Skip to main content

Multiple roles of apolipoprotein B mRNA editing enzyme catalytic subunit 3B (APOBEC3B) in human tumors: a pan-cancer analysis

Abstract

Although there have been some recent cell and animal experiments indicating that expression of the gene encoding apolipoprotein B mRNA editing enzyme catalytic subunit 3B (APOBEC3B) is closely related to cancer, it still lacks pan-cancer analysis. Here we analyzed the potential carcinogenic role of APOBEC3B in 33 tumors based on The Cancer Genome Atlas (TCGA). APOBEC3B was highly expressed in most tumors and weakly expressed in a few. Differences in expression level were significantly correlated with the pathological tumor stage and prognosis of affected patients. The high-frequency APOBEC3B changes were principally mutations and amplifications in some tumors, such as uterine corpus endometrial carcinomas or cutaneous melanomas. In testicular germ cell tumors and invasive breast carcinomas, APOBEC3B expression and CD8+ T lymphocyte counts were correlated. In other cancers, such as human papilloma virus (HPV)-related head and neck squamous cell carcinomas or esophageal adenocarcinomas, there was also cancer-associated fibroblast infiltration. The APOBEC3B enzyme acts in the mitochondrial respiratory electron transport chain and in oxidative phosphorylation. This first pan-cancer study provides a comprehensive understanding of the multiple roles of APOBEC3B in different tumor types.

Peer Review reports

Introduction

Over the past decades, the incidence of cancer has increased rapidly. Cancers are among the leading causes of death worldwide, so the therapeutic success rate needs to be further improved [1, 2]. In this regard, the mechanisms of tumorigenesis and progression are complicated and deserve further investigation. Pan‐cancer analysis has emerged as a widely used method to investigate the common features and heterogeneities of various tumor types. It allows for a thorough and profound understanding of human cancers [3]. The publicly funded The Cancer Genome Atlas (TCGA) project contain functional genomics datasets for different tumors, [4, 5] which enable pan-cancer analysis.

The apolipoprotein B mRNA editing enzyme catalytic subunit 3B (APOBEC3B) protein, also known as A3B or ARP4, is a member of the cytidine deaminase gene family [6]. It is one of seven related genes or pseudogenes found in a cluster on human chromosome 22, thought to result from gene duplication. Members of the cluster encode proteins that are structurally and functionally related to the C-to-U RNA-editing cytidine deaminase APOBEC [7,8,9]. APOBEC3B is involved in the development and progression of breast cancers [10], hepatocellular carcinomas [11], ovarian cancers, [12] nasopharyngeal carcinomas, [13] and chondrosarcomas [14]. Comprehensive analysis of the expression patterns, prognostic significance and relationship with tumor microenvironment of APOBEC3B at pan-cancer level will help us deepen our understanding of this enzyme.

Here, we conducted a pan-cancer analysis of APOBEC3B using the TCGA project. We also analyzed the pathogenesis or potential molecular mechanisms of APOBEC3B in different cancers by gene expression, patient survival status, protein expression, gene mutation, immune infiltration, protein interactions and cellular pathways. These findings could strengthen our understanding of the biological functions of APOBEC3B in different cancer types.

Materials and methods

Gene expression analysis

We entered APOBEC3B into the ‘GENE_DE’ module of ‘Exploration’ on the Tumor Immune Estimation Resource (TIMER2, v. 2) web page (http://timer.cistrome.org/) [15,16,17] and observed the differential expression of AOPBEC3B in different tumors and adjacent normal tissues in the TCGA project. For certain tumors that lacked normal adjacent tissue samples (e.g., TCGA-ACC for adrenocortical carcinomas; TCGA-OV for ovarian serous cystadenocarcinomas), we used the UCSC XENA database (https://xenabrowser.net/datapages/) [18] downloaded the TCGA and the Genotype-Tissue Expression (GTEx) of transcripts per million reads (TPM) RNAseq data in the format processed uniformly by the Toil process [19]. Then we performed log2 transformation of these data, compared the expression between samples of tumor tissues and corresponding normal tissues, and have presented the results in box plots. Gene expression differences were visualized with the ‘ggplot2’ R package. The R language software (R-3.6.3, 64 bit; http://www.r-project.org/) was used in this analysis. In addition, we used the ‘Expression DIY-Pathological Stage Plot’ of the Gene Expression Profiling Interactive Analysis, web services (GEPIA2 v. 2; http://gepia2.cancer-pku.cn/analysis) [20] to obtain violin plots of APOBEC3B expression levels in different pathological stages (stages I through IV) of all TCGA tumors. All the above box plots or violin plots were based on log2(TPM + 1) transformed expression data.

We performed protein expression analysis on the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset using the UALCAN web resource (http://ualcan.path.uab.edu/index.html), [21] a comprehensive, interactive web for analyzing cancer omics data. We entered ‘APOBEC3B’ to analyze differences in APOBEC3B total protein levels between tumors and adjacent normal tissues. The available data sets for eight tumor types were selected: breast cancers, ovarian cancers, renal clear cell carcinomas (RCC), uterine corpus endometrial carcinomas (UCEC), lung adenocarcinomas (LUAD), head and neck squamous carcinomas (HNSC), pancreatic adenocarcinomas (PAAD), and liver hepatocellular carcinomas (LIHC).

Survival analysis

We used the ‘Survival Analysis-Survival Map’ module of GEPIA2 to obtain the survival heat map of overall survival (OS) and disease-free survival (DFS) of APOBEC3B for patients with all TCGA tumors, estimated using the Mantel–Cox test. We used months as the survival time unit, high cutoff (50%) and low cutoff (50%) values as the segmentation criteria of high expression and low expression cohorts and P < 0.05 as the measure of significance level. Subsequently, we analyzed tumors with significant differences in patient survival in heat maps through the ‘survival analysis’ module of GEPIA2 and obtained the corresponding survival plots.

