Abstract
Cancer is not rare anywhere in the world now, and the global burden of cancer continues to increase largely every year. Previous research on infections and cancers reported that, about 17.8 % of the cancers worldwide, which are over 1.9 million cases of cancer, are related to viral infections. At least six oncoviruses, cancer-causing viruses, have been known so far, which include hepatitis B virus, hepatitis C virus, Epstein–Barr virus (EBV or HHV-4), human papillomavirus, human T lymphotropic virus type 1, Kaposi’s sarcoma-associated herpesvirus (KSHV or HHV-8), but the pathogenic mechanism is far from being completely understood. In this study, assuming that finding human proteins significantly similar to viral oncoproteins leads to a categorization of the cancer-related pathways that are currently not clearly known, we analyzed different types of virus-caused cancers based on their similarity in order to clarify the unknown cancer mechanisms. As a result, we obtained several potential tumor pathways that may be significant and essential in oncogenic cancer process, which will be helpful for further study on cancer mechanisms and the development of new drug targets.
Electronic supplementary material
The online version of this article (doi:10.1007/s11693-014-9141-y) contains supplementary material, which is available to authorized users.
Keywords: Cancer, Oncovirus, Pathway, Protein–protein interaction (PPI)
Introduction
Cancer (malignant neoplasm) is a class of diseases in which a cell or a group of cells display uncontrolled growth (division beyond the normal limits), invasion (intrusion on and destruction of adjacent tissues), and sometimes metastasis (spread to other locations in the body via lymph or blood), and it could also be considered as a class of diseases with abnormal signal transductions. Cancer is not rare anywhere in the world now, and the global burden of cancer continues to increase largely every year. Based on the estimates of the worldwide incidence and mortality from 27 cancers in 182 countries by the GLOBOCAN series, about 12.7 million cancer cases and 7.6 million cancer deaths have occurred in 2008 (Ferlay et al. 2010).
Previous research on infections and cancers reported that, about 17.8 % of the cancers worldwide, which are about 1.9 million cases, are related to viral infection (Parkin 2006). At least six oncoviruses, cancer-causing viruses, have been known to contribute to human malignancies so far, which include hepatitis B virus (HBV), hepatitis C virus (HCV), Epstein–Barr virus (EBV or HHV-4), human papillomavirus (HPV), human T lymphotropic virus type 1 (HTLV-1), Kaposi’s sarcoma-associated herpesvirus (KSHV or HHV-8) (Parkin 2006). Previous studies have elucidated that these oncoviruses and the viral oncoproteins can alter multiple cellular pathways and operate at various points in the signaling cascades promoting cell growth and proliferation in human body leading to cancer, but the pathogenic mechanism is far from being completely understood (Weinberg 2006).
Based on the past studies on viral mimicry, it has been reported that one of the strategies for viruses to evade detection and destruction by the host immune system is to encode homologs of cytokines, chemokines and their receptor molecules that have a crucial role in control of the immune response. In addition, viruses can also capture host genes or evolved genes to target specific immune pathways (Alcami 2003). For example, the KSHV gene ORF K9 encodes viral interferon regulatory factors 1 (vIRF-1) which is a protein with low but significant homology to members of the interferon (IFN) regulatory factor (IRF) family responsible for regulating intracellular IFN signal transduction (Moore et al. 1996). The vIRF-1 functions as a repressor of IFN and IRF1-mediated signal transduction and also acts as an oncogene to induce cellular transformation (Seo et al. 2001). Besides, it can modulate the transcription of the IFNA genes according to direct heterodimerization with members of the IRF family, and by competitive binding with cellular transcription factors to the carboxy-terminal region of p300, which could also bind IRF-3 and IRF-1 (Seo et al. 2001; Burýsek et al. 1999). Moreover, a study showed that the EBV protein called latent infection membrane protein 1 (LMP1) can act as a constitutively active receptor that mimics activated CD40, which is a member of the tumor necrosis factor receptor family (Mosialos et al. 1995). Also there is another research indicated the E6 oncoprotein of HPV16 has a potential zinc finger domain critical for binding to E6AP ubiquitin-protein ligase which is an enzyme involved in targeting proteins for degradation within human cells, causing p53 degradation and malignancy (Choudhury et al. 2014).
Besides the experimental researches, there are also computational studies on the prediction of candidate disease genes based on similarity to known disease genes or phenotypes. For example, POCUS (Turner et al. 2003) searches candidate genes by identifying an enrichment of keywords associated with GO, shared InterPro domains (Mulder et al. 2005) and expression profiles among a given set of susceptibility loci relative to the genome at large. Moreover, the Common Pathway Scanning (CPS) applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes as well as the Common Module Profiling (CMP) identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype (George et al. 2006).
