Abstract
The purpose of the present study was to assess if guanylate-binding protein (GBP) mRNAs could be prognostic biomarkers for patients with skin cutaneous melanoma (SKCM). The prognostic value of GBP mRNA expression in patients with SKCM was investigated by analyzing gene expression data in 459 SKCM patients. The data were extracted from the OncoLnc database of The Cancer Genome Atlas. A high expression of GBP1, GBP2, GBP3, GBP4 and GBP5 were correlated with favorable overall survival (OS) in the SKCM patients followed for over 30 years. In addition, a high expression of GBP6 mRNA was not correlated with OS in the SKCM patients. A joint effects analysis showed that the co-incidence of the high expression of GBP1-5 was correlated with favorable overall survival in SKCM patients. Our findings suggest that GBP1-5 mRNAs in SKCM are associated with favorable prognosis and may be potential prognostic biomarkers. The combination of GBP1-5 could improve the sensitivity for predicting OS in SKCM patients.
Keywords: guanylate-binding protein, mRNA, correlation, prognosis
Introduction
Skin cutaneous melanoma (SKCM) is one of the most aggressive malignancies; tumors of millimeters are lethal. SKCM accounts for 91% of new cases of skin cancers and results in 74% of skin-related deaths (1). The incidence of SKCM has continued to increase in recent years, and it tends to affect younger people (1,2). The 5-year and 10-year relative survival rates for persons with SKCM are 92 and 89%, respectively. The primary treatment for SKCM is surgery combined with chemotherapy, immunotherapy and radiation (3). Effective prognosis markers may aid in therapeutic treatment for SKCM patients (4). Previous studies have shown that many genes, including AURKB, CCNE1, CDCA8, CDK4, CENPO, GINS2, H2AFZ, LIG1, PKMYT1, PLK1, PTTG1, SKA1, TUBA1B, TUBA1C, TYMS (5), and EZH2 (6), are associated with poor prognosis in SKCM. However, the association between GBP genes and the prognosis of SKCM has not been reported.
Guanylate-binding protein (GBP) belongs to the superfamily of INF-inducible guanosine triphosphate hydrolases (GTPases) (7,8). Up to now, seven human GBP genes, including guanylate-binding protein 1 (GBP1), guanylate-binding protein 2 (GBP2), guanylate-binding protein 3 (GBP3), guanylate-binding protein 4 (GBP4), guanylate-binding protein 5 (GBP5), guanylate-binding protein family member 6 (GBP6) and guanylate-binding protein 7 (GBP7), have been reported (9–11). GBPs, such as GBP1 and GBP2, have antiviral and antimicrobial activities in host defense (12) and could act as protective factors in host defense, controlling infection and autoimmunity (13).
The roles of GBP genes in cancers are complicated. Studies showed that some GBP family members were expressed in colorectal cancer (CRC) (14–17), breast cancer (18,19), oral squamous cell carcinoma (OSCC) (20), esophageal squamous cell carcinomas (SCC) (21), cutaneous T-cell lymphoma (22), prostate cancer (23), and Kaposi's sarcoma (24,25). GBP1 was upregulated in CRC (15) and OSCC (20), modulated the migration and invasion of OSCC cell in vitro (20), and inhibited the growth of highly malignant TS/A mammary carcinoma cells (19) and CRC tumors (14,15,17) in vivo. The high expression of GBP1 was associated with high overall pathological stage in OSCC tissue. GBP2 was related to T-cell infiltration in breast cancer (18).
The expression of GBP mRNAs is highly induced by interferon-γ (IFN-γ) in many cells including fibroblasts, B cells, T cells, and some tumor cells (15,24,26). GBP was also associated with the prognosis of many cancers (14,16–21,23,25). In addition, GBP1 plays dual roles in different tumor cells. Upregulated GBP1 mediated the anti-tumorigenic effects of IFN-γ and correlated with better OS in CRC (15,17). However, overexpressed GBP1 was significantly associated with poorer prognosis in OSCC patients (20). A high expression of GBP2 with a favorable prognosis was found in patients with node-negative breast carcinomas (18). However, the prognostic value of individual GBP in SKCM remains elusive. The present study investigated the prognostic value of individual GBP mRNA and made a joint effects analysis in 459 SKCM patients using OncoLnc data generated from The Cancer Genome Atlas (TCGA; https://cancergenome.nih.gov/, accessed March 1, 2017) database (27). Our results indicated that a high mRNA expression of individual GBP1-5 genes and a high co-expression of these gene mRNAs were correlated with high OS, suggesting that these genes may be potential prognosis biomarkers in SKCM patients.
