, **p< 0.01, ***p< 0.001).

Figure 9

Drug sensitivity analysis of common chemotherapeutic drugs in ccRCC risk groups: (A) Erlotinib. (B) Axitinib. (C) Afatinib. (D) Rapamycin. (E) Sorafenib.

Figure 10

(A) qRT-PCR results of GJA5 and GJB1 expression at the RNA level. (B) Western blot results of GJA5 and GJB1 expression at protein level. (C) qRT-PCR and Western blot results of GJA5 in 786-O cells after transfection with three small interfering RNAs. (D) qRT-PCR and Western blot results of GJB1 in A498 cells after transfection with three small interfering RNAs. (*p< 0.05, **p< 0.01, ***p< 0.001).

Figure 11

(A) The CCK8 experiment results of GJA5 and GJB1 knockdown on the cell proliferation in 786-O cells and A498 cells, respectively. (B) The EdU experiment results of GJA5 and GJB1 knockdown on the cell proliferation in 786-O cells and A498 cells, respectively (Error bar = 50 μm). (C) The cell scratch test results of GJA5 and GJB1 knockdown on the cell migration in 786-O cells and A498 cells, respectively (Error bar = 500 μm). (D) The Transwell experiment results of GJA5 and GJB1 knockdown on the cell migration potential in 786-O cells and A498 cells, respectively (Error bar = 100 μm). (*p < 0.05, **p < 0.001, ***p < 0.01).

Figure 12

(A) After knocking down GJA5, the protein expression of E-cad, N-cad, VIM, Bax, and Bcl-2 was detected in the 786-O cell line. (B) After knocking down GJB1, the protein expression of E-cad, N-cad, VIM, Bax, and Bcl-2 was detected in the A-498 cell line. (C) Flow cytometry analysis of cell apoptosis after knockdown of GJA5 and GJB1 genes in 786-O and A498 cells, respectively. (*p < 0.05, **p < 0.001, ***p < 0.01).