The immune-related biomarker TEK inhibits the development of clear cell renal cell carcinoma (ccRCC) by regulating AKT phosphorylation

Abstract

Background: High immunogenicity is an important feature of ccRCC, but its underlying immune-related molecular mechanisms remain unclear. This study aimed to investigate the effect of immune-related gene TEK on ccRCC and its prognostic value.

Methods: The immune-related differentially expressed genes (DEGs) and transcription factors (TFs) in ccRCC were screened based on The Cancer Genome Atlas (TCGA) database, and a regulatory network of TF was constructed. Prognostic-related immune genes were screened by univariate Cox regression analysis and functional annotation was performed. Univariate and multivariate Cox regression analyses were performed to construct the immune gene risk model and identify the hub gene TEK that independently affected the prognosis of ccRCC. The effectiveness of the TEK was verified by external microarray datasets. The relationship between TEK and immune cells in ccRCC was evaluated based on Tumor Immune Estimation Resource (TIMER). The expression of TEK in clinical specimens was verified by qRT-PCR and immunohistochemical (IHC) staining. MTT and cloning formation assay were used to evaluate cell proliferation. Transwell assays were used to assess cell migration. Apoptosis was assessed by flow cytometry, and the expression of related proteins was detected by Western blot and immunofluorescence.

Results: We constructed a prognostic model consisting of 12 hub genes and performed risk scores to determine the relationship between these scores and prognosis. Through Cox regression analysis and survival analysis, TEK, an immune marker highly related to survival prognosis, was obtained and validated. In vitro experiments showed that knockdown of TEK promoted the proliferation and migration of ccRCC cells, and we found that TEK promoted apoptosis by regulating the phosphorylation of AKT, thereby inhibiting cell proliferation.

Conclusions: TEK plays an important role in risk assessment and survival prediction for ccRCC patients as a new immune gene and maybe an emerging target for immunotherapy for ccRCC patients.

Keywords: Clear cell renal cell carcinoma; Survival prognosis; TEK; Tumor microenvironment; Tumor‐infiltrating immune cells.

Fig. 1

Screening of ccRCC immune-related DEGs and construction of TF regulatory network. a The volcano plot visualized 1817 immune-related genes based on the TCGA database. Red indicated high expression and green indicated low expression. b Using Venn algorithm to obtain 681 immune-related DEGs in ccRCC. c The volcano plot of 318 TFs based on the TCGA database. Red indicated high expression and green indicated low expression. d A TF regulatory network was constructed using differentially expressed TFs. Blue triangles represented TFs, red circles represented positively regulated genes, and green circles represented negatively regulated genes

Fig. 2

Functional annotation of prognosis-related immune genes. a Performed GO analysis on immune genes related to prognosis. b KEGG pathway analysis of immune genes related to prognosis. c The x-axis represented the z-score, the y-axis represented the negative logarithm of the P-value, and the size of the circle was proportional to the number of genes. Green circles correspond to the biological process and red indicated the molecular function. d The outer circle showed a scatter plot for each term of the logFC of the assigned genes. Red displayed increase, and blue displayed decrease. e The heatmap showed the correlation between prognostic immune genes and pathways

Fig. 3

Construction of immune genes risk model. a Multivariate analysis of the 12 immune genes that made up the risk model. b The risk index distribution of ccRCC patients in the training data set. c The survival status chart of ccRCC patients is based on the TCGA cohort. d The heatmap of 12 hub immune genes is based on the TCGA cohort. e Risk model survival curve analysis. f ROC curve to verify the ability of the risk model to predict prognosis

Fig. 4

Screening and validation of hub genes. a, b Univariate and multivariate analysis of overall survival rate of ccRCC patients. c–h Tissue expression difference analysis and survival analysis of three hub genes based on the TCGA cohort. i TEK mRNA expression difference was analyzed based on GSE53757 dataset. j Correlation analysis between TEK and pathological stages (based on GSE53757 dataset). k, l Analyze the differential expression of TEK in tumor and adjacent tissues by qPCR and IHC experiments

Fig. 5

Immune infiltration of TEK.  a Correlation analysis between TEK and immune infiltration level of ccRCC was performed. b Based on the Timer database, the correlation between TEK and ccRCC immune infiltration level was shown. The generation of scatterplot had partial Spearman correlation and statistical significance

Fig. 6

TEK knockdown promoted the proliferation and migration of ccRCC cells. a, b Verification of TEK-siRNA silencing efficacy on the mRNA and protein levels of ACHN cells and Caki-1 cells. c MTT assay detected the effect of TEK silencing on cell proliferation. d Immunofluorescence staining of Ki-67. e The cloning formation assay evaluated the effect of silencing TEK on cloning formation ability. f Statistical analysis of cloning formation assay. g The effect of TEK silencing on cell migration was evaluated by transwell assay. h Statistical analysis of transwell assay

Fig. 7

TEK could affect the EMT and apoptosis of ccRCC cells by regulating AKT phosphorylation. a Flow cytometry analyzed the effect of TEK knockdown on ccRCC cell apoptosis. b Statistical analysis of Flow cytometry analysis. c Analysis of mRNA levels of apoptosis-related genes in ccRCC cells after TEK knockdown. d Western blot analysis detected EMT-related proteins in TEK knockdown ccRCC cells. e Western blot analysis was performed to detect apoptosis-related proteins and phosphorylated AKT in TEK knockdown ccRCC cells