Materials and methods
Data preparation
TCGA survival data of SKCM was extracted from OncoLnc (http://www.oncolnc.org/, accessed March 3, 2017) (27), including the patients' ID in TCGA, sex, age at diagnosis, events, median survival, survival time, death status, and GBP members' mRNA expression regarding 459 SKCM patients. Briefly, 7 GBP sub-members (GBP1, GBP2, GBP3, GBP4, GBP5, GBP6, and GBP7) were entered into the database (http://www.oncolnc.org/, accessed by March 3, 2017). The patients were sorted into a percentile of 50:50 by the expression of every GBP sub-member, and then SKCM patients' survival data information was obtained.
The Metabolic gEne RApid Visualizer (MERAV: http://merav.wi.mit.edu/SearchByGenes.html, accessed March 1, 2017) (28) was used to make a boxplot of GBP sub-members' expression levels in normal tissue and primary tumors of skin cancer. After GBP genes and the selected tissue type were submitted on the website, boxplots were made and displayed. The unit for mRNA expression is counted in downloaded TCGA data.
Correlation and bioinformatics analysis
The Pearson correlation coefficient was used to assess the co-expression of GBP genes. The relative expression levels of GBP genes in multiple normal tissues were determined with the GTEx Portal (http://www.gtexportal.org/home/, accessed April 25, 2017) (29). A gene function prediction website (GeneMANIA: http://genemania.org/, accessed March 15, 2017) (30) was also used to construct the gene-gene interaction networks. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) v.6.7 (https://david.ncifcrf.gov/tools.jsp, accessed April 3, 2017) (31,32) was used to annotate input genes, classify gene functions, identify gene conversions, and carry out Gene Ontology (GO) term analysis (32). P<0.05 and a false discovery rate (FDR) <0.05 were considered to indicate a statistically significant difference.
Survival analysis
A Kaplan-Meier estimator with a log-rank test was used to evaluate the correlation of six mRNAs with patient survival. Hazard ratios (HR) and 95% confidence intervals (CI) were used to assess the relative risk of SKCM survival.
Joint effects analysis
A joint effects analysis was performed based on the survival analysis results. Patients were regrouped based on the combined GBP mRNA expression and OS scores, which were calculated by summarizing all of the points given to GBP1-5 in a patient when 1 point was assigned to genes of high expression with favorable OS and 0 points were assigned to genes of low expression with poor OS.
Statistical analysis
Statistical analyses were carried out using SPSS v.22.0 software (IBM, Chicago, IL, USA). P<0.05 was considered to indicate a statistically significant difference.
Results
mRNA expression of GBP genes in human normal skin and skin cancer tissues
The GBP family is composed of seven members. Among the seven GBP genes, only GBP7 was not found in www.oncolnc.org, likely due to its low expression (33). In human skin tissue, GBP2 and GBP6 were expressed at high levels, whereas the remaining GBP genes (GBP1, GBP3, GBP4 and GBP5) were expressed at low levels (Fig. 1A-F; Fig. 1A, GBP1; Fig. 1B, GBP2; Fig. 1C, GBP3; Fig. 1D, GBP4; Fig. 1E, GBP5; Fig. 1F, GBP6).
Figure 1.
GBP genes in multiple normal tissues in the GTEx Portal database. The expression of GBP genes in normal skin tissue was highlighted in red. (A) GBP1 gene expression in multiple normal tissues; (B) GBP2 gene expression in multiple normal tissues; (C) GBP3 gene expression in multiple normal tissues. GBP, guanylate-binding protein. (D) GBP4 gene expression in multiple normal tissues; (E) GBP5 gene expression in multiple normal tissues; (F) GBP6 gene expression in multiple normal tissues. GBP, guanylate-binding protein.
The boxplots of the GBP family generated from MERAV shows differences in the expression levels of GBP genes between normal skin tissue and primary skin tumor. The expressions of GBP1, GBP4 and GBP5 in normal skin tissue were higher than skin cancer. Moreover, the expressions of GBP2, GBP3 and GBP6 in skin cancer were higher than in normal skin tissue (Fig. 2).