Gene alteration analysis

We selected ‘TCGA pancancer atlas studies’ in the ‘quick select’ option on the home page of the cBioPortal website (https://www.cbioportal.org/), [22] and then entered ‘APOBEC3B’ to query its gene alterations. In the ‘Cancer Type Summary’ module, the alteration frequency, mutation type and copy number alteration (CNA) results of all TCGA tumors were observed. In the ‘Mutations’ module, the mutation site information of APOBEC3B is displayed in the protein structure track or three-dimensional (3D) structure diagrams. After that, we analyzed the overall, disease-specific, disease-free and progression-free data of TCGA cancer cases with APOBEC3B gene alterations through the ‘Comparison/Survival’ module, and obtained the corresponding Kaplan–Meier plots with log-rank test P-values.

Immune infiltration analysis

We used the ‘Immune gene’ module of the TIMER2 website to evaluate the correlation between APOBEC3B and immune infiltration expression in all TCGA tumors, entered ‘APOBEC3B’ in gene expression and selected ‘T-cells CD8 + ’ and ‘cancer-associated fibroblasts’ in the immune information menu. Then, the immune infiltration data were analyzed by TIMER, EPIC, MCPCOUNTER, XELL, TIDE, CIBERSORT, CIBERSORT-ABS algorithms, and then the P-values and partial correlation values were obtained using a purity-adjusted Spearman’s rank correlation test. Finally, heatmaps and scatter diagrams were obtained after the data were visualized.

APOBEC3B-related gene enrichment analysis

After logging into the STRING (https://cn.string-db.org/) website, [23] we first entered the protein name ‘APOBEC3B’ and selected the organism as ‘Homo sapiens’, Then we set the parameters as follows: meaning of network edges (‘Confidence’), active interaction sources (‘Textmining’, ‘Experiments’, ‘Databases’, ‘Co-expression’, ‘Gene Fusion’, and ‘Co-occurrence’), minimum required interaction score (‘low confidence 0.150’) and maximum number of interactors to show ‘no more than 50 interactors’. Ultimately, we obtained the top 50 APOBEC3B-related evidence-based proteins and corresponding network diagrams.

To search for genes with an expression pattern similar to APOBEC3B in different cancer types, we entered the gene ‘APOBEC3B’ and ‘top 100’ in the ‘Expression Analysis-Similar Gene Detection’ module of the GEPIA2 website, and selected all TCGA tumor data sets for analysis. Subsequently, we selected the top five genes from the list, entered the gene’APOBEC3B’ and ‘top5 genes’ in the ‘expression analysis correlation analysis’ module of GEPIA2, selected the Pearson correlation coefficient, and performed a visual correlation analysis on the entire TCGA tumor dataset. The dot plots obtained were scaled by log2 TPM and contained the P-values and the correlation coefficients (R values). In addition, we used the ‘Exploration Gene_Corr’ module of TIMER2 to obtain the heatmap data related to the top five genes and APOBEC3B in various cancer types, which gave us the purity-adjusted partial Spearman’s rho value as the degree of their correlation.

We used the ‘Draw Venn Diagram’ website (http://bioinformatics.psb.ugent.be/webtools/Venn/) to cross-analyze the previously acquired ‘Top50 gene’ of STRING and ‘Top100 gene’ data from GEPIA2. Subsequently, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for the above genes. We entered the gene list on the David (https://david.ncifcrf.gov/) [24] website and selected ‘OFFICAL_GENE_SYMBOL’ and ‘Homo sapiens’ to finally obtain functional annotation data. In addition, we used the ‘clusterprofiler’ (v. 3.14.3) for enrichment analysis and the ‘org.Hs.eg.db’ (v. 3.10.0 for ID transformation) R packages to perform gene ontology (GO) enrichment analysis. The resulting KEGG and GO analysis including biological process (BP), cellular component (CC), and molecular function (MF) data described above were visualized as bubble plots and network diagrams via the ‘ggplot2’ and ‘clusterprofiler’ R packages (v. 3.3.3). All R languages in this article are based on the R software (R-3.6.3, 64bit; http://www.r-project.org/).

Results

Expression analysis of APOBEC3B

In this study, we principally explored the potential role that APOBEC3B (NM_004900.4 for the mRNA or NP_004891.4 for the protein; Fig. S1a) might play during cancer initiation and progression. The gene encoding APOBEC3B is conserved in the chimpanzee, dog, cow, mouse, and rat, and 20 organisms have orthologs with human APOBEC3B. [25] We investigated the expression profile of the APOBEC3B gene in cells from different normal tissues, as shown in Additional file 2: Figure S1b, the mRNA expression of APOBEC3B showed the highest level in bone marrow, followed by the appendix and colon, based on the Human Protein Atlas dataset.

We analyzed APOBEC3B expression levels in different cancer types from TCGA using the TIMER2 website. As shown in Fig. 1a, the expression level of APOBEC3B was higher in the tumor tissues of bladder urothelial carcinomas (BLCA), breast invasive carcinomas (BRCA), cholangiocarcinomas (CHOL), esophageal carcinomas (ESCA), glioblastoma multiforme (GBM), HNSC, kidney renal clear cell carcinomas (KIRC), kidney renal papillary cell carcinomas (KIRP), LIHC, LUAD, lung squamous cell carcinomas (LUSC), stomach adenocarcinomas (STAD), UCEC (P < 0.001), cervical squamous cell carcinomas and endocervical adenocarcinomas (CESC), kidney chromophobes (KICH), prostate adenocarcinomas (PRAD) (P < 0.01) than in the corresponding normal tissues. However, it was lower in the tumor tissues of colon adenocarcinomas (COAD), thyroid carcinomas (THCA) (P < 0.001) and PRAD (P < 0.01).