According to these observations, we assumed that some viral oncoproteins might pretend to work as similar human proteins in the important human pathways, or that those human proteins might work in a similar way as the harmful viral oncoproteins and would be perturbed easily by viruses. Therefore, in this study, we analyzed the possible functions of the viral oncoproteins in human pathways, i.e., categorized the human proteins and pathways that would be perturbed by oncoviruses. It is quite difficult to predict the function of viral oncoproteins by looking for significant homologs, so we chose to use the domain search and obtained the human proteins that contain similar domains with the viral oncoproteins. Since those virus domains have been proved to be related with tumor process according to literatures (McLaughlin-Drubin and Munger 2008; Moore and Chang 2010; Boccardo and Villa 2007), and many proteins that have similar sequences or domains show similar functions, there is a chance that the obtained human proteins would play a role in the cancer mechanism.
Materials
KEGG DISEASE database
We collected the published data on oncogenic cancers (including the information of oncoviruses and viral oncoproteins) from the KEGG DISEASE database (Kanehisa et al. 2010). This database contains disease information capturing knowledge on genetic and environmental perturbations, and each disease entry contains a list of known genetic factors (disease genes), environmental factors, diagnostic markers, and therapeutic drugs.
KEGG GENES database
KEGG GENES is a collection of gene catalogs for all complete genomes generated from publicly available resources (Kanehisa et al. 2010), mostly NCBI RefSeq (Pruitt et al. 2014), and it provided the human proteins data in this study.
HMMER and Pfam
HMMER, a software suite for protein sequence similarity searches using probabilistic methods (Durbin et al. 1998), is used for searching human proteins that contain similar domains with viral oncoproteins.
The Pfam database, a large collection of protein families (Finn et al. 2010), has been applied for domain search. Pfam consists of two components Pfam-A and Pfam-B. Pfam-A entries are derived from the underlying sequence database, which is built from the most recent release of UniProtKB at a given time-point. Each Pfam-A family consists of a curated seed alignment containing a small set of representative members of the family, profile hidden Markov models (profile HMMs) built from the seed alignment, and an automatically generated full alignment, which contains all detectable protein sequences belonging to the family, as defined by profile HMM searches of primary sequence databases, Pfam-B families are un-annotated and of lower quality as they are generated automatically from the non-redundant clusters of the latest ADDA release (Heger and Holm 2003). We only used Pfam-A in this study because of the high quality due to the manually curated families.
KEGG Mapper
Human proteins sharing similar domains with viral oncoproteins were mapped onto the KEGG pathway diagrams in order to mark the location of the possible perturbed human proteins and to categorize the potential cancer pathways by the KEGG Mapper, which is a mapping tool for the KEGG pathway and the BRITE functional hierarchy (a collection of manually created hierarchical text files capturing functional hierarchies of various of biological objects) (Kanehisa et al. 2010).
BIND database
Considering the limited number of human homologs to viral oncoproteins, the related PPIs data was also collected and involved in the results. We obtained PPI data from the database BIND (bimolecular interaction network database), which is a database designed to store full descriptions of interactions, molecular complexes and pathways (Bader et al. 2001).
Methods
An overview of the workflow for the method is shown in Fig. 1. We first thoroughly checked the published references on virus-caused cancers from KEGG DISEASE database mainly to find the carcinogen proteins. When the virus causing a cancer and the related viral oncoproteins were clearly described in references, the information on the cancer was collected and used in this study.
Fig. 1.
Workflow for categorization of potential cancer pathways
Then the viral oncoprotein sequences were used to obtain involved virus domains with several e-values from Pfam-A database (Fig. 2). Because the numbers of result domains was not affected by the thresholds below 10−2, the recommended threshold 10−5 was used to collect the significant results.
Fig. 2.
E-values and the numbers of domains obtained from Pfam-A database. X-axis shows the six e-values tested in the study from 10−5 to 1, Y-axis shows the numbers of domains obtained from Pfam-A database
Since we only focused on human proteins in the HMMER search, all the human proteins data was extracted from KEGG GENES and made into a database called “hsa-database”.
The HMMER search was performed by using hmmscan, which is one of the tools provided in the HMMER tool box for comparing protein sequences to databases with Hidden Markov Models that represent protein families and domains. We searched human proteins from “hsa-database” for the virus domains obtained from Pfam-A database under different e-values (Fig. 3). Considering the limited data of human homologs to viral oncoproteins, we chose e-value as 0.1 to collect more results for further analysis.
Fig. 3.
E-values and the numbers of human proteins extracted by HMMER. X-axis shows the six e-values tested in the study from 10−5 to 1, Y-axis shows the numbers of proteins extracted by HMMER
After the human proteins sharing similar domains with viral oncoproteins were obtained, they were mapped onto the KEGG pathway maps by the KEGG Mapper to categorize potential cancer pathways. Then we analyzed the resulting pathways and functions of the hit proteins to distinguish the potentials in cancer process with the support of the literatures. Additionally, PPI data of the human proteins provided more related interaction human proteins, which have been involved into the mapping and pathway analysis, but the common PPIs between BIND and KEGG pathways were removed in order to avoid the redundant mapping results.