Figure 2.
The MERAV boxplots of GBP family expression in skin normal tissue and primary tumor. (A) Boxplot for GBP1 expression; (B) boxplot for GBP2 expression; (C) boxplot for GBP3 expression; (D) boxplot for GBP4 expression; (E) boxplot for GBP5 expression; (F) boxplot for GBP6 expression. MERAV, Metabolic gEne RApid Visualizer; GBP, guanylate-binding protein.
Functions and correlation of the mRNA expression of GBP genes in human tissues
A co-expression analysis (Fig. 3) showed that GBP1, GBP2, GBP3, GBP4 and GBP5 were co-expressed in human tissues. GBP3 was in the NFATC2 pathway, GBP2 was in the IFI35, IRF9 and XAF1 pathways, and GBP2 was predicted in the IRF1 pathway. The correlation of individual GBP family gene mRNA expression was tested using the Pearson correlation coefficient (Table I). With the exception of GBP6, the mRNA expression of all other GBP family genes was significantly (R=0.550–0.842, P<0.001) positively correlated (Table I). A GO term analysis using DAVID revealed that GBP genes were significantly associated with the biological process of immune response, as well as the molecular functions of GTPases activity and guanosine triphosphate (GTP) binding (Table II).
Figure 3.
Co-expression/pathway/predication analysis of GBP1, GBP2, GBP3, GBP4 and GBP5 according to human expression data in GeneMANIA. GBP, guanylate-binding protein.
Table I.
Co-expression of GBP family at mRNA level.
| GBP1 | GBP2 | GBP3 | GBP4 | GBP5 | GBP6 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Genes | R | P-value | R | P-value | R | P-value | R | P-value | R | P-value | R | P-value |
| GBP1 | – | – | 0.842 | <0.001 | 0.743 | <0.001 | 0.670 | <0.001 | 0.702 | <0.001 | 0.000 | 0.995 |
| GBP2 | 0.842 | <0.001 | – | – | 0.685 | <0.001 | 0.643 | <0.001 | 0.654 | <0.001 | 0.047 | 0.313 |
| GBP3 | 0.743 | <0.001 | 0.685 | <0.001 | – | – | 0.550 | <0.001 | 0.594 | <0.001 | 0.022 | 0.636 |
| GBP4 | 0.670 | <0.001 | 0.643 | <0.001 | 0.550 | <0.001 | – | – | 0.768 | <0.001 | −0.013 | 0.776 |
| GBP5 | 0.702 | <0.001 | 0.654 | <0.001 | 0.594 | <0.001 | 0.768 | <0.001 | – | – | 0.001 | 0.990 |
| GBP6 | 0.000 | 0.995 | 0.047 | 0.313 | 0.022 | 0.636 | −0.013 | 0.776 | 0.001 | 0.990 | – | – |
The correlations of gene mRNA expression in GBP families were tested using the Pearson correlation coefficient. R, Pearson correlation coefficient; GBP, guanylate-binding protein.
Table II.
Analysis of enriched GO terms for GBP genes carried out using DAVID.
| Category | Term | Genes | P-value | FDR |
|---|---|---|---|---|
| Molecular function | GTPase activity | 6 | 1.08×10−09 | 5.58×10−07 |
| GTP binding | 6 | 1.88×10−08 | 9.70×10−06 | |
| Guanylate nucleotide binding | 6 | 2.15×10−08 | 1.11×10−05 | |
| Guanylate ribonucleotide binding | 6 | 2.15×10−08 | 1.11×10−05 | |
| Ribonucleotide binding | 6 | 5.63×10−05 | 0.029018 | |
| Purine ribonucleotide binding | 6 | 5.63×10−05 | 0.029018 | |
| Purine nucleotide binding | 6 | 7.01×10−05 | 0.03611 | |
| Nucleotide binding | 6 | 1.54×10−04 | 0.079382 | |
| Biological process | Immune response | 6 | 3.40×10−07 | 7.69×10−05 |
| Cellular component | Internal side of plasma membrane | 3 | 0.001798 | – |
| Plasma membrane part | 3 | 0.078854 | – | |
| Plasma membrane | 3 | 0.210327 | – |
GBP, guanylate-binding protein; GO Gene Ontology; DAVID, Database for Annotation, Visualization, and Integrated Discovery; GTPases, guanosine triphosphates. GTP, guanosine triphosphate; FDR, false discovery rate.