Fig. 1
figure 1

Expression level of the APOBEC3B gene in different tumors and pathological stages. a The mRNA expression of the APOBEC3B in different tumor subtypes and adjacent normal tissues. **P < 0.01; ***P < 0.001. b The mRNA expression of the APOBEC3B in ACC, LAML, LGG, OV, SKCM, TGCT, THYM, and UCS tumor types in the TCGA dataset, corresponding normal tissues of the GTEx database were included as controls. ***P < 0.001. c The protein levels of APOBEC3B between normal tissues and primary tissues of breast cancers, clear cell RCC, hepatocellular carcinomas, PAAD, UCEC, HNSC, and LUAD, based on the CPTAC dataset. **P < 0.01. d The relationship between APOBEC3B expression and clinical stages in ACC, BLCA, CHOL, KIRC, KIRP, LIHC, OV, and THCA tumor types

For those TCGA tumors with missing normal tissue data, we acquired the normal tissue data of the GTEX dataset and used them as a control group to further analyze the expression differences of APOBEC3B in normal tissues and tumor tissues of Adrenocortical carcinoma (ACC), acute myeloid leukemia (LAML), OV, testicular germ cell tumors (TGCT), uterine carcinosarcomas (UCS), brain lower grade gliomas (LGG), skin cutaneous melanomas (SKCM) and thymomas (THYM) (Fig. 1b , P < 0.001). Among them, LAML and THYM were weakly expressed in tumor tissues. Nevertheless, we did not find significant differences in the expression of APOBEC3B in lymphoid neoplasm diffuse large B-cell lymphomas (DBLC), mesotheliomas (MESO), pheochromocytomas and paragangliomas (PCPG), or uveal melanomas (UVM) tissues.

To further explore the proteomics data of APOBEC3B in patients with cancers, we conducted an analysis using the CPTAC dataset. The APOBEC3B protein expression level was higher in renal clear cell carcinomas, UCEC, HNSC, PAAD and LIHC and lower in breast cancers and LUAD (Fig. 1c, all P < 0.01) than in normal tissues. Interestingly, the proteomics data suggested lower expression of the APOBEC3B protein in breast cancer and LUAD, opposite of that observed at the mRNA level. These inconsistent results might be caused by post-transcriptional or translational regulation.

In addition, we used the ‘Pathological Stage Plot’ module of GEPIA2 to analyze the relationship between the expression of APOBEC3B and the pathological stages of cancers. As shown in Fig. 1d, we obtained the pathological stage plots of ACC, BLCA, CHOL, KIRC, KIRP, LIHV, OV and THCA (all P < 0.05). To be specific, in ACC, BLCA, KIRC, and KIRP tissues, APOBEC3B RNA expression levels were consistently associated with later clinical stages; in CHOL, LIHC, and OV mRNA expression level of APOBEC3B was significantly higher in the early stage of these diseases.

Thus, APOBEC3B has different expression levels in different tumor tissues; the expression levels were closely related to the pathological state of the tumor, but the correlation was not consistent across different cancer species. These findings indicated a diverse biological function of this enzyme in different tumor types.

Survival analysis data

Taking the median expression level of APOBEC3B in tumor cases as the standard, we divided the tumor cases in TCGA datasets into high- and low-expression groups, and explored the relationship between the expression of APOBEC3B and the prognoses for different patients. As shown in Fig. 2a, the tumors associated with high expression of APOBEC3B and poor OS were ACC (P < 0.001), LGG (P < 0.001), LIHC (P = 0.03) and UVM (P = 0.032) within the TCGA project In the DFS analysis (Fig. 2b). A high expression level of APOBEC3B was associated with poor prognosis for patients with ACC (P < 0.001), KIRP (P = 0.015), LGG (P = 0.0016), LIHC (P = 0.037), and THCA (P = 0.0068). Furthermore, low expression of APOBEC3B was related to poor OS and DFS prognoses for patients with CESC (Fig. 2a , P = 0.0012; Fig. 2b , P = 0.029). These inconsistent results in different cancer species suggested that APOBEC3B might play different roles in different tumor types.

Fig. 2
figure 2

Correlation between APOBEC3B gene expression and survival prognosis for patients with cancers in TCGA. We performed overall survival (OS) (a) and disease-free survival (DFS) (b) analyses of different tumors in TCGA according to APOBEC3B gene expression. The survival maps and Kaplan–Meier curves with positive results were provided

We also analyzed the clinical prognostic significance of DNA methylation of APOBEC3B in the website tool (https://biit.cs.ut.ee/methsurv/). We have listed the results in Additional file 1: Table S1. Methylation at cg06837067 had the most prognostic significance, predicting a poor overall survival in ACC (HR: 2.179, P = 0.04), BLCA (HR: 1.924, P < 0.01), CESC (HR: 2.034, P < 0.01), HNSC (HR: 1.556, P = 0.01), KIRC (HR: 2.353, P < 0.01), KIRP (HR: 2.258, P = 0.01), SKCM (HR: 1.735, P < 0.01), and STAD (HR: 1.526, P < 0.01), however, in LGG (HR: 0.343, P < 0.01), MESO (HR: 0.564, P = 0.045), and UVM (HR: 0.229, P = 0.046), the methylation level at cg06837067 predicted greater overall survival (Additional file 1: Table S1). This further suggested that APOBEC3B might play different roles in different cancer types.