Results and discussions
We collected 12 types of virus-caused cancers that have been registered in the KEGG DISEASE database and found that most of them had clear references for the oncoviruses and carcinogen proteins. Ten types of oncoviruses as well as ten viral oncoproteins were selected for the following analysis in this study (Table 1). It was obvious that some oncoviruses can cause various types of cancers, also different kinds of viruses can lead to the same disease in diverse mechanisms.
Table 1.
Oncoviruses caused cancers
| Cancer | Oncovirus | Viral oncoprotein |
|---|---|---|
| Penile cancer | Human papillomavirus types 16 and 18 (HPV16,18) | Early protein 6 (E6), early protein 7(E7) |
| Vulvar cancer | Human papillomavirus types 16 and 18 (HPV16,18) | Early protein 6 (E6), early protein 7(E7) |
| Cervical cancer | Human papillomavirus types 16 and 18 (HPV16,18) | Early protein 6 (E6), early protein 7(E7) |
| Cancer of the anal canal | Human papillomavirus types 16 and 18 (HPV16,18) | Early protein 6 (E6), early protein 7(E7) |
| Kaposi’s sarcoma | Kaposi’s sarcoma-associated herpesvirus (KSHV or HHV-8) | Viral interferon regulatory factors-1,2,3(v-IRF-1, 2, 3) |
| Hodgkin lymphoma | Epstein-Barr virus (EBV) | Epstein-Barr nuclear antigen (EBNA), latent membrane proteins (LMP) |
| Burkitt lymphoma | Epstein-Barr virus (EBV) | Epstein-Barr nuclear antigen(EBNA), Latent membrane proteins (LMP) |
| Nasopharyngeal cancer | Epstein-Barr virus (EBV) | Epstein-Barr nuclear antigen(EBNA), Latent membrane proteins (LMP) |
| Malignant pleural mesothelioma | Simian virus 40 (SV40) | SV40 Large T-antigen |
| Hepatocellular carcinoma | Hepatitis B virus (HBV), hepatitis C virus (HCV) | Hepatitis B viral protein (HBcAg) |
| Adult T-cell leukemia | Human T-lymphotropic virus 1 (HTLV-1) | Tax |
| Squamous cell carcinoma | Human papillomavirus types 8,18,5 (HPV8,18,5) | No clear reference |
Then all the human proteins that contain similar domains with viral oncoproteins were extracted by e-value 0.1 and obtained PPI data from BIND database. Then KEGG Mapper was used to map all the extracted human proteins and PPI-obtained human proteins onto the KEGG pathway maps in order to locate the possible human proteins, which would be perturbed by the oncogenic viruses easily or would work for the tumor process, and also to categorize the potential cancer-related pathways.
As a result, parts of the obtained human proteins were mapped onto the current pathway maps, and the pathways could be categorized for most kinds of oncogenic cancers as potential tumor process. There are total 53 human proteins extracted by HMMER searches and 17 of them can be mapped onto KEGG pathway maps, as well as 19 human proteins extracted by PPI from BIND database with 16 of them can be mapped. Table 2 summarized the categorization results of human proteins for each cancer. In addition, there was one PPI-obtained protein appearing in the results of both Hodgkin lymphoma cancer group and malignant pleural mesothelioma, so the total number of human proteins extracted by PPI is 19 which is not contradictory from the data in Table 2.
Table 2.
Categorization results of human proteins for each cancer
| Cancer | Mapped human proteins extracted by HMMER | Unmapped human proteins extracted by HMMER | Mapped human proteins extracted by PPI | Unmapped human proteins extracted by PPI |
|---|---|---|---|---|
| Penile cancer, vulvar cancer, cervical cancer, cancer of the anal canal | 5 | 4 | 2 | 2 |
| Kaposi’s sarcoma | 6 | 7 | 11 | 2 |
| Hodgkin lymphoma, Burkitt lymphoma, nasopharyngeal cancer | 3 | 13 | 1 | 0 |
| Malignant pleural mesothelioma | 0 | 3 | 1 | 0 |
| Hepatocellular carcinoma | 3 | 9 | 1 | 0 |
| Adult T-cell leukemia | 0 | 0 | 0 | 0 |
| Squamous cell carcinoma | 0 | 0 | 0 | 0 |
Moreover, we hypothesize that the more human proteins marked in a pathway may indicate the more possibility that the pathway is significant to the tumor process. Figure 4 provided the relationships between the numbers of hit proteins and the numbers of result pathways. Considering the large numbers of pathways with one hit protein as well as the limited effect that one protein would give to the whole pathway according to our hypothesis, this study only focused on the pathways that contain at least two hit proteins.