Survival analysis
The prognostic value of the GBP family gene mRNA expression was assessed with SPSS. A high expression of GBP1-5 was significantly (P<0.001) associated with a favorable OS in SKCM patients (Fig. 4A-E). GBP6 expression did not show a significant correlation with OS in SKCM patients (P=0.401925, HR=1.121, 95% CI=0.8580–1.465) (Fig. 4F).
Figure 4.
The prognostic value of GBP expression. (A) survival curves are plotted for all SKCM patients of GBP1 (n=458); (B) survival curves are plotted for all SKCM patients of GBP2 (n=458); (C) survival curves are plotted for all SKCM patients of GBP3 (n=458); (D) survival curves are plotted for all SKCM patients of GBP4 (n=458); (E) survival curves are plotted for all SKCM patients of GBP5 (n=458); (F) survival curves are plotted for all SKCM patients of GBP6 (n=458). Data were analyzed using SPSS. GBP, guanylate-binding protein; SKCM, skin cutaneous melanoma.
Joint effects analysis
A joint effects analysis was used to determine the combined effect of the GBP gene mRNA co-expression on the OS of SKCM patients. Patients were divided into 6 groups: Group 1 (0 points group, n=136), group 2 (1 point group, n=60), group 3 (2 points group, n=32), group 4 (3 points group, n=39), group 5 (4 points group, n=47) and group 6 (5 points group, n=141) (detailed grouping information is shown in Table III). Kaplan-Meier estimator with a log-rank test was used to evaluate the prognostic value of these 6 groups. The co-overexpression of GBP1-5 in Group 6 (141 from 455) was found to be more highly correlated with a favorable OS than the co-overexpression of fewer GBP genes in other groups (P<0.0001). In contrast, the expression of GBPs was homogeneously low in Group 1 (136 from 455), which was found to be more highly correlated with poor OS than the other groups (P<0.0001) (Fig. 5).
Table III.
Grouping information for the combination among GBP genes.
| Group | Points | Composition |
|---|---|---|
| 1 | 0 | Low GBP1+Low GBP2+Low GBP3+Low GBP4+Low GBP5 |
| 2 | 1 | High GBP1+Low GBP2+Low GBP3+Low GBP4+Low GBP5 |
| 2 | 1 | Low GBP1+High GBP2+Low GBP3+Low GBP4+Low GBP5 |
| 2 | 1 | Low GBP1+Low GBP2+High GBP3+Low GBP4+Low GBP5 |
| 2 | 1 | Low GBP1+Low GBP2+Low GBP3+High GBP4+Low GBP5 |
| 2 | 1 | Low GBP1+Low GBP2+Low GBP3+Low GBP4+High GBP5 |
| 3 | 2 | High GBP1+High GBP2+Low GBP3+Low GBP4+Low GBP5 |
| 3 | 2 | High GBP1+Low GBP2+High GBP3+Low GBP4+Low GBP5 |
| 3 | 2 | High GBP1+Low GBP2+Low GBP3+High GBP4+Low GBP5 |
| 3 | 2 | High GBP1+Low GBP2+Low GBP3+Low GBP4+High GBP5 |
| 3 | 2 | Low GBP1+High GBP2+High GBP3+Low GBP4+Low GBP5 |
| 3 | 2 | Low GBP1+High GBP2+Low GBP3+High GBP4+Low GBP5 |
| 3 | 2 | Low GBP1+High GBP3+Low GBP3+Low GBP4 +High GBP5 |
| 3 | 2 | Low GBP1+Low GBP2+High GBP3+High GBP4+Low GBP5 |
| 3 | 2 | Low GBP1+Low GBP2+High GBP3+Low GBP4+High GBP5 |
| 3 | 2 | Low GBP1+Low GBP2+Low GBP3+High GBP4+High GBP5 |
| 4 | 3 | High GBP1+High GBP2+High GBP3+Low GBP4+Low GBP5 |
| 4 | 3 | High GBP1+High GBP2+Low GBP3+High GBP4+Low GBP5 |
| 4 | 3 | High GBP1+High GBP2+Low GBP3+Low GBP4+High GBP5 |
| 4 | 3 | High GBP1+Low GBP2+High GBP3+High GBP4+Low GBP5 |
| 4 | 3 | High GBP1+Low GBP2+High GBP3+Low GBP4+High GBP5 |
| 4 | 3 | High GBP1+Low GBP2+Low GBP3+High GBP4+High GBP5 |
| 4 | 3 | Low GBP1+High GBP2+High GBP3+High GBP4+Low GBP5 |
| 4 | 3 | Low GBP1+High GBP2+High GBP3+Low GBP4+High GBP5 |
| 4 | 3 | Low GBP1+High GBP2+Low GBP3+High GBP4+High GBP5 |
| 4 | 3 | Low GBP1+Low GBP2+High GBP3+High GBP4+High GBP5 |
| 5 | 4 | High GBP1+High GBP2+High GBP3+High GBP4+Low GBP5 |
| 5 | 4 | High GBP1+High GBP2+High GBP3+Low GBP4+High GBP5 |
| 5 | 4 | High GBP1+High GBP2+Low GBP3+High GBP4+High GBP5 |
| 5 | 4 | High GBP1+Low GBP2+High GBP3+High GBP4+High GBP5 |
| 5 | 4 | Low GBP1+High GBP2+High GBP3+High GBP4+High GBP5 |
| 6 | 5 | High GBP1+High GBP2+High GBP3+High GBP4+High GBP5 |