Gene alteration analysis

We analyzed the gene alteration status of APOBEC3B in all tumor samples in the TCGA dataset through cBioPortal. As shown in Fig. 3a, the tumor type with the highest alteration frequency of APOBEC3B was UCEC (> 4%), mainly of the ‘mutation’ type. The second most frequent alteration was in SKCM (~ 3.7% frequency). In cases with ovarian serous cystadenocarcinomas, the main type was ‘amplification’, and the alteration frequency was ~ 2%. It is worth mentioning that all types of genetic alteration (~ 2% frequency) in cases of thymomas were of the ‘deep deletion’ type. In addition, the types, loci and corresponding cases of APOBEC3B gene alteration are shown in Fig. 3c. Missense mutations were found to be the main type of APOBEC3B genetic alterations: an R114H/S alteration in the APOBEC-like N-terminal domain, which was detected in one case of GMB, one case of PRAD and one case of STAD (Fig. 3b), and a P355S alteration in APOBEC-like C-terminal domain, which was detected in one case of LUSD and two cases of SKCM. These alterations are able to induce a frame shift mutation of the APOBEC3B gene, from R (Arginine) to H (Histidine) or S (Serine) at site 114 and P (Proline) to S at site 355 of the APOBEC3B protein, and subsequent APOBEC3B protein truncation. We also noted the sites of R144,R306 and P335 in the 3D structure of APOBEC3B protein (Fig. 3c). In addition, we also analyzed the association between APOBEC3B gene alteration and clinical survival and prognosis with different types of cancers. As shown in Fig. 3d, the APOBEC3B altered group in patients with SKCM showed better prognosis in DSS (P = 0.0411), OS (P < 0.01) and progression-free (P = 0.0137) survival, but in UCEC cases overall, disease-specific, disease-free and progression-free patient survival rates were not statistically significant.

Fig. 3
figure 3

Mutation feature of APOBEC3B in different tumors listed in TCGA. a, b The mutation features of APOBEC3B for the TCGA-listed tumors using the cBioPortal tool. The alteration frequencies with mutation type (a) and mutation site (b) were displayed. c The highest alteration frequency of mutation sites (R114, R306, R355) of APOBEC3B were shown in the 3D structure. d The potential correlation between mutation status and disease-specific, overall and progression-free survival in patients with SKCM

Immune infiltration analysis data

Tumor-infiltrating immune cells play an important role in the occurrence, progression and metastasis of tumors [26]. We used the TIMER, EPIC, MCPCOUNTER, XELL, TIDE, CIBERSORT, and CIBERSORT-ABS algorithms to explore correlations between the infiltration level of various immune cells and the expression level of APOBEC3B gene in different TCGA tumors. Cancer-associated fibroblasts are the main component of the stroma and secrete growth factors, inflammatory ligands and extracellular matrix proteins to promote tumor proliferation, therapeutic resistance and immune rejection. After a series of analyses, we observed that the infiltration value of cancer-associated fibroblasts in ESCA, GBM, HNSC-HPV, LGG, PCPG, rectum adenocarcinoma (READ), TGCT, and THCA type tumors were positively correlated with the expression level of APOBEC3B, but negatively correlated with BRCA and HNSC-HPV+ tumor types (Fig. 4a). Moreover, we observed a statistically significant positive correlation between the immune infiltration of CD8+ T-cells and the expression level of APOBEC3B in HNSC0-HPV+ and UVM tumors based on most algorithms, while the ESCA tumor type was negatively correlated (Fig. 4b). Finally, as shown in Fig. 5c, we selected an algorithm to obtain a scatterplot of this tumor. For example, based on XCELL algorithm, the expression level of APOBEC3B in BRCA was negatively correlated with the infiltration level of cancer-associated fibroblasts (Fig. 5c , R = –0.316, P = 1.85e–24).

Fig. 4
figure 4

Correlation analysis between APOBEC3B expression and immune infiltration of cancer-associated fibroblasts and CD8+ T-cells. a Heatmap showing the correlation between the expression levels of the APOBEC3B and the infiltration level of cancer-associated fibroblasts/CD8+ T-cells obtained by different algorithms. b The correlation between APOBEC3B expression and immune infiltration cancer-associated fibroblasts in some TCGA-listed tumors

Fig. 5
figure 5

APOBEC3B-related gene enrichment analysis. a The APOBEC3B-binding proteins identified using the STRING tool. b The correlation between the expression of APOBEC3B and top 5 genes co-expression with APOBEC3B (TK1, MELK, CEP55, MCM2, and NCAPH). c Heatmap showing the correlation between the expression of APOBEC3B and top 5 genes co-expression with APOBEC3B (TK1, MELK, CEP55, MCM2, and NCAPH) in the detailed cancer types. d Venn diagram showing the intersection analysis of the APOBEC3B-binding and correlated genes. e Bubble chart of KEGG pathway analysis and GO enrichment analysis based on the APOBEC3B-binding and interacting genes

