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

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,✉
PMCID: PMC9590584  PMID: 36939765

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: RNA helicase, DDX1, Pan-cancer analysis, Survival analysis, Prognostic marker

Highlights

A first pan-cancer analysis of DDX1 in normal and cancer samples
Highly expressed DDX1 is linked to a poor prognosis of overall survival in cancers including ACC, BLCA, KICH, and LIHC
The phosphorylation level of S481 is enhanced in several tumors, including BRCA and OV, but decreases in KIRC
In tumors, the expression level of DDX1 can affect CD8+ T-cell, MSI, TMB, and ICT
RNA transport/DNA replication-associated issue is involved in the oncogenic role of DDX1
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Introduction

Members of the DEAD-box (DDX) RNA superfamily are involved in various cellular processes, including RNA splicing, ribosome biogenesis, RNA transport, degradation, and protein translation (Gibbons et al. 1997; Jarmoskaite and Russell 2014; Godbout et al. 1998; Hondele et al. 2019; Heerma et al. 2017). DDX family proteins contain nine conserved motifs, especially with the Walker B motif, characterized by the DEAD (Asp–Glu–Ala–Asp) sequence (Hall and Matson 1999). As many RNA helicases can regulate mRNA translation in cancer cells and are involved in tumorigenesis, inhibition of DDXs can be exploited for anti-cancer therapeutics (Heerma et al. 2017; Bol et al. 2015).

The Human DDX1 plays a vital role in the metabolism of RNAs located in the cell nucleus, and it participates in mRNA/miRNA processing (Mitkova et al. 2003; Zhong et al. 2018), and the NF-κB-mediated transcription (Khemici and Linder 2018; Tang et al. 2018). In addition, DDX1 plays key roles in the replication of HIV-Rev (Edgcomb et al. 2012). Moreover, DDX1 forms a complex with DDX21, DHX36, and TIR domain-containing Adapter Molecule 1 (TRIF), which can sense and recognize dsRNA and activate the expression of NF-κB and type I interferon. DDX1 is initially found in neuroblastoma (NB) and retinoblastoma (RB) cell lines and tumors (Tanner and Linder 2001). Also, DDX1 deficiency cells have defects in the colony and sphere-forming capacity in vitro and tumorigenesis in nude mice (Tanaka et al. 2018). DDX1 expression level is elevated in multiple cancers, including germ cell tumors, NB, RB, glioblastoma, BRCA, and colorectal carcinogenesis. However, how DDX1 functions in various cancers remains unclear.

Here, we performed a systematic pan-cancer analysis of RNA helicase DDX1 by TCGA and GTEx databases. Through a series of gene and protein expression differential analysis, patient prognostic survival analysis, genetic alteration analysis, DNA methylation level, protein phosphorylation level, immune infiltration analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and the gene ontology (GO) analysis, DDX1 was first discovered that it has oncogenic roles related with RNA transport/DNA replication processes, and was significantly associated with the prognosis of renal cancers, including KIRC, KICH, and KIRP, which might help us for treating RNA helicase related cancers.

Methods

Gene expression analysis

We inputted DDX1 in the ‘Gene_DE’ module of TIMER2 (Tumor immune estimation resource, version 2) web (http://timer.cistrome.org/) and checked the expression difference of DDX1 between tumor and adjacent normal tissues for the different tumors or specific tumor subtypes of the TCGA project (Li et al. 2020).

For certain tumors without usual or with highly limited normal tissues, we used the ‘Expression Analysis-Box Plots’ module of the GEPIA2 webserver (http://gepia2.cancer-pku.cn/#analysis) to obtain the box plots of the expression difference between the tumor tissues and the corresponding normal tissues of the GTEx database (Tang et al. 2019). Additionally, the violin plots of the DDX1 expression in different pathological stages (stage I, stage II, stage III, and stage IV) of all TCGA tumors were obtained via the ‘Pathological Stage Plot’ module of HEPIA2. The log2 [TPM (Transcripts per million) + 1] transformed expression data were applied for the violin plots analysis.

The UALCAN portal (http://ualcan.path.uab.edu/analysis-prot.html), an interactive web resource for analyzing cancer Omics data, was used to conduct gene expression of TCGA analysis and protein expression analysis of the CPTAC (Clinical proteomic tumor analysis consortium) dataset (Chandrashekar et al. 2017; Cerami E et al. 2012).

