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. 2022 Jan 19;2(1):33-49.
doi: 10.1007/s43657-021-00034-x. eCollection 2022 Feb.

Pan-cancer analysis identifies RNA helicase DDX1 as a prognostic marker

Baocai Gao  1 , Xiangnan Li  1 , Shujie Li  2 , Sen Wang  1 , Jiaxue Wu  1 , Jixi Li  1
Affiliations

Pan-cancer analysis identifies RNA helicase DDX1 as a prognostic marker

Baocai Gao et al. Phenomics. .

Abstract

The DEAD-box RNA helicase (DDX) family plays a critical role in the growth and development of multiple organisms. DDX1 is involved in mRNA/rRNA processing and mature, virus replication and transcription, hormone metabolism, tumorigenesis, and tumor development. However, how DDX1 functions in various cancers remains unclear. Here, we explored the potential oncogenic roles of DDX1 across 33 tumors with The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. DDX1 is highly expressed in breast cancer (BRCA), cholangiocarcinoma (CHOL), and colon adenocarcinoma (COAD), but it is lowly expressed in renal cancers, including kidney renal clear cell carcinoma (KIRC), kidney chromophobe (KICH), and kidney renal papillary cell carcinoma (KIRP). Low expression of DDX1 in KIRC is correlated with a good prognosis of overall survival (OS) and disease-free survival (DFS). Highly expressed DDX1 is linked to a poor prognosis of OS for adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), KICH, and liver hepatocellular carcinoma (LIHC). Also, the residue Ser481 of DDX1 had an enhanced phosphorylation level in BRCA and ovarian cancer (OV) but decreased in KIRC. Immune infiltration analysis exhibited that DDX1 expression affected CD8+ T cells, and it was significantly associated with MSI (microsatellite instability), TMB (tumor mutational burden), and ICT (immune checkpoint blockade therapy) in tumors. In addition, the depletion of DDX1 dramatically affected the cell viability of human tumor-derived cell lines. DDX1 could affect the DNA repair pathway and the RNA transport/DNA replication processes during tumorigenesis by analyzing the CancerSEA database. Thus, our pan-cancer analysis revealed that DDX1 had complicated impacts on different cancers and might act as a prognostic marker for cancers such as renal cancer.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-021-00034-x.

Keywords: DDX1; Pan-cancer analysis; Prognostic marker; RNA helicase; Survival analysis.

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Conflict of interest statement