APOBEC3B-related gene enrichment analysis data

To further study how expression of the APOBEC3B gene affects tumorigenesis, we screened APOBEC3B-binding proteins and APOBEC3B expression-related genes, and carried out a series of pathway and function enrichment analyses. Through the STRING tool, we screened the top 50 APOBEC3B binding proteins based on text mining, experiments, databases, co-expression, Gene Fusion, and co-occurrence. Figure 5a shows the interaction network of these proteins. We then obtained the top 100 genes related to APOBEC3B expression based on all tumor expression data of TCGA using the GEPIA2 tool. As shown in Fig. 5b, the expression level of APOBEC3B was positively correlated with those for the genes encoding thymidine kinase 1 (TK1; R = 0.49), maternal embryonic leucine zipper kinase (MELK; R = 0.47), centrosomal protein 55 (CEP55; R = 0.46), minichromosome maintenance complex component 2 (MCM2; R = 0.46) and non-SMC condensin I complex subunit H (NCAPH; R = 0.45) (all P < 0.001). The corresponding heatmap data also showed that the expression level of APOBEC3B was positively correlated with the above five genes in most tumor types (Fig. 5c). The top 50 APOBEC3B-binding proteins and the top 100 APOBEC3B expression-related-genes were cross analyzed to obtain a common gene, namely that encoding karyopherin subunit alpha 2 (KPNA2; Fig. 5d).

To further clarify the function of APOBEC3B in tumors, we combined the top 50 APOBEC3B-binding proteins and the top 100 co-expression genes above for KEGG (www.kegg.jpkegg/kegg1.html) and GO enrichment analysis. KEGG data show that the terms ‘thermogenesis’, ‘non-alcoholic fatty liver disease’ and ‘oxidative phosphorylation’ might be involved in the mechanism of APOBEC3B affecting tumorigenesis. The GO enrichment analysis data showed that most of these genes are involved in or regulate the mitochondrial oxidative respiratory chain and its related pathways, such as the respiratory electron transport chain, ATP synthesis-coupled electron transport, mitochondrial ATP synthesis-coupled electron transport, and the mitochondrial respiratory chain among others. These data of KEGG and GO enrichment analysis have been visualized as a bubble plot (Fig. 5e).

Discussion

Gene mutations are closely related to the occurrence of tumors, and the sources that mediate mutation are divided into exogenous and endogenous types [27, 28]. Endogenous mutation sources involve enzymes acting in the DNA repair system, and there are reports that the mutation mode of APOBEC cytidine deaminases might play a role in carcinogenic somatic mutations and eventually lead to genomic instability [29, 30]. The human APOBEC protein family has seven members and are important members of the innate immune system [31]. Thus, APOBEC3B had the highest expression in bone marrow among normal tissues in our study. It not only participates in innate immunity, but also participates in a variety of biological processes in different cells. In particular, the APOBEC3B protein with cytosine deaminase activity can lead to base substitutions in tumor-causing genes [32, 33]. Many publications have reported that APOBEC3B is closely related to the occurrence and development of a variety of tumors [10, 13, 14, 34]. However, there is still a lack of in-depth research on whether APOBEC3B can play a role in different tumors through molecular mechanisms. We searched the literature of major databases and failed to retrieve any pan-cancer analysis publications on APOBEC3B from the perspective of tumors as a whole. Therefore, based on the TCGA and CPTAC databases, we comprehensively analyzed the gene expression, gene changes and immune infiltration of the APOBEC3B gene in 33 different tumors.

APOBEC3B proved to be highly expressed in most tumor tissues, but weakly in a few types, such as COAD, LAML, READ, THCA, and THYM. To minimize errors caused by individual differences in tumor data, we downloaded the RNAseq pan-cancer data in level 3 HTseq-FPKM format from the TCGA project, selected paired sample data, and then analyzed the differential expression of APOBEC3B through the ‘ggplot2’ R package. The results obtained are basically consistent with Fig. 1a (see also Additional file 2: Figure. S1c). We also found that in some tumors, the expression of APOBEC3B was mainly positively correlated with the tumor’s pathological stage, but in CHOL and OV tumors, the expression of APOBEC3B was negatively correlated with pathological stage. These results indicate that the expression trend of APOBEC3B is different in different tumors, and it may play a different role in their progression.

In the patient survival analysis, we found that the relationship between APOBEC3B expression and survival obtained by GEPIA2 was basically the same for OS and DFS. In most tumors, a high expression level of APOBEC3B indicated a poor prognosis, such as ACC, KIRP, LGG, LIHC, THCA, and UVM (but not in CESC). To further confirm the reliability of survival analysis data, we used Kaplan–Meier plotter tool for progression-free survival (PFS) analysis of the above cancers with different prognoses. The results (Additional file 2: Figure. S2) showed that high expression levels of APOBEC3B in ACC, LGG, LIHC, THCA and UVM were associated with poor PFS prognosis. It was associated with the low expression of APOBEC3B in CESC, but it was not statistically significant in KIRP. Thus, the expression level of APOBEC3B is closely linked to the prognosis and survival of patients in some tumor cases, but in different ways. Therefore, a larger sample size is needed to test the role of APOBEC3B in the survival and prognosis of different types of cancers.

Gene alterations play important roles in tumorigenesis. We found that an APOBEC3B gene alteration was associated with patient prognosis and survival in SKCM, and the prognosis for the gene-altered group was significantly worse than that of the unaltered group. However, in other tumors with an APOBEC3B gene alteration, we did not find an impact of gene alterations on patient prognosis and survival. Because of the small number of altered group cases for most tumors, we think it is necessary to further expand the data sample for verification in the future. In addition, we also analyzed the relationship between the expression levels of APOBEC3B and tumor mutational burden (TMB) and microsatellite instability (MSI) in all TCGA tumors. MSI often reflects a defect in the DNA mismatch repair system and the TMB reflects cancer mutation quantity. [35, 36] Additional file 2: Figure S3 shows that the expression level of APOBEC3B was positively correlated with the TMB of ACC, BLCA, BRCA, CESC, COAD, LIHC, LUAD, OV, PRAD, PAAD, SKCM, THCA, and THYM. The expression level of APOBEC3B was also negatively correlated with the MSI of BRCA, HNSC, KIRC, LIHC, STAD, and TGCT. These findings deserve further analysis.