With the median value of the gene expression as cutoff, the patients were designated as high expression or low expression for every member of GBP family, and grouped based on the combination of the gene expression levels. Group 1 (all low expression genes, 0 points group, n=136), group 2 (1 high expression gene, 1 point group, n=60), group 3 (2 high expression genes, 2 points group, n=32), group 4 (3 high expression genes, 3 points group, n=39), group 5 (4 high expression genes, 4 points group, n=47), group 6 (all high expression genes, 5 points group, n=141). GBP, guanylate-binding protein.
Figure 5.
The result of the joint effects analysis. OS stratified by 5 GBP genes expression levels. Group 1 (0 points group, n=136), Group 2 (1 point group, n=60), Group 3 (2 points group, n=32), Group 4 (3 points group, n=39), Group 5 (4 points group, n=47) and Group 6 (5 points group, n=141). GBP, guanylate-binding protein.
Discussion
In the present study, the data for the GBP gene mRNA expression and survival of SKCM patients were extracted from OncoLnc, analyzed to predict the function of GBP genes, and assessed for the potential of the mRNA expression of GBP genes to be used as prognosis biomarkers. Our analysis revealed that GBPs may be responsible for host defense, GTP binding and GTP hydrolysis. The correlation between the GBP gene mRNA levels and OS suggested that GBP mRNA may be good prognosis biomarkers for SKCM patients.
Our bioinformatics analysis revealed that the most meaningful molecular functions of GBP were GTPase activity, GTP binding, guanylate nucleotide binding, guanylate ribonucleotide binding, ribonucleotide binding, purine ribonucleotide binding, purine nucleotide binding, and nucleotide binding, which is in agreement with the observations that GBP belongs to the superfamily of INF-inducible GTPases including four sub-families: GBPs, immunity-related GTPases, very large inducible GTPase and myxovirus resistance proteins (7,8). The probable involvement of GBPs in the biological process of immunity deduced by our analysis is in agreement with the observations that GBPs, such as GBP1 and GBP2, have antiviral and antimicrobial activities in host defense (12) and that GBPs could act as protective factors in host defense, controlling infection and autoimmunity (13). The predicted immunity roles of GBP genes are also in agreement with the finding that upregulated GBP1 in CRC inhibits tumor growth (14,15,17) and that GBP2 was associated with T-cell infiltration in breast cancer (18).
In the present study, the Kaplan-Meier curves show that a high expression of GBP1-5 was found to be correlated with favorable OS in all SKCM patients. The correlation between a high expression of GBP1 and favorable OS in SKCM patients is in accordance with its correlation with high survival in CRC (17) but contrary to its correlation with poor survival in OSCC (20). These results indicated that GBP1 could play different roles in different cancers. The correlation of a high expression of GBP2 with a favorable OS in SKCM observed in the present study and in node-negative breast carcinomas (18) suggests that GBP2 may share the same mechanism in both SKCM and node-negative breast carcinomas. Though the expression of GBP6 in skin tumor tissue was more than its expression in normal tissue, no correlation between prognosis value with high expression of GBP6 or low expression of GBP6 was found. It is unclear why the high expression of downregulated GBP1, GBP4 and GBP5, as well as upregulated GBP2 and GBP3 showed the same correlation with a favorable OS in skin cancer. No survival information on GBP7 in SKCM patients is available, likely due to its low expression in normal skin tissue and SKCM, which makes it difficult to assess its correlation with prognosis outcomes.