Raw counts of RNA-sequencing data (level 3) and corresponding clinical information from ACC, BLCA, KICH, LIHC, KIRC, KIRP, UVM, and ovarian serous cystadenocarcinoma were obtained from the TCGA dataset (https://portal.gdc.cancer.gov/) in January 2020, in which the method of acquisition and application have complied with the guidelines and policies. The Sanguini diagram was built based on the R software package alluvial. All the above analysis methods and R package implemented by the R foundation for statistical computing version 4.0.3 (Zeng et al. 2019).

Survival prognosis analysis

We used the ‘Survival Map’ module of GEPIA2 to obtain the OS and DFS significance map data of DDX1 across all TCGA tumors. Cutoff-high (50%) and cutoff-low (50%) values were used as the expression thresholds for splitting the high-expression and low-expression cohorts. The log-rank test was used in the hypothesis test, and the survival plots were obtained through the ‘Survival Analysis’ module of GEPIA2.

The univariate and multivariate cox regression analysis was performed to identify the proper terms to build the nomogram. The forest was used to show the p-value, HR (Hazard ratios), and 95% CI (Confidence intervals) of each variable through the ‘forest plot’ R package. A nomogram was developed based on multivariate Cox proportional hazards analysis results to predict the X-year overall recurrence. The nomogram provided a graphical representation of the factors, which can be used to calculate the risk of recurrence for an individual patient by the points associated with each risk factor through the ‘rms’ R package.

Genetic alteration analysis

After logging into the cBioPortal web (https://www.cbioportal.org/) (Cerami et al. 2012; Gao et al. 2013), we chose the ‘TCGA Pan-Cancer Atlas Studies’ in the ‘Quick select’ section and entered ‘DDX1’ for queries of the genetic alteration characteristics of DDX1. The results of the alteration frequency, mutation type, and CNA (Copy number alteration) across all TCGA tumors were observed in the ‘Cancer Types Summary’ module. We also used the ‘Comparison’ module to obtain the overall disease-free, progression-free, and disease-free survival differences for the TCGA cancer cases with or without DDX1 genetic alteration. Kaplan–Meier plots with log-rank p-value were generated as well.

DNA methylation analysis

The DNMIVD (DNA Methylation Interactive Visualization Database) (http://119.3.41.228/dnmivd/index/) is a comprehensive annotation and interactive visualization database for DNA methylation profile of diverse human cancer constructed with high-throughput microarray data from TCGA and GTEx databases, and it also integrates some data from the Pancan-meQTL and HACER (Human ACtive Enhancer to interpret Regulatory variants) databases (Ding et al. 2020).

MEXPRESS (https://www.mexpress.be/index.html) is a data visualization tool designed for the easy visualization of TCGA expression, DNA methylation, and clinical data, as well as the relationships between them (Koch et al. 2015). We used the DNMIVD database and the MEXPRESS approach to investigate the potential association between DNA methylation of DDX1 and different tumor pathogenesis in the TCGA project.

Immune infiltration analysis

The tumor RNA-seq data from 33 different types of tumors were downloaded from the Genomic Data Commons (GDC) data portal website in the TCGA database (https://portal.gdc.cancer.gov/). Each tumor has mRNA expression data from matched standard tissue samples. For reliable immune score evaluation, we used the immunedeconv, an R software package that integrates six latest algorithms, including TIMER, xCell, MCP-counter, CIBERSORT, EPIC, and QUANTISEQ. These genes include the SIGLEC15 (Sialic acid-binding ig like lectin 15), PD-L1 (CD274, Programmed cell death 1 ligand 1), HAVCR2 (Hepatitis a virus cellular receptor 2), PD1 (Programmed death 1), CTLA4 (Cytotoxic t-lymphocyte-associated protein 4), LAG3 (Lymphocyte-Activation Gene 3), and PDCD1LG2 (Recombinant Programmed Cell Death Protein 1 Ligand 2).

TMB (Tumor mutational burden) was derived from the Immune Landscape of Cancer (Huang et al. 2019). MSI (Microsatellite instability) was derived from the Landscape of Microsatellite Instability (Bonneville et al. 2017). We used the Spearman rank correlation coefficient to describe the correlation between quantitative variables without normal distribution. If not otherwise stated, the rank-sum test detects two sets of data, and a p-value of < 0.05 is considered statistically significant. The data are visualized as a heatmap and a scatter plot.

DDX1-related gene enrichment analysis

We first searched the DDX1 through the website (https://string-db.org/) using the query of a single protein name (‘DDX1’) and organism (‘Homo sapiens’) (Szklarczyk et al. 2019). Subsequently, we set the following main parameters: the minimum required interaction score is 0.150, the meaning of network edges is evidence and active interaction sources are experiments. Finally, the available experimentally determined DDX1-binding proteins were obtained.