Cancer-associated fibroblasts are considered to be a form of mutant cells with negative epithelial, endothelial and leukocyte markers, with slender morphology not found in cancer cells [37]. They have a variety of functions, including matrix deposition and remodeling, extensive signal interaction with cancer cells, and cross-talking with invasive leukocytes [38]. Therefore, they are potential factors affecting cancer treatment strategies. CD8+ cytotoxic T lymphocytes are immune cells that can target cancer, and tumor-associated fibroblasts, macrophage type 2 cells and regulatory T cells can form an immune barrier to the anti-tumor immune response mediated by such cells [39]. Our study is the first to suggest an association between the expression of APOBEC3B and the level of immune cell infiltration of tumor-associated fibroblasts and CD8+ T cells in some tumors. In addition, we integrated gene information related to APOBEC3B binding or affecting APOBEC3B expression in all tumors via the STRING and GEPIA2 databases, and conducted a variety of interaction, correlation and enrichment analyses. In addition, we integrated gene information related to APOBEC3B binding or affecting APOBEC3B expression in all tumors through a variety of databases, and conducted a variety of interaction, correlation and enrichment analyses (Table S2). Finally, we found that the GO terms ‘ATP synthesis-coupled electron transport’, ‘thermogenesis’ and ‘oxidative phosphorylation’ might be involved in the pathogenesis of cancer. Moreover, we screened for the KPNA2 gene based on two databases, and found that its expression was highly positively correlated with the expression level of APOBEC3B. KPNA2 is a member of the nuclear transport protein family. In many studies, KPNA2 has been linked to tumorigenesis (such as non-small cell lung cancer, breast cancers, and epithelial ovarian cancer), [40,41,42] and the gene is highly expressed in many cancerous tissues, which is consistent with the expression of the APOBEC3B gene in this study. It has a cancer promoting effect in in vivo and in vitro experiments, and is often related to a poor prognosis for patients [43]. We hypothesize that there might be some mechanism connecting APOBEC3B and KPNA2 that promotes the occurrence and development of tumors, which needs to be further tested by in vivo and in vitro experiments in the future.

Conclusions

Here we conducted pan-cancer analysis of APOBEC3B, explored the relationship between APOBEC3B expression levels and clinical prognosis, immune cell infiltration, TMB or MSI, as well as the mutation sites, interactive genes and involved pathways of the APOBEC3B gene. These findings indicate that APOBEC3B might have different biological functions in different cancer species. Their main significance lies in the need to take such differences into account when designing drugs targeted for individual genes (Additional file 3: Table S2).

Availability of data and materials

The datasets generated and analyzed during the current study are available in the TCGA repository, https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga.

Abbreviations

ACC:

Adrenocortical carcinoma

APOBEC3B :

Apolipoprotein B mRNA editing enzyme catalytic subunit 3B

BLCA:

Bladder urothelial carcinomas

BP:

Biological process

CC:

Cellular component

CEP55:

Centrosomal protein

CESE:

Cervical squamous cell carcinomas and endocervical adenocarcinomas

CHOL:

Cholangiocarcinomas

CNA:

Copy number alteration

COAD:

Colon adenocarcinomas

CPTAC:

Clinical proteomic tumor analysis consortium

DBLC:

Lymphoid neoplasm diffuse large B-cell lymphomas

DFS:

Disease-free survival

ESCA:

Esophageal carcinomas

GBM:

Glioblastoma multiforme

GEPIA:

Gene expression profiling interactive analysis

GO:

Gene ontology

GTE:

Genotype-tissue expression

HNSC:

Head and neck squamous carcinomas

HPV:

Human papilloma virus

KEGG:

Kyoto encyclopedia of genes and genomes

KICH:

Kidney chromophobes

KIRC:

Kidney renal clear cell carcinomas

KIRP:

Kidney renal papillary cell carcinomas

KPNA2:

Karyopherin subunit alpha 2

LAML:

Acute myeloid leukemia

LGG:

Brain lower grade gliomas

LIHC:

Liver hepatocellular carcinomas

LUAD:

Lung adenocarcinomas

LUSC:

Lung squamous cell carcinomas

MCM2:

Minichromosome maintenance complex component 2

MELK:

Maternal embryonic leucine zipper kinase

MESO:

Mesotheliomas

MF:

Molecular function

MSI:

Microsatellite instability

NCAPH:

Non-SMC condensin I complex subunit H

OV:

Ovarian cancers

PAAD:

Pancreatic adenocarcinomas

PCPG:

Pheochromocytomas and paragangliomas

PFS:

Progression-free survival

PRAD:

Prostate adenocarcinomas

READ:

Rectum adenocarcinoma

SKCM:

Skin cutaneous melanomas

STAD:

Stomach adenocarcinomas

TCGA:

The cancer genome atlas

TGCT:

Testicular germ cell tumors

THCA:

Thyroid carcinomas

THYM:

Thymomas

TIMER:

Tumor immune estimation resource

TK1:

Thymidine kinase 1

TMB:

Tumor mutational burden. TPM: transcripts per million reads

UCEC:

Uterine corpus endometrial carcinomas

UCS:

Uterine carcinosarcomas

UVM:

Uveal melanomas

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics 2021. CA Cancer J Clin. 2021;71(1):7–33. https://doi.org/10.3322/caac.21654.