The joint effects analysis showed that the co-expression of GBP1-5 all at high levels was correlated with a favorable OS in SKCM patients. In contrast, the co-expression of GBP1-5 at low levels was correlated with poor OS in SKCM patients. There was a tendency for GBP genes with higher expressions to be more highly correlated with favorable OS. The induced high co-expression of GBP1-5 in cells by IFN-γ (10,34) suggests that it may be possible to increase patients' favorable OS through the induction of a higher co-expression of GBP genes with IFN-γ. This hypothesis needs to be further investigated and experimentally proved. The combination of GBP1-5 may improve the sensitivity of predicting OS in SKCM patients.
There were limitations to the present study that should be recognized. First, since the data from the TCGA database and OncoLnc was not comprehensive, the present study evaluated the association between gene expression level and OS based on a log-rank test in Kaplan-Meier analysis. Second, the patients in the present study were exclusively from a single source, which meant that a multivariate analysis could not be used to validate the results. It is necessary to validate the prognostic value of these genes in patients with SKCM using independent external validation datasets containing complete clinical information. Despite these limitations, our current study was the first to report that the upregulation of the GBP genes (GBP1, GBP2, GBP3, GBP4 and GBP5) in SKCM was associated with a favorable prognosis. GBP1-5 may be used as prognostic biomarkers for SKCM patients.
In conclusion, a high expression of 5 GBP genes (GBP1, GBP2, GBP3, GBP4 and GBP5) was individually and coincidentally related to a favorable prognosis for SKCM. GBP1-5 may be used as potential prognostic biomarkers for SKCM patients. These results need to be confirmed in further studies.
Acknowledgements
The authors would like to thank the contributors of OncoLnc for sharing the SKCM survival data on open access.
Glossary
Abbreviations
- GBP
guanylate-binding protein
- OS
overall survival
- SKCM
skin cutaneous melanoma
References
- 1.Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66:7–30. doi: 10.3322/caac.21332. [DOI] [PubMed] [Google Scholar]
- 2.Siegel R, Ma J, Zou Z, Jemal A. Cancer statistics, 2014. CA Cancer J Clin. 2014;64:9–29. doi: 10.3322/caac.21208. [DOI] [PubMed] [Google Scholar]
- 3.Miller KD, Siegel RL, Lin CC, Mariotto AB, Kramer JL, Rowland JH, Stein KD, Alteri R, Jemal A. Cancer treatment and survivorship statistics, 2016. CA Cancer J Clin. 2016;66:271–289. doi: 10.3322/caac.21349. [DOI] [PubMed] [Google Scholar]
- 4.Weiss SA, Hanniford D, Hernando E, Osman I. Revisiting determinants of prognosis in cutaneous melanoma. Cancer. 2015;121:4108–4123. doi: 10.1002/cncr.29634. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Zainulabadeen A, Yao P, Zare H. Underexpression of specific interferon genes is associated with poor prognosis of melanoma. PLoS One. 2017;12:e0170025. doi: 10.1371/journal.pone.0170025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Tiffen J, Wilson S, Gallagher SJ, Hersey P, Filipp FV. Somatic copy number amplification and hyperactivating somatic mutations of EZH2 correlate with DNA methylation and drive epigenetic silencing of genes involved in tumor suppression and immune responses in melanoma. Neoplasia. 2016;18:121–132. doi: 10.1016/j.neo.2016.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Kim BH, Shenoy AR, Kumar P, Bradfield CJ, MacMicking JD. IFN-inducible GTPases in host cell defense. Cell Host Microbe. 2012;12:432–444. doi: 10.1016/j.chom.2012.09.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Hotter D, Sauter D, Kirchhoff F. Guanylate binding protein 5: Impairing virion infectivity by targeting retroviral envelope glycoproteins. Small GTPases. 