We used the ‘Similar Gene Detection’ module of GEPIA2 to obtain the top 100 DDX1-correlated targeting genes based on the datasets of all TCGA tumors and normal tissues (Tang et al. 2019). We also applied the ‘correlation analysis’ module of GEPIA2 to perform a pairwise gene Pearson correlation analysis of DDX1 and selected genes. The log2 TPM was applied for the dot plot. The p-value and the correlation coefficient (r) were indicated. Moreover, we used the ‘Gene_ Cor’ module of TIMER2 to supply the heatmap data of the selected genes, which contains the partial correlation (Cor) and p-value in the purity-adjusted Spearman's rank correlation test. The Jvenn (http://jvenn.toulouse.inra.fr/app/index.html) was used to conduct an intersection analysis to compare the DDX1-binding and interacted genes (Bardou et al. 2014). WebGestalt (WEB-based Gene Set Analysis Toolkit) is a functional enrichment analysis web tool (http://www.webgestalt.org/) (Liao et al. 2019). Therefore, we combined the datasets from Jvenn and WebGestalt to perform KEGG pathway analysis.

Knockout of DDX1 in human tumor-derived cell lines

The CRISPR-Cas9 system has enabled precise genome-scale identification of genes essential for the proliferation and survival of cancer cells. However, Cas9-mediated DNA cleavage produces a gene-independent antiproliferative effect that confounds such measurement of genetic dependency. CERES is a tool to estimate gene-dependency levels from CRISPR-Cas9 essentiality screens while accounting for this effect (Meyers et al. 2017). To define a cancer dependency map, we used genome-scale CRISPR-Cas9 essentiality screens across 342 cancer cell lines and applied CERES to these data. The process is as follows: log in to the DepMap (Cancer Dependency Map) database (Tsherniak et al. 2017), search for the DDX1 gene, and select CRISPR (DepMap 21Q2Public + Score) as the data set in the Perturbation Effects option.

CancerSEA analysis

CancerSEA (Cancer single-cell state atlas) is the first dedicated database to comprehensively explore distinct functional states of cancer cells at the single-cell level (http://biocc.hrbmu.edu.cn/CancerSEA/) (Yuan et al. 2019). CancerSEA portrays a cancer single-cell functional state atlas, involving 14 functional states including stemness, invasion, metastasis, proliferation, EMT (Epithelial-Mesenchymal Transition), angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation, and quiescence of 41,900 single cancer cells from 25 cancers. In addition, it allows querying which functional states are associated with the gene (or gene list) of interest in different cancers. The process is as follows: log in to the CancerSEA, select ‘search’, enter ‘DDX1’ in ‘input a gene’, and then conduct research.

Results

Gene expression analysis of human DDX1

The human protein atlas (HPA, https://www.proteinatlas.org/) was used to explore human DDX1 (NP_004930.1) expression based on the mass-spectrometry-based proteomics, transcriptomics, and system biology (Uhlén et al. 2015; Thul et al. 2017; Uhlen et al. 2017). First, we have an overview of the basic information of human DDX1 (NM_004939.3, NP_004930.1). Through the HPA, we found that DDX1 is ubiquitously expressed in cells, tissues, and organs. The transcription level of DDX1 is highest in skeletal muscle, and the overall difference is negligible. Therefore, DDX1 expression belongs to low tissue specificity (data not shown).

Next, the TIMER2 was used to perform statistical analysis on clinical tumor samples in the TCGA and GTEx databases. In CHOL (p < 0.001), COAD (p < 0.001), ESCA (esophageal carcinoma) (p < 0.001), GBM (glioblastoma multiforme) (p < 0.05), HNSC (head and neck squamous cell carcinoma) (p < 0.001), LIHC (p < 0.001), LUAD (lung adenocarcinoma) (p < 0.001), LUSC (lung squamous cell carcinoma) (p < 0.001), STAD (p < 0.001), DLBC (lymphoid neoplasm diffuse large B-cell lymphoma) (p < 0.05), and THYM (thymoma) (p < 0.05), DDX1 had higher expression level than the corresponding normal tissues (Fig. 1a–b). The expression levels of DDX1 were down-regulated in SKCM (p < 0.001), THCA (p < 0.001), KIRC (p < 0.001), KICH (p < 0.001), and KIRP (p < 0.001) (Fig. 1a–b). To further verify the results, we compared clinical tumor samples and found a high degree of similarity between the TCGA and the GTEx databases (Fig. S1).