    Article  PubMed  Google Scholar 

  2. Rahib L, Wehner MR, Matrisian LM, Nead KT. Estimated projection of US Cancer incidence and death to 2040. JAMA Netw Open. 2021;4(4): e214708. https://doi.org/10.1001/jamanetworkopen.2021.4708.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ganini C, Amelio I, Bertolo R, et al. Global mapping of cancers: the cancer genome atlas and beyond. Mol Oncol. 2021;15(11):2823–40. https://doi.org/10.1002/1878-0261.13056.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Tomczak K, Czerwinska P, Wiznerowicz M. The cancer genome atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol. 2015;19(1A):A68–77. https://doi.org/10.5114/wo.2014.47136.

    Article  Google Scholar 

  5. Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 2013. https://doi.org/10.1093/nar/gks1193.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Yang B, Li X, Lei L, Chen J. APOBEC: from mutator to editor. J Genet Genomics. 2017;44(9):423–37. https://doi.org/10.1016/j.jgg.2017.04.009.

    Article  PubMed  Google Scholar 

  7. Dunham I, Shimizu N, Roe BA, et al. The DNA sequence of human chromosome 22. Nature. 1999;402(6761):489–95. https://doi.org/10.1038/990031.

    CAS  Article  PubMed  Google Scholar 

  8. Jarmuz A, Chester A, Bayliss J, et al. An anthropoid-specific locus of orphan C to U RNA-editing enzymes on chromosome 22. Genomics. 2002;79(3):285–96. https://doi.org/10.1006/geno.2002.6718.

    CAS  Article  PubMed  Google Scholar 

  9. Wedekind JE, Dance GS, Sowden MP, Smith HC. Messenger RNA editing in mammals: new members of the APOBEC family seeking roles in the family business. Trends Genet. 2003;19(4):207–16. https://doi.org/10.1016/S0168-9525(03)00054-4.

    CAS  Article  PubMed  Google Scholar 

  10. Burns MB, Lackey L, Carpenter MA, et al. APOBEC3B is an enzymatic source of mutation in breast cancer. Nature. 2013;494(7437):366–70. https://doi.org/10.1038/nature11881.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Wu PF, Chen YS, Kuo TY, Lin HH, Liu CW, Chang LC. APOBEC3B: a potential factor suppressing growth of human hepatocellular carcinoma cells. Anticancer Res. 2015;35(3):1521–7.

    PubMed  Google Scholar 

  12. Du Y, Tao X, Wu J, Yu H, Yu Y, Zhao H. APOBEC3B up-regulation independently predicts ovarian cancer prognosis: a cohort study. Cancer Cell Int. 2018;18:78. https://doi.org/10.1186/s12935-018-0572-5.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. Feng C, Zhang Y, Huang J, Zheng Q, Yang Y, Xu B. The prognostic significance of APOBEC3B and PD-L1/PD-1 in nasopharyngeal carcinoma. Appl Immunohistochem Mol Morphol. 2021;29(3):239–44. https://doi.org/10.1097/PAI.0000000000000852.

    CAS  Article  PubMed  Google Scholar 

  14. Jin Z, Han YX, Han XR. The role of APOBEC3B in chondrosarcoma. Oncol Rep. 2014;32(5):1867–72. https://doi.org/10.3892/or.2014.3437.

    CAS  Article  PubMed  Google Scholar 

  15. Li T, Fu J, Zeng Z, et al. TIMER20 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509–14. https://doi.org/10.1093/nar/gkaa407.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  16. Li T, Fan J, Wang B, et al. TIMER: A web server for comprehensive analysis of Tumor-Infiltrating immune cells. Cancer Res. 2017;77(21):e108–10. https://doi.org/10.1158/0008-5472.CAN-17-0307.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Li B, Severson E, Pignon JC, et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 2016;17(1):174. https://doi.org/10.1186/s13059-016-1028-7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Goldman MJ, Craft B, Hastie M, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38(6):675–8. https://doi.org/10.1038/s41587-020-0546-8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Vivian J, Rao AA, Nothaft FA, et al. Toil enables reproducible, open source, big biomedical data analyses. Nat Biotechnol. 2017;35(4):314–6. https://doi.org/10.1038/nbt.3772.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: An enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556–60. https://doi.org/10.1093/nar/gkz430.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Chandrashekar DS, Bashel B, Balasubramanya S, et al. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017;19(8):649–58. https://doi.org/10.1016/j.neo.2017.05.002.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 2013;6(269): l1. https://doi.org/10.1126/scisignal.2004088.

    CAS  Article  Google Scholar 

  23. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–13. https://doi.org/10.1093/nar/gky1131.

