Bioinformatic analysis of molecular characteristics and oncogenic features of CARD14 in human cancer

Abstract

Aberrant caspase recruitment domain family member 14 (CARD14) signaling has been strongly associated with inflammatory skin conditions. CARD14 acts as a scaffold protein, ultimately activating the transcription factor NF-KB. Although primarily studied in the context of inflammation, recent research has suggested its potential implications in tumorigenesis. In this study, we gathered The Cancer Genome Atlas (TCGA) tumor data to gauge the involvement of CARD14 in cancer, including genetic alterations, expression patterns, survival correlations, immune cell infiltration and functional interactions across diverse cancer types. We found heightened CARD14 expression in most tumors and there was a significant correlation between CARD14 expression and the prognosis of patients for certain tumors. For instance, patients with higher CARD14 expression had a better prognosis in sarcoma, lung, cervix and head and neck cancers. Moreover, CARD14 expression positively correlated with neutrophil infiltration in most of the cancer types analyzed. Finally, enrichment analysis showed that epithelial development and differentiation pathways were involved in the functional mechanism of CARD14. Our results show that CARD14 may have the potential to become a prognostic biomarker in several cancers, hence, further prospective studies will be required for its validation.

Keywords: CARD14; Cancer; Inflammation; Prognosis; TCGA.

Fig. 1

Analysis of CARD14 gene mutations in cBioPortal (TCGA PanCancer Atlas Studies). (A) Types of mutations and their frequencies in each cancer type. (B) Location of cancer mutations in the CARD14 gene sequence. (C) Survival analysis comparing DFS, DSS, OS and PFS in cancer patients with and without CARD14 gene alterations. The log-rank test was used to test the null hypothesis, we considered significance when, p < = 0.05. (D) Percentage of samples with mutations described in ClinVar. ACC: Adrenocortical carcinoma. BLCA: Bladder Urothelial Carcinoma. BRCA: Breast invasive carcinoma. CESC: Cervical squamous cell carcinoma and endocervical adenocarcinoma. CHOL: Cholangio carcinoma. COAD: Colon adenocarcinoma. DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma. ESCA: Esophageal carcinoma. GBM: Glioblastoma multiforme. HNSC: Head and Neck squamous cell carcinoma. KICH: Kidney Chromophobe. KIRC: Kidney renal clear cell carcinoma. KIRP: Kidney renal papillary cell carcinoma. LAML: Acute Myeloid Leukemia. LGG: Brain Lower Grade Glioma. LIHC: Liver hepatocellular carcinoma. LUAD: Lung adenocarcinoma. LUSC: Lung squamous cell carcinoma. MESO: Mesothelioma. OV: Ovarian serous cystadenocarcinoma. PAAD: Pancreatic adenocarcinoma. PCPG: Pheochromocytoma and Paraganglioma. PRAD: Prostate adenocarcinoma. READ: Rectum adenocarcinoma. SARC: Sarcoma. SKCM: Skin Cutaneous Melanoma. STAD: Stomach adenocarcinoma. TGCT: Testicular Germ Cell Tumors. THCA: Thyroid carcinoma. THYM: Thymoma. UCEC: Uterine Corpus Endometrial Carcinoma. UCS: Uterine Carcinosarcoma. UVM: Uveal Melanoma.

Fig. 2

CARD14 gene expression across cancers. (A) TIMER2.0 was used to plot CARD14 RNA levels in TCGA samples, statistical significance was calculated with the Wilcoxon test (*p < 0.05; **p < 0.01; ***p < 0.001). (B) Expression levels of the CARD14 gene in tumor and normal tissues from the TCGA and GTEx datasets, respectively, were analyzed with GEPIA2. The log2FC cutoff was 1, and the p value cutoff was 0.01. (C) Analysis of CARD14 RNA isoform expression in different cancer types with GEPIA2. (D) Methylation status of the CpG islands in the CARD14 promoter across various cancers analyzed with the SMART app. One-way ANOVA: ns, p > 0.05; *, p < = 0.05; **, p < = 0.01; ***, p < = 0.001; ****, p < = 0.0001.

Fig. 3

CARD14 protein expression in cancer. UALCAN CARD14 protein expression from the CPTAC database in (A) LUAD, LUSC, LIHC and HNSC. (B) CARD14 protein expression in female and male cancer patients. (C) CARD14 expression levels according to cancer stage in HNSC and LUAD patients. (D) CARD14 expression levels in cancer subtypes. Protein expression values downloaded from the CPTAC were log2 transformed. Then a Z-value for each sample for each protein was calculated as standard deviations from the median across samples. Welch’s T-test estimated the significance of differences in expression levels between normal and primary tumors or tumor subgroups. Anova was used for multiple comparisons. P value < 0.05 was considered to be statistically different.

Fig. 4

Survival analysis of patients stratified by the expression of CARD14 across cancers. Correlation between CARD14 expression and the survival outcomes of patients with diverse tumors, including both (A) overall survival (OS) and (B) disease-free survival (DFS), using the GEPIA2 website. The analysis employed the log-rank test with a significance level of P < 0.05 and a p value adjustment by FDR, and the cutoff was a median of 50%. Outlined squares indicate significance. (C) Kaplan‒Meier plots obtained from Kaplan‒Meier plotter comparing overall survival (OS) and (D) relapse-free survival (RFS) across different types of cancer. The p values were obtained from a log-rank test between two groups, a log rank p value < 0.05 was considered to be statistically significant.

Fig. 5

Correlation between CARD14 expression and immune cell infiltration. (A) TIMER2 correlation between the RNA expression of the CARD14 gene and immune cell type infiltration in each cancer type. (B) Correlation between CARD14 and neutrophils determined via deconvolution using the MCPCOUNTER algorithm. (C) TISIDB was used to analyze the correlation between CARD14 gene expression and immune cell infiltration or (D) chemokine expression in different cancers. P-values and partial correlation were obtained by Spearman’s rank correlation test after adjusting by purity. And all data were displayed as a heatmap and scatter plot. ns, p > 0.05; *, p < = 0.05; **, p < = 0.01; ***, p < = 0.001; ****, p < = 0.0001.

Fig. 6

Function of CARD14 and its associated proteins. (A) The interaction network of CARD14 and its partners was generated with STRING. Proteins are represented as nodes, and lines indicate associations based on known functional interactions in humans. The network was significantly enriched in interactions (PPI enrichment p value: 1.07e–16, FDR < 0.05). (B) Circle diagram of genes and their pathways; only enriched pathways from the String database with FDRs below 0.04 were used. (C) Functional enrichment analysis of Gene Ontology (GO) biological process (GO BP) terms with a false discovery rate (FDR) < 0.1 and a p value < 0.05. (D) Drug sensitivity of CARD14, MALT1 and BCL10 according to the GSCA Lite database. The bubble plot shows the correlation between gene expression and drugs. A positive Spearman correlation coefficient indicates that upregulated gene expression is associated with drug resistance whereas negative correlation indicates a higher gene expression may make drug sensitive.