Fig. 1.

Fig. 1

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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

To further identify the correlation between DDX1 expression and the pathological stages of cancers, the HEPIA2 and UALCAN were used to explore different cancers, including KIRC (p < 0.01), LIHC (p < 0.001), and UCS (p < 0.05) (Fig. 1c). The results showed that the expression level of DDX1 is down-regulated in THCA and renal cancers KICH, KIRP, and KIRC, but it is up-regulated in COAD, HNSC, LIHC, LUAD, CHOL, ESCA, LUSC, and STAD (Figs. 1d, S2). Next, the Sankey program was used to determine the differences for DDX1 expression in different tumors and different tumor stages; the results showed that the expression level of DDX1 was correlated with the age, gender, pTNM stage, and prognosis of tumor patients.

As DDX1 functions mainly depending on the helicase activity (Ribeiro et al. 2018), we evaluated the protein expression level of DDX1 in different tumors through the CPTAC database. DDX1 protein is highly expressed in BRCA, OV, and LUAD, but it has lower expression in the KIRC and uterine corpus endometrial carcinoma (UCEC) than that in normal tissues (Fig. 2a). Also, in KIRC and BRCA, the expression level of DDX1 protein in tumor patients was significantly higher than that in normal tissues, evidenced by using different DDX1 antibodies (Fig. 2b). To further understand whether the expression levels and differences of DDX1 protein existed in different tumors, the individual cancer stages were compared. The results showed that DDX1 protein was significantly higher than normal samples in stages 2 and 3 of BRCA (p < 0.001) and OV (p < 0.001) (Figs. S3a–S3b), while no difference existed in colon cancer (Fig. S3c). However, the expression of DDX1 was significantly lower than that in the normal samples in KIRC (p < 0.001) from stage 1 to stage 4 (Fig. S3d). In stage 1/2/3 of LUAD, the expression level of DDX1 was significantly higher than the normal sample (p < 0.001) (Fig. S3e). The mass-spectrometry-based proteomic data from the CPTAC confirmatory/discovery cohorts were used to categorize 532 cases into ten different pan-cancer subtypes of cancer (K1–K10), which turned out that DDX1 expression had significant differences between normal samples and different subtypes of BRCA, KIRC, and UCEC (p < 0.001) (Figs. S3f–S3h). Therefore, the expression levels of the DDX1 protein are different in different tumors and patient stages. DDX1 might be used as a clinical diagnostic marker in BRCA, OV, LUAD, and KIRC.

Fig. 2.

Fig. 2

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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

Survival prognosis analysis of DDX1

As the expression level of DDX1 might be related to the prognosis of tumor patients, the cancer cases from the TCGA and GTEx datasets were divided into high-expression and low-expression groups. Highly expressed DDX1 is linked to a poor prognosis of OS in cancers including ACC (p = 0.0078), BLCA (p = 0.0089), KICH (p = 0.0085), and LIHC (p = 0.00087) (Fig. 3a). However, the high expression of DDX1 is correlated with the good prognosis of KIRC in OS (p < 0.001) and in disease-free survival (DFS) (p < 0.0043) (Fig. 3a–b). Also, highly expressed DDX1 is linked to a poor prognosis of DFS in ACC (p = 0.0046), KICH (p = 0.028), KIRP (p = 0.031), LIHC (p = 0.0018), and UVM (p = 0.033) (Fig. 3b). The further analysis showed that DDX1 is a protective factor for KIRC patients with a risk factor for KICH, ACC, and LIHC patients (Figs. 3, S4). The above data indicated that DDX1 expression is differentially associated with the prognosis of cases with different cancers.

Fig. 3.

Fig. 3

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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

Genetic alteration analysis of DDX1

To identify whether the genetic changes of DDX1 can affect the occurrence of tumors and the prognosis of patients, we used the cBioPortal web (https://www.cbioportal.org/) to analyze the data from the TCGA. Based on the cancer study (Fig. 4a), the main types of point mutation of DDX1 are in uterine cancer, bladder cancer, lung squamous, stomach cancer, lung adenocarcinoma, melanoma, mesothelioma, and colorectal cancer. Also, the primary cancer types that occurred with amplification are from uterine carcinosarcoma, uterine cancer, bladder cancer, lung squamous, liver cancer, ovarian cancer, GBM, and ACC (Fig. 4a). On the other hand, fusion mutation appears less frequently, mainly in bladder cancer, head and neck cancer (Fig. 4a). Based on the cancer type (Fig. S5a), of all the tumors with genetic alteration, endometrial carcinoma has the highest percentage (5.63%), followed by cervical adenocarcinoma (4.35%) and bladder urothelial carcinoma (4.14%). The main types of genetic alteration in endometrial carcinoma are point mutation and amplification. Deep deletion is the type of major genetic alteration in mature B-cell neoplasms. Thus, the primary genetic change types of DDX1 are point mutation and amplification in most tumors.