Conflicts of interestThis study is based on open-source data, and there are no ethical issues and other conflicts of interest. The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
The expression level of the DDX1 gene in different tumors. a The expression status of the DDX1 gene in different cancers or specific cancer subtypes was analyzed through the TIMER2. *p < 0.05; **p < 0.01; ***p < 0.001. b The tumors DLBC, GBM, LGG, SKCM, TGCT, and THYM in the TCGA and the corresponding normal tissues in the GTEx database were compared, respectively. The box plot data were supplied. *p < 0.05. c The expression levels of the DDX1 gene in KIRC, LIHC, and UCS were analyzed by the main pathological stages (stages I, II, III, and IV). Log2 (TPM + 1) was applied for log-scale. d The expression levels of the DDX1 gene in KICH, KIRP, KIRC, HNSC, LIHC, and LUAD on different cancer stages were analyzed through the UALCAN. KICH: Normal-vs-Stage I/II/III, p < 0.001; KIRP: Normal-vs-Stage I/II/III/IV, p < 0.001; KIRC: Normal-vs-Stage I/II/III/IV, p < 0.001; HNSC: Normal-vs-Stage I/II/III/IV, p = 0.002, Normal-vs-Stage II/III/IV, p < 0.001; LIHC: Normal-vs-Stage I/II/III, p < 0.001; LUAD: Normal-vs-Stage I/II/III/IV, p < 0.001
Fig. 2
Fig. 2
The expression levels of DDX1 protein in different tumors. a The expression levels of the DDX1 protein were analyzed by the CPTAC dataset between normal tissue and primary tissue of BRCA, OV, LUAD, KIRC, and UCEC. b Antibodies (HPA034502, HPA034503) were labeled with DAB (3,3'-diaminobenzidine). The section was furthermore counterstained with hematoxylin to enable visualization of microscopical features for renal cancer and breast cancer
Fig. 3
Fig. 3
Correlation between DDX1 gene expression and survival prognosis of different cancers. The GEPIA2 tool was used to perform OS a and DFS b analyses of different tumors in TCGA. The survival map and the Kaplan–Meier curves were shown, respectively
Fig. 4
Fig. 4
Mutation feature of DDX1 in different tumors in TCGA. The alteration frequency with mutation type a and mutation site b were displayed, respectively. Each color denoted a mutation type. Mutation (green): point mutation. c The potential correlation between the mutation status of DDX1 and overall survival, disease-free survival, progression-free survival, and disease-specific survival of different cancers were analyzed using the cBioPortal tool
Fig. 5
Fig. 5
The effects of DDX1 methylation on tumor occurrence and prognosis. a Boxplots of expression levels for DDX1 in LUAD. b The Beta value indicated DNA methylation level ranging from 0 (unmethylated) to 1 (fully methylated). Different beta value cut-off was considered to indicate the hyper-methylation (Beta-value: 0.7–0.5) or the hypo-methylation (Beta-value: 0.3–0.25). c Correlation between DDX1 methylation and survival prognosis in BLCA, LUSC, SKCM, and CESC is shown, respectively
Fig. 6
Fig. 6
Phosphorylation analysis of DDX1 protein in different tumors. a DDX1 phosphoprotein expression profile in BRCA is shown based on the sample types. b DDX1 proteomic expression profiles in OV were shown. The z-values were compared based on the sample types (left), tumor grade (middle), and the individual cancer stages (right). c DDX1 proteomic expression profiles in KIRC were shown. The Z-values were compared based on the sample types (left), the tumor grade (middle), and the individual cancer stages (right). d DDX1 proteomic expression profiles in UCEC were shown. The Z-values were compared based on the sample types (left), the tumor grade (middle), and the individual cancer stages (right). The Z-values represented standard deviations from the median across samples for the given cancer type. Log2 Spectral count ratio values from CPTAC were first normalized within each sample profile, then normalized across samples
Fig. 7
Fig. 7
Correlation analysis between DDX1 expression and immune infiltration in different cancers. a–b Correlation analysis of DDX1 gene expression and TMB or MSI is shown, respectively. The horizontal axis in the figure represented the expression distribution of the gene, and the ordinate was the expression distribution of the TMB or MSI score. The density curve on the right represented the distribution trend of the TMB or MSI score. The upper-density curve represented the distribution trend of the gene. The top side represented the correlation p-value, correlation coefficient, and correlation calculation method. c The correlation between DDX1 expression and cancer-driver genes in different cancers was analyzed. The color scale indicated the Spearman correlation. d The corresponding heatmap data in the exact cancer types were displayed. The red box showed the high expression renal cancers KIRC, KIRP, and KICH
Fig. 8
Fig. 8
Enrichment analysis for DDX1-related genes. a Protein–protein interaction network between the available experimentally determined DDX1-binding proteins was analyzed using the STRING tool. b The expression correlation between DDX1 and selected targeting genes, including SLC4A1AP, RDH14, E2F6, NOL10, DHX9, and HNRNOK is shown. c Heat map of the Pearson correlation between the six genes and DDX1 for pan-cancers (red: positive correlation; blue: negative correlation) is shown. d The Venn diagram showed two types of crossover genes, including FAM98B, FAM98A, and PPP1R8. e The KEGG pathway was analyzed by using the DDX1-binding and interacted genes. f–h The GO enrichment analysis was performed by using the DDX1-binding and interacted genes
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