Discovery of CDK signature and CDK5 as potential biomarkers for predicting prognosis and immunotherapeutic response in gastric cancer peritoneal metastases

Abstract

Gastric cancer peritoneal metastases (GCPM) are a leading cause of death in gastric cancer patients. In this study, we focused on the expression of cyclin-dependent protein kinases (CDK), essential regulators of transcription, metabolism, and cell differentiation, in GCPM. Utilizing the GSE62254 cohort, we established a CDK signature (CDKS) model comprising ten CDK gene family members. Analysis of both the GSE62254 and TCGA cohorts revealed that patients with low CDKS had a worse prognosis compared to those with high CDKS. Furthermore, patients with high CDKS demonstrated positive responses from immunotherapy, as observed in the KIM cohort. We investigated the association between CDKS and the tumor microenvironment, including immune escape mechanisms. Immunohistochemistry analysis revealed a positive correlation between CDK5 and PD-L1 expression in gastric cancer. Furthermore, we found that CDK5 knockdown led to the inhibition of PD-L1 expression in gastric cancer cells. Our findings highlight the potential of CDKS as a prognostic biomarker and an indicator of immunotherapy response in gastric cancer patients. Moreover, our study suggests that targeting CDK5 could provide a new pathway for exploring immunotherapeutic research.

Keywords: CDK; CDK5; Gastric cancer; PD-L1; peritoneal metastases.

Figure 1

The landscape of genetic variation and correlation of the CDK gene family in gastric cancer peritoneal metastasis. A. Analyzing CDK family gene expression differences in gastric cancer tissues with/without peritoneal metastasis through the GSE62254 cohort; B. Investigating the associations between eight genes and relevant biological pathways; C. Performing Spearman’s correlation analysis to assess the relationship among ten genes by utilizing the GSE62254 database; D. Determining the chromosomal locations of the ten genes and analyzing their distribution; E. Identifying the mutation status of ten specific genes in gastric cancer cases by utilizing the TCGA database; F. Displaying the mutation landscape of ten genes in gastric cancer patients from the TCGA database through oncoplots with each vertical column representing an individual sample. ns, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001.

Figure 2

The correlation between CDKS, genomic alterations, and molecular subtypes in gastric cancer. (A, B) Displaying genomic alteration landscapes in (A) low and (B) high CDKS subtypes through oncoplots; (C) Comparing tumor mutation burden in low and high CDKS subtypes; (D) Investigating the correlation between CDKS and molecular subtypes of gastric cancer by analyzing the GSE62254 database; (E) Identifying the top 15 genes with the highest mutation frequency associated with CDKS using the TCGA database; (F) Analyzing the impact of PIK3CA mutations on the expression of immune checkpoints. **, P<0.01; ***, P<0.001.

Figure 3

Correlation between CDKS subtypes and clinicopathological features and prognosis in gastric cancer. A. Comparing various clinicopathological features of low CDKS and high CDKS subtypes in the GSE62254 cohort; B-D. Survival analysis that investigates differences in OS and DFS between low CDKS and high CDKS subtypes in both GSE62254 and TCGA cohort.

Figure 4

Correlation between the CDKS and the tumor microenvironment in gastric cancer. A. Box plots showing the correlation between CDKS subtypes and immune cell infiltration; B. CDKS subtypes possessing a significant association with stromal score; C. Spearman analysis indicates a correlation among CDKS and known gene signatures; D. GSEA analysis conducted on low and high CDKS subtypes. ns, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001.

Figure 5

Predicting responses to immune checkpoint blockade treatment. A. Assessing the correlation between CDKS and response to immunotherapy in the KIM cohort. CR (complete response), PD (progressive disease), PR (partial response), SD (stable disease); B. Comparing CDKS levels between PD/SD and PR/CR groups; C. Comparing the proportion of patients with different immunotherapy responses in two CDKS subtypes; D. Comparing CDKS levels between EBV positive and negative status; E. Analyzing the correlation between the expression of ten genes and CD274. ns, P>0.05; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001.

Figure 6

Correlation analysis of CDK5 and PD-L1 expression in gastric cancer. (A) Violin diagram displays the distribution of CDK5 expression in different cells from different databases; (B) Single-cell cluster map of CDK5 in different databases; (C) Violin diagram displays the distribution of PD-L1 expression in different cells from different databases; (D) Single-cell cluster map of PD-L1 in different databases; (E-G) Immunohistochemistry results for (E) CDK5 and (F) PD-L1 and (G) their correlation analysis.

Figure 7

Knockdown of CDK5 expression can inhibit PD-L1 expression. (A) Effects of IFN-γ induction on PD-L1 expression in HGC-27cells; (B) Effects of IFN-γ induction on PD-L1 expression in MKN74 cells; (C, D) The impact of CDK5 knockdown on the expression of PD-L1, c-MYC, IRF-1, p-STAT1, and STAT1 in (C) HCG-27 and (D) MKN74 cells.