Fig. 4.
Numbers of obtained pathways and hit proteins. X-axis stands for the minimum numbers of hit proteins in result pathways. Y-axis shows the numbers of result pathways. For example, there are over 120 result pathways containing at least one hit protein, and over 40 pathways involve no less than two hit proteins
We will show the details about the viral oncoproteins, associated Pfam virus protein domains, and the pathways obtained by mapping extracted human proteins and PPI data in the following sections for each cancer.
Penile cancer, vulvar cancer, cervical cancer and cancer of the anal canal
These four cancers are caused by the same oncovirus and viral oncoproteins. We extracted nine human proteins having the significantly similar domains to viral domains from viral oncoproteins and five of them were mapped onto pathways maps. We also obtained from PPI data four more human proteins, two of which were mapped. Table 3 shows the details of the results for penile cancer, vulvar cancer, cervical cancer and cancer of the anal canal.
Table 3.
Results for vulvar cancer, cervical cancer and cancer of the anal canal
| Penile cancer, vulvar cancer, cervical cancer and cancer of the anal canal | ||
|---|---|---|
| Viral oncoproteins | Early protein 6 (E6), early protein 7(E7) | |
| Associated Pfam domains | Early protein | |
| Human proteins mapped to KEGG pathway extracted by HMMER | 5 | RPL38; ribosomal protein L38 |
| hsa03010 ribosome | ||
| RPS27; ribosomal protein S27 | ||
| hsa03010 ribosome | ||
| CXCR6; chemokine (C-X-C motif) receptor 6 | ||
| hsa04060 cytokine-cytokine receptor interaction | ||
| hsa04062 chemokine signaling pathway | ||
| IL10; interleukin 10 | ||
| hsa04060 cytokine–cytokine receptor interaction | ||
| hsa05330 allograft rejection | ||
| hsa05169 Epstein-Barr virus infection | ||
| hsa05133 Pertussis | ||
| hsa05322 systemic lupus erythematosus | ||
| hsa05310 asthma | ||
| hsa05150 staphylococcus aureus infection | ||
| hsa05320 autoimmune thyroid disease | ||
| hsa05142 Chagas disease (American trypanosomiasis) | ||
| hsa05144 malaria | ||
| hsa04672 intestinal immune network for IgA production | ||
| hsa05140 leishmaniasis | ||
| hsa05145 toxoplasmosis | ||
| hsa05146 amoebiasis | ||
| hsa04630 Jak-STAT signaling pathway | ||
| hsa05152 tuberculosis | ||
| hsa05143 African trypanosomiasis | ||
| hsa04660 T cell receptor signaling pathway | ||
| hsa05321 Inflammatory bowel disease (IBD) | ||
| YOD1; YOD1 deubiquitinase (EC:3.4.19.12) | ||
| hsa04141 Protein processing in endoplasmic reticulum | ||
| Human proteins mapped to KEGG pathway extracted by PPI | 2 | IL10RA; interleukin 10 receptor, alpha |
| hsa04060 cytokine–cytokine receptor interaction | ||
| hsa05169 Epstein-Barr virus infection | ||
| hsa05145 toxoplasmosis | ||
| hsa04630 Jak-STAT signaling pathway | ||
| hsa05152 tuberculosis | ||
| PXN; paxillin | ||
| hsa04062 chemokine signaling pathway | ||
| hsa05100 bacterial invasion of epithelial cells | ||
| hsa05203 viral carcinogenesis | ||
| hsa04670 leukocyte transendothelial migration | ||
| hsa05205 proteoglycans in cancer | ||
| hsa04810 regulation of actin cytoskeleton | ||
| hsa04370 VEGF signaling pathway | ||
| hsa04510 focal adhesion | ||
Here, seven pathways with at least two hit proteins are selected for the following analysis. All the obtained pathways begin with “hsa” as human pathway IDs. Numbers in parentheses and the following lines show the number of hit proteins contained in this pathway and the details of the hit proteins, respectively.
-
hsa04060 Cytokine-cytokine receptor interaction (3)
CXCR6; chemokine (C-X-C motif) receptor 6
IL10; interleukin 10
IL10RA; interleukin 10 receptor, alpha
-
hsa04062 Chemokine signaling pathway (2)
CXCR6; chemokine (C-X-C motif) receptor 6
PXN; paxillin
-
hsa05169 Epstein–Barr virus infection (2)
IL10; interleukin 10
IL10RA; interleukin 10 receptor, alpha
-
hsa05145 Toxoplasmosis (2)
IL10; interleukin 10
IL10RA; interleukin 10 receptor, alpha
-
hsa04630 Jak-STAT signaling pathway (2)
IL10; interleukin 10
IL10RA; interleukin 10 receptor, alpha
-
hsa05152 Tuberculosis (2)
IL10; interleukin 10
IL10RA; interleukin 10 receptor, alpha
-
hsa03010 Ribosome (2)
RPL38; ribosomal protein L38
RPS27; ribosomal protein S27
The hsa04060 pathway is more a collection of cytokine–cytokine receptors than a pathway, and CXCR6 and IL10 are found in separate paths. It is therefore difficult to make much sense in the context of signaling and metabolic network. Similarly, the hsa03010 pathway is more a protein complex than a pathway, and we considered it inadequate to mark the relationship with tumor process. Therefore, we concluded that the two pathways are not significant in terms of potential cancer pathways.