Fig. 4.

Fig. 4

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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

Moreover, the missense mutation of DDX1 is the primary type of genetic alteration (Fig. 4b). There is a total of 93 cases of missense type, accounting for 80.17% (93/116) (Fig. 4b). Further analysis showed that the genetic changes of DDX1 have an adverse effect on DFS (p = 0.0329) and DSS (disease-specific survival) (p = 0.0385), but no significant effects on OS (p = 0.228) and PFS (progress-free survival, p = 0.135) (Fig. 4c). Next, the results were analyzed in LUSC, and it showed that the genetic changes of DDX1 have an adverse effect on DFS (p < 0.001) and DSS (p = 0.0254) (Fig. S5b). Taken together, we explored the potential association between genetic alteration of DDX1 and the clinical survival prognosis of different cancers.

DNA methylation analysis of DDX1

DNA methylation regulates gene expression in different cancers. In 22 tumors from the DNMIVD database, DNA methylation had a negative correlation with DDX1 expression level. The strongest correlation was in LUAD (Pearson _R = − 0.22166, Pearson_p = 1.0384e-06; Spearman _R = − 0.289865; Spearman_p = 1e−10) (Fig. 5a–b). The DNA methylation levels were correlated with the good prognosis in BLCA, CESC, and SKCM by grouping the median survival (Fig. 5c); the DNA methylation levels were correlated with the poor prognosis in LUSC. Next, analysis with the CNCB-NGDC and the MEXPRESS showed that the DNA methylation of DDX1 was negatively correlated with the expression level in GBM and LGG (CNCB-NGDC Members and Partners 2021).

Fig. 5.

Fig. 5

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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

Protein phosphorylation analysis of DDX1

Dysfunction of protein phosphorylation might result in severe outcomes in different cancers. Thus, the phosphorylation analysis of DDX1 was performed with BRCA, OV, KIRC, and UCEC based on the CPTAC database (Fig. 6). According to the CPTCA database information, DDX1 has three phosphorylation sites in BRCA (S481, S486, S632). By comparing normal tissues and cancer tissues, we found that only S481 has a significant difference (p < 0.001), while S486 (p = 0.085) and S632 (p = 0.786) are not statistically different. The phosphorylation level of S481 was higher than normal tissues in BRCA. This indicates that the phosphorylation of S481 is most clinically significant in BRCA. Also, the phosphorylation level of S481 increased in OV in tumor tissues (p < 0.001), while decreased in KIRC (p < 0.001). In UCEC, there was no significant difference in the phosphorylation level of S481 between normal tissues and tumor tissues (p = 0.085). In summary, the phosphorylation level of S481 was higher than normal tissues in BRCA and OV, but lower in KIRC (Fig. 6). Thus, the occurrence of tumors might be accompanied by the enhancement of S481 phosphorylation of DDX1. Furthermore, the phosphorylation level of DDX1 varies to different degrees among different tumor grades, individual cancer stages, and pan-cancer subtypes (Fig. 6). The phosphorylation level of DDX1 out of 4440 TCGA tumor samples from 15 cancer types in the PhosphoSitePlus website showed that many phosphorylation sites were mainly located in the low complex region (LCR) of the DDX1 protein (Fig. 4b). As the LCRs of DDX family proteins can affect the protein conformation and function (Chen et al. 2020), the phosphorylation level at this region might play an essential role in tumorigenesis.

Fig. 6.

Fig. 6

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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

Immune infiltration analysis of DDX1

The tumor microenvironment (TME) contributes to the modulation of tumor progression (Litchfield et al. 2021). Thus, the TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, XCELL, MCPCOUNTER, and EPIC algorithms were used to investigate the potential relationship between the infiltration level of different immune cells and DDX1 gene expression in different cancers from TCGA. Overall, the expression level of DDX1 has a specific correlation with the infiltration of different immune cells in many cancers. DDX1 expression is negatively correlated with the immune infiltration of CD8+ T-cells in ESCA, LUAD, LUSC, sarcoma (SARC), SKCM, testicular germ cell tumors (TGCT), and UCEC, but it positively correlationed with CD8+ T-cells in KICH, PAAD, pheochromocytoma and paraganglioma (PCPG), and UVM (Fig. S6a).