    CAS  Article  PubMed  Google Scholar 

  24. Jiao X, Sherman BT, Huang DW, et al. DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics. 2012;28(13):1805–6. https://doi.org/10.1093/bioinformatics/bts251.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Burns MB, Temiz NA, Harris RS. Evidence for APOBEC3B mutagenesis in multiple human cancers. Nat Genet. 2013;45(9):977–83. https://doi.org/10.1038/ng.2701.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Sokratous G, Polyzoidis S, Ashkan K. Immune infiltration of tumor microenvironment following immunotherapy for glioblastoma multiforme. Hum Vaccin Immunother. 2017;13(11):2575–82. https://doi.org/10.1080/21645515.2017.1303582.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Shinohara M, Io K, Shindo K, et al. APOBEC3B can impair genomic stability by inducing base substitutions in genomic DNA in human cells. Sci Rep. 2012;2:806. https://doi.org/10.1038/srep00806.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Kuong KJ, Loeb LA. APOBEC3B mutagenesis in cancer. Nat Genet. 2013;45(9):964–5. https://doi.org/10.1038/ng.2736.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Barbieri CE, Baca SC, Lawrence MS, et al. Exome sequencing identifies recurrent SPOP, FOXA1 and MED12 mutations in prostate cancer. Nat Genet. 2012;44(6):685–9. https://doi.org/10.1038/ng.2279.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  30. Stransky N, Egloff AM, Tward AD, et al. The mutational landscape of head and neck squamous cell carcinoma. Science. 2011;333(6046):1157–60. https://doi.org/10.1126/science.1208130.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. Pak V, Heidecker G, Pathak VK, Derse D. The role of amino-terminal sequences in cellular localization and antiviral activity of APOBEC3B. J Virol. 2011;85(17):8538–47. https://doi.org/10.1128/JVI.02645-10.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. Prohaska KM, Bennett RP, Salter JD, Smith HC. The multifaceted roles of RNA binding in APOBEC cytidine deaminase functions. Wiley Interdiscip Rev RNA. 2014;5(4):493–508. https://doi.org/10.1002/wrna.1226.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. Bacolla A, Cooper DN, Vasquez KM. Mechanisms of base substitution mutagenesis in cancer genomes. Genes. 2014;5(1):108–46. https://doi.org/10.3390/genes5010108.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. Vio CP, Martinez L, Gandolfi C, Leniz P. Effect of cyclosporine a on the distal nephron: the use of kallikrein as a specific morphological marker. Transplant Proc. 1990;22(4):1730–2.

    CAS  PubMed  Google Scholar 

  35. Yang G, Zheng RY, Jin ZS. Correlations between microsatellite instability and the biological behaviour of tumours. J Cancer Res Clin Oncol. 2019;145(12):2891–9. https://doi.org/10.1007/s00432-019-03053-4.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Jardim DL, Goodman A, de Melo GD, Kurzrock R. The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell. 2021;39(2):154–73. https://doi.org/10.1016/j.ccell.2020.10.001.

    CAS  Article  PubMed  Google Scholar 

  37. Sahai E, Astsaturov I, Cukierman E, et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat Rev Cancer. 2020;20(3):174–86. https://doi.org/10.1038/s41568-019-0238-1.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Olumi AF, Grossfeld GD, Hayward SW, Carroll PR, Tlsty TD, Cunha GR. Carcinoma-associated fibroblasts direct tumor progression of initiated human prostatic epithelium. Cancer Res. 1999;59(19):5002–11. https://doi.org/10.1186/bcr138.

    CAS  Article  PubMed  Google Scholar 

  39. Farhood B, Najafi M, Mortezaee K. CD8(+) cytotoxic T lymphocytes in cancer immunotherapy: a review. J Cell Physiol. 2019;234(6):8509–21. https://doi.org/10.1002/jcp.27782.

    CAS  Article  PubMed  Google Scholar 

  40. Wang CI, Chien KY, Wang CL, et al. Quantitative proteomics reveals regulation of karyopherin subunit alpha-2 (KPNA2) and its potential novel cargo proteins in nonsmall cell lung cancer. Mol Cell Proteomics. 2012;11(11):1105–22. https://doi.org/10.1074/mcp.M111.016592.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  41. Noetzel E, Rose M, Bornemann J, Gajewski M, Knuchel R, Dahl E. Nuclear transport receptor karyopherin-alpha2 promotes malignant breast cancer phenotypes in vitro. Oncogene. 2012;31(16):2101–14. https://doi.org/10.1038/onc.2011.403.

    CAS  Article  PubMed  Google Scholar 

  42. Zheng M, Tang L, Huang L, et al. Overexpression of karyopherin-2 in epithelial ovarian cancer and correlation with poor prognosis. Obstet Gynecol. 2010;116(4):884–91. https://doi.org/10.1097/AOG.0b013e3181f104ce.

    CAS  Article  PubMed  Google Scholar 

  43. Han Y, Wang X. The emerging roles of KPNA2 in cancer. Life Sci. 2020;241: 117140. https://doi.org/10.1016/j.lfs.2019.117140.

    CAS  Article  PubMed  Google Scholar 

Download references

Acknowledgements

We appreciate Xiantao (https://www.xiantao.love/) for providing technical guidance for this manuscript.

Funding

This work was supported by grants from the Zhejiang Province Basic Public Welfare Projects (GF22H011488); Ningbo Natural Science Foundation (2021J288, 202003N4269); Ningbo Medical Science and Technology Plan (2020Y01).

Author information

Authors and Affiliations

Authors

Contributions

J. Wu wrote the main manuscript text; J.Wu, N. Li, M. Li, and H. Chen conducted data collection and software processing; J. Wu, L. Zhu, M. Ye, and Y. Wei prepared Figs. 1, 2, 3, 4, 5; D. Zhen and G. Shao prepared supplementary materials; All authors read and approved the final manuscript..

Corresponding author

Correspondence to Guofeng Shao.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors report no competing interests in this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

. Clinical prognostic significance of DNA methylation of APOBEC3B.

Additional file 2

. An overview of apobec3b and the relationship between its expression and PFS or TMB.

Additional file 3

. GO and KEGG enrichment analysis of APOBEC3B related genes.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, J., Li, N., Zhu, L. et al. Multiple roles of apolipoprotein B mRNA editing enzyme catalytic subunit 3B (APOBEC3B) in human tumors: a pan-cancer analysis. BMC Bioinformatics 23, 312 (2022). https://doi.org/10.1186/s12859-022-04862-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12859-022-04862-0

Keywords

  • APOBEC3B
  • Oncogenic role
  • Pan-cancer analysis