Among the remaining five pathways, four contained interleukin 10 (IL10) and interleukin 10 receptor alpha (IL10RA), and their interaction is a normal process according to the BIND database as well as pathway hsa04630, Jak-STAT signaling pathway. Since IL-10 may play a key role in maintenance of regulatory T cells and explains the immunosuppressive state of cervical cancer (Bhairavabhotla et al. 2007), the pathways involving this interaction could be a possible evidence for tumor process.
The other pathway hsa04062 Chemokine signaling pathway was worth further analysis because the obtained human protein chemokine (C-X-C motif) receptor 6 (CXCR6) seems to play the critical role in the whole pathway according to Fig. 5, and would affect the cell survival, apoptosis and other processes if any problems occur here. It has been studied that the expression of CXCR6 was associated significantly with lymph node metastasis. In Kaplan–Meier analysis, patients with high CXCR6 expression had significantly lower overall survival than did those with low CXCR6 expression (Huang et al. 2013). The elevated co-expression levels of CXCL12/CXCR4 and CXCL16/CXCR6 in cervical intraepithelial neoplasia (CIN) and cervical carcinoma suggest a durative process in cervical carcinoma evolution, and CXCR6 may be useful as a biomarker and a valuable prognostic factor for cervical cancer (Huang et al. 2013). According to our hypothesis, the high expression of CXCR6 in the cervical cancer process could be inferred as an advantage of the HPV infection, because more CXCR6 might be perturbed by viral oncoproteins easily and lead to tumor. Moreover, some studies mentioned that down-regulated expression and activity of several chemokine signaling pathways have been implicated in cancer progression (Hembruff and Cheng 2009). Therefore, not only the hit protein CXCR6 but also the hsa04062 Chemokine signaling pathway is probably potential for the cervical cancer process.
Fig. 5.
Chemokine signaling pathway map created based on KEGG pathways. The obtained protein CXCR6 is colored in blue while another hit protein PXN is colored in red. (Color figure online)
Kaposi’s sarcoma
Thirteen human proteins containing the significantly similar domains to viral oncoprotein domains have been obtained for Kaposi’s sarcoma and six of them were mapped onto pathways, besides, 13 more human proteins were obtained from PPI data and 11 of them were mapped. The details of the results for Kaposi’s sarcoma are shown in Table S1 (see Supplement 1). The following 30 pathways were obtained for further analysis:
hsa05164 Influenza A—Homo sapiens (human) (7)
hsa05168 Herpes simplex infection—Homo sapiens (human) (7)
hsa05161 Hepatitis B—Homo sapiens (human) (6)
hsa05162 Measles—Homo sapiens (human) (6)
hsa04620 Toll-like receptor signaling pathway—Homo sapiens (human) (6)
hsa05160 Hepatitis C—Homo sapiens (human) (6)
hsa05133 Pertussis—Homo sapiens (human) (5)
hsa05169 Epstein–Barr virus infection—Homo sapiens (human) (4)
hsa05203 Viral carcinogenesis—Homo sapiens (human) (4)
hsa04350 TGF-beta signaling pathway—Homo sapiens (human) (4)
hsa04380 Osteoclast differentiation—Homo sapiens (human) (4)
hsa04622 RIG-I-like receptor signaling pathway—Homo sapiens (human) (4)
hsa05142 Chagas disease (American trypanosomiasis)—Homo sapiens (human) (3)
hsa04144 Endocytosis—Homo sapiens (human) (3)
hsa04630 Jak-STAT signaling pathway—Homo sapiens (human) (3)
hsa05152 Tuberculosis—Homo sapiens (human) (3)
hsa04060 Cytokine-cytokine receptor interaction—Homo sapiens (human) (3)
hsa04623 Cytosolic DNA-sensing pathway—Homo sapiens (human) (3)
hsa05200 Pathways in cancer—Homo sapiens (human) (3)
hsa04010 MAPK signaling pathway—Homo sapiens (human) (2)
hsa05140 Leishmaniasis—Homo sapiens (human) (2)
hsa04064 NF-kappa B signaling pathway—Homo sapiens (human) (2)
hsa05166 HTLV-I infection—Homo sapiens (human) (2)
hsa04390 Hippo signaling pathway—Homo sapiens (human) (2)
hsa04062 Chemokine signaling pathway—Homo sapiens (human) (2)
hsa05016 Huntington’s disease—Homo sapiens (human) (2)
hsa05145 Toxoplasmosis—Homo sapiens (human) (2)
hsa04950 Maturity onset diabetes of the young—Homo sapiens (human) (2)
hsa04520 Adherens junction—Homo sapiens (human) (2)
hsa04120 Ubiquitin mediated proteolysis—Homo sapiens (human) (2)
Except hsa05203 Viral carcinogenesis, hsa04060 Cytokine-cytokine receptor interaction, hsa05200 Pathways in cancer, hsa04120 Ubiquitin mediated proteolysis, and hsa04144 Endocytosis, all of which are collections of pathways and difficult to interpret as a protein interaction network, the obtained pathways are worth further analysis and some of them already show the relationships with cancers, such as hsa05169 Epstein–Barr virus infection (Xu et al. 2007), hsa05166 HTLV-I infection (Veyssier-Belot et al. 1990) as well as hsa05168 Herpes simplex infection (Bustamante and Wade 1991), hsa05161 Hepatitis B (Xie et al. 2011), hsa05160 Hepatitis C (McGivern and Lemon 2011) and hsa04064 NF-kappa B signaling pathway (Kilger et al. 1998).