The inhibitory immunoreceptors PD-1, CTLA4, LAG3, TIM3, TIGIT, and BTLA are named immune checkpoints, referring to molecules that act as gatekeepers of immune responses (He and Xu 2020). ICT has become a powerful weapon in fighting cancer (Brahmer et al. 2012; Zhang and Zhang 2020). MSI and TMB represent a valuable estimation of tumor neoantigen load (Rizvi et al. 2015; Hugo et al. 2016). DDX1 expression positively correlated with MSI in READ, TCGT, and STAD (Fig. S6b), but it negatively correlated with MSI in DLBC (Fig. 7a, S6b). Also, DDX1 expression positively correlated with TMB in STAD, LUAD, and SKCM, but it negatively correlated with THCA (Fig. 7b, S7). As the expression level of DDX1 is positively correlated with MSI and TMB in STAD, the further analysis with STAD patients showed a significant association between DDX1 and MLH1 (Mutl Homolog 1, Cor = 0.256), MSH2 (Muts Homolog 2, Cor = 0.762), MSH6 (Muts Homolog 6, Cor = 0.717), and PMS2 (Pms1 Homolog 2, Cor = 0.551) (Fig. S6d). The above results indicate that the expression level of the DDX1 gene is correlated with MSI and TMB.

Fig. 7.

Fig. 7

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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

Next, we analyzed the correlation between DDX1 and eight immune checkpoints in different tumors. DDX1 positively correlates with the immune checkpoints in UVM, READ, PCPG, PAAD, and LIHC, but negatively correlates with immune checkpoints in THCA, TGCT, LUSC, LUAD, and CESC (Fig. 7c). Previous reports showed that DDX1 is involved in the regulation of CXCL12 (Stromal cell-derived factor 1), CXCL10 (C-X-C motif chemokine 10), CCR5 (C–C chemokine receptor type 5), and CXCL9 (C-X-C motif chemokine 9) (Nagel et al. 2004). However, DDX1 had weak correlations with these immune checkpoints in tumor-adjacent normal samples, while it had a significant positive correlation with CXCL9 (Fig. S6e).

The tumor mutation database IntOGen was used to identify cancer genes and pinpoint their putative mechanism of action across tumor types (Martínez et al. 2020). We found that DDX1 is mainly positively correlated with cancer driver genes TP53 (Cellular tumor antigen p53), APC (Adenomatous polyposis coli protein), BRAF (Serine/threonine-protein kinase b-raf), ARID1A (At-rich interactive domain-containing protein 1a), KRAS (GTPase KRas), LRP1B (Low-density lipoprotein receptor-related protein 1b), MLL2 (Histone-lysine N-methyltransferase 2d), 3 MLL3 (Myeloid/lymphoid or mixed-lineage leukemia), PIK3CA (Phosphatidylinositol 4,5-bisphosphate 3-kinase catalytic subunit alpha isoform), and PTEN (Phosphatase and tensin homolog deleted on chromosome ten) (Fig. 7d). Also, DDX1 has a strong positive correlation with APC (Cor = 0.871), KRAS (Cor = 0.877), and MLL3 (Cor = 0.844) in UVM (Fig. 7d). In summary, there is a close relationship between DDX1 with tumor immunity, MSI, TMB, and ICT.

Enrichment analysis of DDX1-related partners

To further investigate the molecular mechanism of the DDX1 gene in tumorigenesis, we screened out the DDX1-binding proteins and the DDX1 expression-correlated genes for a series of pathway enrichment analyses. Based on the STRING tool, we obtained a total of 50 DDX1-binding proteins, which were supported by experimental evidence (Fig. 8a). We used the GEPIA2 tool to combine all tumor expression data of TCGA and obtained the top 100 genes that correlated with DDX1 expression. As shown in Fig. 8b, the DDX1 expression level was positively correlated with that of SLC4A1AP (Kanadaptin) (r = 0.62), RDH14 (Retinol dehydrogenase 14) (r = 0.57), E2F6 (Transcription factor E2F6) (r = 0.57), NOL10 (Nucleolar protein 10) (r = 0.57), DHX9 (ATP-dependent RNA helicase A) (r = 0.54), and HNRNPK (Heterogeneous nuclear ribonucleoprotein K) (r = 0.53) (all genes with p < 0.001). All the above six genes were positively correlated with DDX1 in multiple tumors (p < 0.05) using the TIMER2 tool (Fig. 8c–d). Also, the expression level of the DDX1 has a positive correlation with FAM98B (Protein FAM98B), FAM98A (Protein FAM98A), and PPP1R8 (Nuclear inhibitor of protein phosphatase 1) in various types of tumors analyzed by using the TIMER2 tool (Fig. S8a).

