Identifying and validating the roles of the cuproptosis-related gene DKC1 in cancer with a focus on esophageal carcinoma

Abstract

Background: Esophageal cancer is a common malignancy of the digestive tract. Despite remarkable advancements in its treatment, the overall prognosis for patients remains poor. Cuproptosis is a form of programmed cell death that affects the malignant progression of tumors. This study aimed to examine the impact of the cuproptosis-associated gene DKC1 on the malignant progression of esophageal cancer.

Methods: Clinical and RNA sequencing data of patients with esophageal cancer were extracted from The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis was used to identify the differentially expressed genes related to cuproptosis that are associated with prognosis. We then validated the difference in the expression of DKC1 between tumor and normal tissues via three-dimensional multiomics difference analysis. Subsequently, we investigated the association between DKC1 expression and the tumor microenvironment by employing the TIMER2.0 algorithm, which was further validated in 96 single-cell datasets obtained from the TISCH database. Additionally, the functional role of DKC1 in pancarcinoma was assessed through GSEA. Furthermore, a comprehensive pancancer survival map was constructed, and the expression of DKC1 was verified in various molecular subtypes. By utilizing the CellMiner, GDSC, and CTRP databases, we successfully established a connection between DKC1 and drug sensitivity. Finally, the involvement of DKC1 in the progression of esophageal cancer was investigated through in vivo and in vitro experiments.

Results: In this study, we identified a copper death-related gene, DKC1, in esophageal cancer. Furthermore, we observed varying levels of DKC1 expression across different tumor types. Additionally, we conducted an analysis to determine the correlation between DKC1 expression and clinical features, revealing its association with common cell cycle pathways and multiple metabolic pathways. Notably, high DKC1 expression was found to indicate poor prognosis in patients with various tumors and to influence drug sensitivity. Moreover, our investigation revealed significant associations between DKC1 expression and the expression of molecules involved in immune regulation and infiltration of lymphocyte subtypes. Ultimately, the increased expression of DKC1 in esophageal cancer tissues was verified using clinical tissue samples. Furthermore, DKC1-mediated promotion of esophageal cancer cell proliferation and migration was confirmed through both in vitro and in vivo experiments. Additionally, it is plausible that DKC1 may play a role in the regulation of cuproptosis.

Conclusion: In this study, we conducted a systematic analysis of DKC1 and its regulatory factors and experimentally validated its excellent diagnostic and prognostic abilities in various cancers. Further research indicated that DKC1 may reshape the tumor microenvironment (TME), highlighting the potential of DKC1-based cancer treatment and its usefulness in predicting the response to chemotherapy.

Keywords: DKC1; Diagnosis; Esophageal carcinoma; Prognosis; Tumor microenvironment.

Fig. 1

Identification of differentially expressed cuproptosis-related mRNAs (crmRNAs) with prognostic value in ESCA. A Sankey plot displaying the network of coexpressed CRGs and crmRNAs (R² > 0.4 and P < 0.05). B Volcano plot showing the differential crmRNAs among normal and tumor samples, with red representing upregulated crmRNAs. C Forest plot showing the prognostic crmRNAs in ESCA. D Relationship between DKC1 expression and prognosis in ESCA patients

Fig. 2

The expression landscape of DKC1. A The expression levels of DKC1 in various tumor tissues and their corresponding normal tissues. B Expression and distribution of DKC1s in various organs. C Y-axis representing DKC1 mRNA expression in the TCGA cohort. Boxplots show the median, quartiles, min, and max, with each point representing one sample. p values are based on the Wilcoxon test. D Similar to C but in paired samples grouped by cancer from the TCGA. Each point represents one sample. (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001). E Logistic regression analysis of both TCGA and TCGA-GTEx data. Red indicates that the OR is greater than 1, and blue indicates that the OR is between 0 and 1. F Staining of DKC1 in tumor tissues based on the HPA. G Analysis of the protein level of DKC1 in the CPTAC mass spectrometry database