Besides, we could find that most of these pathways are concerned with human immune system. The hsa04620 pathway is concerned with rapid activation of innate immunity by inducing production of proinflammatory cytokines and upregulation of costimulatory molecules. In the hsa04622 pathway, specific families of pattern recognition receptors are responsible for detecting viral pathogens and generating innate immune responses. Additionally, specific families of pattern recognition receptors are responsible for detecting foreign DNA from invading microbes or host cells and generating innate immune responses in the hsa04623 pathway. Meanwhile, the hsa05160 pathway is also concerned with the RIG-I pathway and immune response to inhibit the action of IFN. Indeed, most of these pathways contain the obtained proteins that are IFN regulatory factors, which have been indicated as the important possible role in signaling tumor types (Luker et al. 2001). Thus, these pathways are probably potential in the tumor process.
Moreover, it has been reported that the sequence of the KSHV genome revealed at least 89 open-reading frames (ORFs). Many of these KSHV genes represent pirated versions of cellular genes, including those encoding molecules involved in cell cycle control, apoptosis prevention, immune system regulation and inter- and intracellular communication, thus harboring proangiogenic and oncogenic potential (Russo et al. 1996), which suggests the possibility of the obtained protein related to oncoviruses.
Hodgkin lymphoma, Burkitt lymphoma, nasopharyngeal cancer
The results of Hodgkin lymphoma, Burkitt lymphoma, nasopharyngeal cancer are shown in Table 4. These three cancers are caused by the same oncovirus and its protein products, and we extracted 16 human proteins that contain the domains having significant similarity with viral domains from the viral oncoproteins. Three of them were mapped onto pathways and another human protein was obtained from PPI data. Total six pathways have been selected as follows:
hsa00561 Glycerolipid metabolism (2)
hsa00565 Ether lipid metabolism (2)
hsa00564 Glycerophospholipid metabolism (2)
hsa04666 Fc gamma R-mediated phagocytosis (2)
hsa04975 Fat digestion and absorption (2)
hsa00600 Sphingolipid metabolism (2)
Table 4.
Results for Hodgkin lymphoma, Burkitt lymphoma, nasopharyngeal cancer
| Hodgkin lymphoma, Burkitt lymphoma, nasopharyngeal cancer | ||
|---|---|---|
| Viral oncoproteins | Epstein-Barr nuclear antigen (EBNA), latent membrane proteins (LMP) | |
| Associated Pfam domains | Epstein-Barr nuclear antigen-3 (EBNA-3) | |
| Latent membrane proteins-1,2 (LMP1,2) | ||
| Human proteins mapped to KEGG pathway extracted by HMMER | 3 | PPAP2C; phosphatidic acid phosphatase type 2C (EC:3.1.3.4) |
| hsa00561 glycerolipid metabolism | ||
| hsa00565 ether lipid metabolism | ||
| hsa00564 glycerophospholipid metabolism | ||
| hsa04666 Fc gamma R-mediated phagocytosis | ||
| hsa04975 fat digestion and absorption | ||
| hsa00600 sphingolipid metabolism | ||
| PPAP2B; phosphatidic acid phosphatase type 2B (EC:3.1.3.4) | ||
| hsa00561 glycerolipid metabolism | ||
| hsa00565 ether lipid metabolism | ||
| hsa00564 glycerophospholipid metabolism | ||
| hsa04666 Fc gamma R-mediated phagocytosis | ||
| hsa04975 fat digestion and absorption | ||
| hsa00600 sphingolipid metabolism | ||
| ITGA6; integrin, alpha 6 | ||
| hsa04151 PI3K-Akt signaling pathway | ||
| hsa05414 dilated cardiomyopathy | ||
| hsa04640 hematopoietic cell lineage | ||
| hsa04514 cell adhesion molecules (CAMs) | ||
| hsa05222 small cell lung cancer | ||
| hsa05412 arrhythmogenic right ventricular cardiomyopathy (ARVC) | ||
| hsa05145 toxoplasmosis | ||
| hsa04810 regulation of actin cytoskeleton | ||
| hsa05200 pathways in cancer | ||
| hsa05410 hypertrophic cardiomyopathy (HCM) | ||
| hsa04512 ECM-receptor interaction | ||
| hsa04510 focal adhesion | ||
| Human proteins mapped to KEGG pathway extracted by PPI | 1 | HNF4A; hepatocyte nuclear factor 4, alpha |
| hsa04950 maturity onset diabetes of the young | ||