Fig. 8.

Fig. 8

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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

To further understand the pathway distribution of these related genes, we integrated the two sets of gene data for KEGG and GO enrichment analysis. Multiple pathways, including the RNA transport, homologous recombination, and Fanconi anemia pathway, might be involved in the pathogenesis of DDX1 (Figs. 8e, S8b). Based on biological process categories (BP) analysis (Figs. 8f, S8c), DDX1 may participate in the occurrence of tumors by participating in the regulation of the metabolic process, RNA splicing, protein translation, and other pathways. Based on cellular component categories (CC) analysis (Figs. 8g, S8d), these genes mainly exist in the nucleus chromosome, chromosomal region, and cytoplasmic ribonucleoprotein granule. Based on molecular function categories (MF) analysis (Figs. 8h, S8e), these genes have single-stranded DNA binding activity, helicase activity, and catalytic activity.

Knocking out of DDX1 in different cell lines

Next, the data from CRISPR-Cas9 screens across 558 cancer cell lines (https://depmap.org) were analyzed for ‘Essentiality’ of the DDX1 genes (Meyers et al. 2017). ‘Essentiality’ of a gene was estimated using CERES, which estimates gene-dependency scores (gDS). ‘Strictly essential genes’, whose deletion severely affects cell viability, have gDS <  − 1. ‘Strictly non-essential genes’, whose deletion does not affect cell viability, have gDS > 0. Genes with gDS between − 1 and 0 are ‘potentially non-essential’, indicating the deletion affects cell viability to some degree but is not lethal. In all knockout cell lines, DDX1 had a CERES score of less than 0, and only a few cell lines had a score of less than -1, indicating that DDX1 has a vital role in tumor cell lines (Fig. S9). Also, the cell lines from blood, gastric, peripheral nervous system, soft tissue, and upper aerodigestive have gDS <  − 1. The RNA interference (RNAi) results are also similar (Fig. S9a). In conclusion, depletion of DDX1 affected the cell viability of human tumor-derived cell lines.

Tumor-related single-cell sequencing data

To further explore the mechanistic pathways of DDX1 in tumors, the CancerSEA was used to analyze reported tumor single-cell sequencing data (Fig. S9b). We found that DDX1 has the strongest positive correlation with the DNA repair pathway (Cor = 0.3) in BRCA, Cell Cycle (Cor = 0.26) in high-grade glioma (HGG), and Invasion (Cor = 0.26) in BRCA. DDX1 has the strongest negative correlation with Differentiation (Cor = − 0.29) in OV, followed by Metastasis (Cor = − 0.28) in colorectal cancer (CRC), and Inflammation (Cor = − 0.26) in CRC. The above results verify that DDX1 participates in multiple pathways in tumors.

Discussion

Pan-cancer analysis was successfully used for identifying diagnostic and prognostic markers in many cancers (Cheng et al. 2021; Ju et al. 2021; Chatrath et al. 2020; Mitra et al. 2020; Berger et al. 2018; Robichaux et al. 2019; Turajlic et al. 2017). Here, we explored the potential oncogenic roles of DDX1 across 33 tumors based on the TCGA and the GTEx databases, and it was systematically explained that differences in DDX1 expression levels, genetic changes, DNA methylation levels, and protein phosphorylation modifications could affect and regulate the immune cells, change the tumor microenvironment, and affect tumors by participating in the RNA transport pathway (Fig. 1–8). DDX1 is highly expressed in many tumors, including BRCA, CHOL, and COAD, in agreement with previous reports (Tanaka et al. 2018) (Fig. 1). Also, the low expression of DDX1 is related to the poor prognosis of OV patients (Fig. 3), similar to the previous report (Li et al. 2008). Most importantly, we found that DDX1 is lowly expressed in renal cancers, including KIRC, KICH, and KIRP. Low expression of DDX1 in KIRC is correlated with a good prognosis of OS and DFS (Fig. 3). Thus, DDX1 might be a protective factor for KIRC patients and a risk factor for KICH, ACC, and LIHC patients.