2017;8:31–37. doi: 10.1080/21541248.2016.1189990. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Goo YH, Son SH, Yechoor VK, Paul A. Transcriptional profiling of foam cells reveals induction of guanylate-binding proteins following western diet acceleration of atherosclerosis in the absence of global changes in inflammation. J Am Heart Assoc. 2016;5:e002663. doi: 10.1161/JAHA.115.002663. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Tripal P, Bauer M, Naschberger E, Mörtinger T, Hohenadl C, Cornali E, Thurau M, Stürzl M. Unique features of different members of the human guanylate-binding protein family. J Interferon Cytokine Res. 2007;27:44–52. doi: 10.1089/jir.2007.0086. [DOI] [PubMed] [Google Scholar]
- 11.Degrandi D, Konermann C, Beuter-Gunia C, Kresse A, Würthner J, Kurig S, Beer S, Pfeffer K. Extensive characterization of IFN-induced GTPases mGBP1 to mGBP10 involved in host defense. J Immunol. 2007;179:7729–7740. doi: 10.4049/jimmunol.179.11.7729. [DOI] [PubMed] [Google Scholar]
- 12.Vestal DJ, Jeyaratnam JA. The guanylate-binding proteins: Emerging insights into the biochemical properties and functions of this family of large interferon-induced guanosine triphosphatase. J Interferon Cytokine Res. 2011;31:89–97. doi: 10.1089/jir.2010.0102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Shenoy AR, Wellington DA, Kumar P, Kassa H, Booth CJ, Cresswell P, MacMicking JD. GBP5 promotes NLRP3 inflammasome assembly and immunity in mammals. Science. 2012;336:481–485. doi: 10.1126/science.1217141. [DOI] [PubMed] [Google Scholar]
- 14.Friedman K, Brodsky AS, Lu S, Wood S, Gill AJ, Lombardo K, Yang D, Resnick MB. Medullary carcinoma of the colon: A distinct morphology reveals a distinctive immunoregulatory microenvironment. Mod Pathol. 2016;29:528–541. doi: 10.1038/modpathol.2016.54. [DOI] [PubMed] [Google Scholar]
- 15.Britzen-Laurent N, Lipnik K, Ocker M, Naschberger E, Schellerer VS, Croner RS, Vieth M, Waldner M, Steinberg P, Hohenadl C, Stürzl M. GBP-1 acts as a tumor suppressor in colorectal cancer cells. Carcinogenesis. 2013;34:153–162. doi: 10.1093/carcin/bgs310. [DOI] [PubMed] [Google Scholar]
- 16.Britzen-Laurent N, Herrmann C, Naschberger E, Croner RS, Stürzl M. Pathophysiological role of guanylate-binding proteins in gastrointestinal diseases. World J Gastroenterol. 2016;22:6434–6443. doi: 10.3748/wjg.v22.i28.6434. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Naschberger E, Croner RS, Merkel S, Dimmler A, Tripal P, Amann KU, Kremmer E, Brueckl WM, Papadopoulos T, Hohenadl C, et al. Angiostatic immune reaction in colorectal carcinoma: Impact on survival and perspectives for antiangiogenic therapy. Int J Cancer. 2008;123:2120–2129. doi: 10.1002/ijc.23764. [DOI] [PubMed] [Google Scholar]
- 18.Godoy P, Cadenas C, Hellwig B, Marchan R, Stewart J, Reif R, Lohr M, Gehrmann M, Rahnenführer J, Schmidt M, Hengstler JG. Interferon-inducible guanylate binding protein (GBP2) is associated with better prognosis in breast cancer and indicates an efficient T cell response. Breast Cancer. 2014;21:491–499. doi: 10.1007/s12282-012-0404-8. [DOI] [PubMed] [Google Scholar]
- 19.Lipnik K, Naschberger E, Gonin-Laurent N, Kodajova P, Petznek H, Rungaldier S, Astigiano S, Ferrini S, Stürzl M, Hohenadl C. Interferon gamma-induced human guanylate binding protein 1 inhibits mammary tumor growth in mice. Mol Med. 2010;16:177–187. doi: 10.2119/molmed.2009.00172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Yu CJ, Chang KP, Chang YJ, Hsu CW, Liang Y, Yu JS, Chi LM, Chang YS, Wu CC. Identification of guanylate-binding protein 1 as a potential oral cancer marker involved in cell invasion using omics-based analysis. J Proteome Res. 2011;10:3778–3788. doi: 10.1021/pr2004133. [DOI] [PubMed] [Google Scholar]