All of these pathways contain the same hit human proteins phosphatidic acid phosphatase (PAP) type 2C (PPAP2C) and PAP type 2B (PPAP2B), which are members of the PAP family. PAPs convert phosphatidic acid to diacylglycerol, and function in de novo synthesis of glycerolipids as well as in receptor-activated signal transduction mediated by phospholipase D, and these two proteins have been shown to possess membrane associated PAP activity. Besides, considering the roles of these pathways play in the lipid metabolisms (the pathways hsa00561, hsa00565, hsa00564, hsa04975, hsa00600) or the host-defense mechanisms (for example, the pathway hsa04666), an alteration of the pathways might lead to disease process once these human proteins are affected by oncovirus easily because of the viral-similar domains, which could be essential for EBV mediated transformations or disease process.
Although there were no clear references for the relationships among the extracted pathways, human proteins and cancer process, previous studies have detected part of the immune systems in EBV infection which immortalizes B lymphocytes and causes polyclonal proliferation, leading to the escape of some transformed clones. Also it has been studied that the LMP1, a viral encoded transmembrance protein, could mediate some of the proliferation signals driving the B cell population expansion (Arvanitakis et al. 1995).
Latent infection membrane protein 1 acts as a constitutively active receptor that mimics activated CD40, which is a member of the tumor necrosis factor receptor family (Mosialos et al. 1995). Also the cytoplasmic carboxyl terminus of LMP1 has a critical role in EBV-induced B-cell transformation (Brown et al. 2001), which induces the activation of some key signaling molecules such as PI3K, JNK, JAKs, leading to the activation of transcription factors including NF-kB, AP-1, and STATs (Kilger et al. 1998). Since all of them have been proved to be related to human malignancies, it is reasonable for us to believe the possibility that those result human proteins which contain the domains having significant similarity with viral domains from viral oncoproteins LMP1 and EBNA as well as the related result pathways may take meaningful roles in the tumor process.
Other virus associated cancers
For malignant pleural mesothelioma, we obtained very few results for further discussion (Table S2 in supplement 1). On the contrary, many pathways were obtained for hepatocellular carcinoma, but all of them only contain one hit human protein which cannot be collected for further study (Table S3 in supplement 1). Both adult T-cell leukemia and squamous cell carcinoma could not associate with any human proteins for mapping.
Common tumor process
From above, it was shown that some pathways appeared in different cancers. Figure 6 summarizes the results showing that the following six pathways contain hit proteins related to at least two different oncoviruses, with the numbers in parentheses representing the numbers of hit proteins.
hsa04630 Jak-STAT signaling pathway (6)—contains hit proteins from penile cancer, vulvar caner, cervical cancer, cancer of the anal canal, Kaposi’s sarcoma and hepatocellular carcinoma
hsa05133 Pertussis (6)—contains hit proteins from penile cancer, vulvar caner, cervical cancer, cancer of the anal canal, Kaposi’s sarcoma
hsa05169 Epstein–Barr virus infection (6)—contains hit proteins from penile cancer, vulvar caner, cervical cancer, cancer of the anal canal, Kaposi’s sarcoma
hsa05152 Tuberculosis (5)—contains hit proteins from penile cancer, vulvar caner, cervical cancer, cancer of the anal canal, Kaposi’s sarcoma
hsa05145 Toxoplasmosis (5)—contains hit proteins from hodgkin lymphoma, Burkitt lymphoma, nasopharyngeal caner, penile cancer, vulvar caner, cervical cancer, cancer of the anal canal, Kaposi’s sarcoma
hsa05142 Chagas disease (American trypanosomiasis) (4)—contains hit proteins from penile cancer, vulvar caner, cervical cancer, cancer of the anal canal, Kaposi’s sarcoma
Fig. 6.