More evidence showed that genetic changes of DDX1 are associated with different cancers; for example, changes of gene copy number and abnormally amplification of DDX1 with the MYCN oncogene in neuroblastoma are associated with poor prognosis in many tumors (Bayani et al. 2000; De et al. 2002). The genetic alteration analysis showed that the primary genetic change types of DDX1 are point mutation and amplification in most tumors. Especially in LUSC, the genetic alteration of DDX1 had an adverse effect on DFS (p < 0.001) and DSS (p = 0.0254) (Fig. S5b). Taken together, we explored the potential association between genetic alteration of DDX1 and the clinical survival prognosis of different cancers.

The correlation analysis between DDX1 and DNA methylation showed that the amplifier is the primary genetic change in different tumors (Fig. 4). Therefore, we conducted a methylation analysis and found that DNA methylation negatively correlated with DDX1 expression level in cancers (Fig. 5). The methylation levels of DDX1 have distinct effects on the prognosis of different cancers. We found that the DNA methylation level was significantly lower than that of normal tissues in SKCM, while the DNA methylation level was significantly higher than that of normal tissues in LUSC. However, there is no significant difference between BLCA and CESC. That may be one of the reasons for the different prognostic outcomes. At present, through analysis, we have found that the high DNA methylation level of DDX1 is related to the excellent prognosis of patients in LUSC, suggesting that DDX1 may regulate gene activity through DNA methylation levels and cause-related chromosomes changes in structure, DNA conformation, and DNA stability to regulate the gene expression level of DDX1. All the data above explain the difference in DDX1 expression in different cancers from epigenetics. Unfortunately, there are no available data on DDX1 protein methylation.

In addition, DDX1 can promote the expression of a subset of miRNAs in a mesenchymal ovarian cancer subtype, facilitated by the ataxia telangiectasia mutated (ATM)-mediated phosphorylation (Han et al. 2014). We found that Ser481 of DDX1 had an enhanced phosphorylation level in BRCA and OV but decreased in KIRC (Fig. 6). Thus, the phosphorylation of DDX1 might act as a regulatory protein to induce miRNAs expression and be involved in the occurrence of multiple tumors. It may also be because of the increase in the expression level of DDX1 protein, which leads to the increase in phosphorylation level in BRCA (Fig. 2). Therefore, the occurrence of tumors will be accompanied by the enhancement of S481 phosphorylation of DDX1, which has an important physiological role, and the specific related regulatory mechanisms are worthy of further study. Immune infiltration analysis showed that DDX1 expression affected CD8+ T cells, and is significantly associated with MSI, TMB, and ICT in tumors (Fig. 7). Therefore, DDX1 may become a potential therapeutic prediction target when combing with PD-1/PD-L1 and other immunotherapies. Loss of DDX1 caused rRNA processing defects, thereby activating the ribosome stress-p53 pathway (Suzuki et al. 2021). By KEGG and GO analysis, we further identified that DDX1 might participate in the regulation of tumorigenesis through RNA transport, Homologous recombination, Fanconi anemia pathway, and other pathways (Fig. 8). The two reported databases (Depmap and CancerSEA) were used to verify that DDX1 plays an essential function in tumor cell lines and it is related to the cancer driver genes (TP53, APC, ARID1A, BRAF, KRAS, LRP1B, MLL2, MLL3, PIK3CA, and PTEN) (Fig. S9).

In conclusion, the pan-cancer study of DDX1 offers a comprehensive understanding of the oncogenic roles of DDX1 across different tumors, suggesting that DDX1 might be a prognostic marker for renal cancers and a clinical diagnostic marker in BRCA, OV, LUAD and KIRC.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 3543 kb) (3.5MB, docx)

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (82071782) and the Shanghai Committee of Science and Technology (20XD1400800) to JL.

Authors' contribution

JL conceived and supervised the study. JL and BG wrote the manuscript. BG, XL, SL, SW, and JW conducted the analysis, interpreted the results, and critically reviewed the manuscript.

Data availability

The datasets generated during and/or analyzed during the current study are available in the TCGA and GTEx. The TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) and GTEx (https://www.genome.gov/Funded-Programs-Projects/Genotype-Tissue-Expression-Project) belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data free for research and publish relevant articles.

Code availability

Not applicable.

Declarations

Conflicts of interest

This 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.

Consent to publication

Not applicable.

Consent to participate

Not applicable.

Ethics approval

Not applicable.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary file1 (DOCX 3543 kb) (3.5MB, docx)

Data Availability Statement

The datasets generated during and/or analyzed during the current study are available in the TCGA and GTEx. The TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga) and GTEx (https://www.genome.gov/Funded-Programs-Projects/Genotype-Tissue-Expression-Project) belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data free for research and publish relevant articles.

Not applicable.

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