- 21.Guimarães DP, Oliveira IM, de Moraes E, Paiva GR, Souza DM, Barnas C, Olmedo DB, Pinto CE, Faria PA, De Moura Gallo CV, et al. Interferon-inducible guanylate binding protein (GBP)-2: A novel p53-regulated tumor marker in esophageal squamous cell carcinomas. Int J Cancer. 2009;124:272–279. doi: 10.1002/ijc.23944. [DOI] [PubMed] [Google Scholar]
- 22.Fellenberg F, Hartmann TB, Dummer R, Usener D, Schadendorf D, Eichmüller S. GBP-5 splicing variants: New guanylate-binding proteins with tumor-associated expression and antigenicity. J Invest Dermatol. 2004;122:1510–1517. doi: 10.1111/j.0022-202X.2004.22613.x. [DOI] [PubMed] [Google Scholar]
- 23.Persano L, Moserle L, Esposito G, Bronte V, Barbieri V, Iafrate M, Gardiman MP, Larghero P, Pfeffer U, Naschberger E, et al. Interferon-alpha counteracts the angiogenic switch and reduces tumor cell proliferation in a spontaneous model of prostatic cancer. Carcinogenesis. 2009;30:851–860. doi: 10.1093/carcin/bgp052. [DOI] [PubMed] [Google Scholar]
- 24.Lubeseder-Martellato C, Guenzi E, Jörg A, Töpolt K, Naschberger E, Kremmer E, Zietz C, Tschachler E, Hutzler P, Schwemmle M, et al. Guanylate-binding protein-1 expression is selectively induced by inflammatory cytokines and is an activation marker of endothelial cells during inflammatory diseases. Am J Pathol. 2002;161:1749–1759. doi: 10.1016/S0002-9440(10)64452-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Guenzi E, Töpolt K, Cornali E, Lubeseder-Martellato C, Jörg A, Matzen K, Zietz C, Kremmer E, Nappi F, Schwemmle M, et al. The helical domain of GBP-1 mediates the inhibition of endothelial cell proliferation by inflammatory cytokines. EMBO J. 2001;20:5568–5577. doi: 10.1093/emboj/20.20.5568. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Cheng YS, Colonno RJ, Yin FH. Interferon induction of fibroblast proteins with guanylate binding activity. J Biol Chem. 1983;258:7746–7750. [PubMed] [Google Scholar]
- 27.Anaya J. OncoLnc: Linking TCGA survival data to mRNAs, miRNAs, and lncRNAs. Peer J Comput Sci. 2016;2:e67. doi: 10.7717/peerj-cs.67. [DOI] [Google Scholar]
- 28.Shaul YD, Yuan B, Thiru P, Nutter-Upham A, McCallum S, Lanzkron C, Bell GW, Sabatini DM. MERAV: A tool for comparing gene expression across human tissues and cell types. Nucleic Acids Res. 2016;44:D560–D566. doi: 10.1093/nar/gkv1337. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Carithers LJ, Ardlie K, Barcus M, Branton PA, Britton A, Buia SA, Compton CC, DeLuca DS, Peter-Demchok J, Gelfand ET, et al. A novel approach to high-quality postmortem tissue procurement: The GTEx project. Biopreserv Biobank. 2015;13:311–319. doi: 10.1089/bio.2015.0032. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, et al. The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38:W214–W220. doi: 10.1093/nar/gkq537. (Web Server Issue) [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1–13. doi: 10.1093/nar/gkn923. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4:44–57. doi: 10.1038/nprot.2008.211. [DOI] [PubMed] [Google Scholar]
- 33.Olszewski MA, Gray J, Vestal DJ. In silico genomic analysis of the human and murine guanylate-binding protein (GBP) gene clusters. J Interferon Cytokine Res. 2006;26:328–352. doi: 10.1089/jir.2006.26.328. [DOI] [PubMed] [Google Scholar]
- 34.Britzen-Laurent N, Bauer M, Berton V, Fischer N, Syguda A, Reipschläger S, Naschberger E, Herrmann C, Stürzl M. Intracellular trafficking of guanylate-binding proteins is regulated by heterodimerization in a hierarchical manner. PLoS One. 2010;5:e14246. doi: 10.1371/journal.pone.0014246. [DOI] [PMC free article] [PubMed] [Google Scholar]