Fig. 3

DKC1 is highly expressed in ESCA. A qPCR detection of DKC1 transcript expression levels in ESCA tumor (n = 9) and adjacent normal (n = 8) tissues. B Protein expression levels were compared between ESCA tumor(n = 27) and adjacent normal (n = 29) tissues. C Representative images of DKC1 IHC staining in ESCA tumor and adjacent normal tissues. All data were analyzed with the unpaired t-test. **P < 0.01, ***P < 0.001. Error bars represent the mean ± SD

Fig. 4

Genetic alterations of DKC1 in cancers. A Pancancer sites and numbers of patients with DKC1 genetic alterations according to cBioPortal. B The structure of DKC1 mutation sites. C Frequency of DKC1 mutations in different tumor types. D Relationship between DKC1 mRNA expression and genetic alterations. E Spearman’s correlation between somatic copy number alterations and DKC1 expression. F Heatmap showing the differential methylation of DKC1 in cancers; hypermethylated and hypomethylated DKC1 are marked in red and blue, respectively (Wilcoxon rank-sum test). G Spearman’s correlation of DKC1 transcription and promoter methylation. Red and blue represent positive and negative correlations, respectively

Fig. 5

Pathway and functional mechanism analysis. A The enrichment results of KEGG analysis. B DKC1 mRNA expression was strongly correlated with 14 malignant features of all tumors. Cell cycle and DNA repair scores were generally positively correlated with DKC1 mRNA expression. C Differences in the enrichment of DKC1 in 50 HALLMARK and 74 metabolic gene sets. NES is the normalized enrichment score in the GSEA algorithm. D, E Correlation scatter plot of the cell cycle distribution and DNA repair

Fig. 6

Association of DKC1 expression with immune infiltration. A Heatmap showing correlations between DKC1 mRNA expression and the expression of chemokines, chemokine receptors, immune inhibitors, immune stimulants, and major histocompatibility complex (MHC) genes. B The bubble map shows the difference in the TIP score. The size represents significance, the depth of color represents logfc, blue represents a decrease, and red represents an increase. C Seven software programs were used to evaluate the correlation between DKC1 expression and cancer immune infiltration. D Pancancer cell sources of DKC1 at the single-cell level. The heatmap shows the distributions of DKC1 expression in different cells among 24 types of cancer

Fig. 7

Analysis of clinical variables and molecular subtypes. A The chi-square test confirmed that there were more patients with the C1 and C2 subtypes in the DKC1 high-expression group and more patients with the C3 subtype in the DKC1 low-expression group. B Expression levels of DKC1 in different immune subtypes. The Kruskal test detected differences between the 6 immune subtypes. C The Kruskal‒Wallis test was used to examine differences in DKC1 expression among molecular subtypes

Fig. 8

Pancancer survival landscape. A Association between DKC1 expression and various clinical outcomes, including OS, DSS, DFI, and PFI. Red indicates a higher risk, while green represents a protective factor. B–E The forest plot shows the prognostic significance of DKC1 in various cancer types according to univariate Cox regression analysis. F Kaplan‒Meier survival analysis and log-rank tests were performed using the “survival” and “survminer” packages

Fig. 9

Drug resistance analysis. Drug sensitivity analysis based on DKC1 expression using three different databases: CellMiner (A), CTRP (B), and GDSC (C). A value of P < 0.05 indicated statistical significance in the analysis. D Prediction of potential compounds targeting DKC1. The candidate compounds were visualized based on connectivity map analysis of 32 cancer types that could target DKC1

Fig. 10

Experimental verification in ESCA in vivo and in vitro. A Western blot and qPCR were used to detect the expression of DKC1 protein and mRNA after knocking down DKC1. B Wound healing rates were compared between the negative control group and the ShDKC1 group. C Cell proliferation was measured by a colony formation assay. D Cell migration was measured by a wound healing assay. E Cell migration was measured by a transwell assay. F Noninvasive imaging of ESCA in live mice, tumor volumes of mice, photographs of tumors, and IHC and HE staining of tumor tissues, *P < 0.05, ***P < 0.001. G The correlation between DKC1 expression and cell death and between DKC1 expression and immunofluorescence staining following 100 nM Elesclomol-Cu (1:1 ratio)-induced cuproptosis. (green: GFP; blue: DAPI; red: DLAT)