The overview of all the results for each cancer and pathway. Pathways that only obtained by extracted human proteins without PPI data are marked in blue, pathways obtained with PPI data are marked in red. (Color figure online)
Taking hsa04630 Jak-STAT signaling pathway as an example, it not only contains hit protein IRF9 and its interacting proteins CREB binding protein (CREBBP), signal transducer and activator of transcription 2 (STAT2) related to Kaposi’s sarcoma, but also IL10 and IL10RA from cervical cancer as well as penile cancer, vulvar caner and cancer of the anal canal. In addition, the cytokine receptor-like factor 2 (CRLF2) related to hepatocellular carcinoma takes a place in this pathway, suggesting that the Jak-STAT signaling pathway is probably involved in the common tumor process. Actually, it has been proved in many other studies that this pathway is concerned with various cancers (Boudny and Kovarik 2002).
Another pathway hsa05145 Toxoplasmosis shows the relationship with three different kinds of oncoviruses which should be noted. Previous studies also suggested that toxoplasmosis should be considered as a potential pathogen in cancer patients who have clinical syndromes, especially lymphadenopathy (McCabe 1990). In addition, there are studies on the relationships between hsa05152 Tuberculosis and cancer (Vento and Lanzafame 2011) as well as hsa05142 Chagas disease and cancer (Matsuda et al. 2009).
Those pathways contain various human proteins that would be perturbed by different types of oncoviruses, and it is probable for the pathways to be considered as common tumor processes for the cancer research.
Conclusions
According to this study, we categorized several potential cancer pathways, some of which are already known to interplay in the tumor process, some of them still need further researches to confirm. Here we make a list of those unreported pathways with the possible hypotheses towards their role in cancer development, which may provide helpful information for further researches (Table 5).
Table 5.
List of unreported pathways and possible hypotheses
| Potential tumor pathways | Possible hypotheses for the potential pathways interplay in cancer process |
|---|---|
| hsa05145 Toxoplasmosis | This pathway might be considered as a potential pathogen in cancer patients who have clinical syndromes, especially lymphadenopathy and is possible to be affected by EBV, HPV16, HPV18 and KSHV |
| hsa04062 chemokine signaling pathway | This pathway is probably potential for cervical cancer process and may affect the cell survival, apoptosis and other mechanisms after viral product perturbation |
| hsa05164 influenza A | These pathways might be potential with the perturbations on human immune system, especially the ones which contain IFN regulatory factors are possible to be affected by KSHV and play important roles in signaling tumor types |
| hsa05162 measles | |
| hsa04620 toll-like receptor signaling pathway | |
| hsa05133 pertussis | |
| hsa04350 TGF-beta signaling pathway | |
| hsa04380 osteoclast differentiation | |
| hsa04622 RIG-I-like receptor signaling pathway | |
| hsa05142 Chagas disease (American trypanosomiasis) | |
| hsa04010 MAPK signaling pathway | |
| hsa05140 leishmaniasis | |
| hsa04390 hippo signaling pathway | |
| hsa05016 Huntington’s disease | |
| hsa04950 maturity onset diabetes of the young | |
| hsa00561 glycerolipid metabolism | These pathways might be essential for EBV mediated transformations or disease process |
| hsa00565 ether lipid metabolism | |
| hsa00564 glycerophospholipid tabolism | |
| hsa04666 Fc gamma R-mediated phagocytosis | |
| hsa04975 fat digestion and absorption | |
| hsa00600 sphingolipid metabolism |
Since it is quite difficult to find human homologs for viral oncoproteins, we conducted the domain similarity search that resulted in the human proteins containing the similar domains with the viral oncoproteins. Some of those domains have been found in the pathways that are related with tumor process, indicating the possible role in the cancer mechanism. For example, it has been reported that the E6 protein of the cancer associated HPV16 can interact with the focal adhesion protein paxillin (PXN) (Vande Pol et al. 1998), which fits our results because PXN was obtained by the human protein sharing similar domain with E6 (Fig. 5). However, the CD40 which can be mimics activated by LMP1 (Mosialos et al. 1995) had not been covered in this study, suggesting the other mechanisms during oncogenic process should be studied in the future.
Moreover, it is notable that some of the obtained human pathways have not been categorized as cancer pathways but have high possibilities to concern with the tumor process. Needless to say that experimental verification is needed for the identification of the exact role of such viral oncoproteins. At the same time, the future increase of the knowledge on the human pathways may lead to the more findings based on the analysis presented in this study.
Electronic supplementary material
Acknowledgments
Computational resources were provided by the Bioinformatics Center and Supercomputer system, Institute for Chemical Research, Kyoto University. Funding from the Ministry of Education, Culture, Sports, Science and Technology of Japan, and the Japan Science and Technology Agency.
Contributor Information
Zhao Jin, Email: jin@kuicr.kyoto-u.ac.jp.
Masaaki Kotera, Email: kot@kuicr.kyoto-u.ac.jp.
Susumu Goto, Phone: +81-774-383272, FAX: +81-774-383269, Email: goto@kuicr.kyoto-u.ac.jp.
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