Research ArticleExperimental Studies

Epstein-Barr Virus-induced Gene 3 as a Novel Biomarker in Metastatic Melanoma With Infiltrating CD8+ T Cells: A Study Based on The Cancer Genome Atlas (TCGA)

SATORU YONEKURA
Anticancer Research January 2022, 42 (1) 511-517; DOI: https://doi.org/10.21873/anticanres.15509

Abstract

Background/Aim: Epstein-Barr virus-induced gene 3 (EBI3) is an immunomodulatory protein-coding gene. So far, the prognostic role of EBI3 in human metastatic melanoma has been unclear. This study aimed to evaluate the EBI3 expression as a potential biomarker using the public database with tumor-infiltrating lymphocytes (TILs) data. Materials and Methods: Survival analyses were performed in the database of The Cancer Genome Atlas (TCGA) and GSE65904, GSE19234, GSE22153, and GSE22154. The mRNA levels, the distribution pattern of TILs, and the estimated fractions of TILs from the TCGA database were integrated. Results: Higher EBI3 expression in tumors was significantly associated with longer overall survival in TCGA and the other independent cohorts. Interestingly, the patients with high EBI3 expression had a brisk pattern of TILs and increased CD8+ T cells over regulatory T cells with less pigmentation-related gene expressions. Conclusion: EBI3 could serve as a novel biomarker in metastatic melanoma with a favorable TILs profile.

Key Words:

Cutaneous melanoma is an aggressive malignancy of melanocytes. Melanoma is the third most common cutaneous malignant tumor, accounting for less than 5% of cases (1). However, the majority of skin cancer-associated mortality is caused by melanoma (2). The tumor immune microenvironment (TIME) of melanoma is enriched with tumor-infiltrating lymphocytes (TILs) (3). TILs have a clinical role as a prognostic predictor in melanoma (4). Investigation of the immune context of the TIME is important for understanding its impact on response to therapy (5).

Epstein-Barr virus-induced gene 3 (EBI3) is a member of the interleukin-12 (IL-12) family and forms a heterodimer either with the IL-27p28 subunit to form IL-27 or with IL-12p35 subunit to form IL-35 (6). Both IL-27 and IL-35 can affect the TIME by modulating regulatory T cells (Treg) and cytotoxic T cells (7-10). The prognosis of multiple cancers, including breast cancer (11), colorectal cancer (6), lung cancer (12), and cervical cancer (13) is associated with EBI3 expression in tumors. However, the prognostic role of EBI3 in melanoma has not been investigated so far.

The current study aimed to provide evidence on the role of EBI3 expression as a novel biomarker in metastatic melanoma by characterizing the TIME using the publicly available database.

Materials and Methods

Survival analysis with EBI3 mRNA level. Clinicopathological data was obtained from The Cancer Genome Atlas (TCGA) database in the cBioportal (https://www.cbioportal.org/) (14, 15). Survival analysis with gene expression was performed using the dataset of TCGA and GSE65904 (16). The patients with upper 50% expression of EBI3 were defined as EBI3 high, and the others were EBI3 low, in each dataset. For visualizing the survival data, the Kaplan–Meier plotting and log-rank test was used to compare the survival distribution between groups. The EBI3 gene expression as a continuous variable was included for the multivariate Cox regression analysis with the other parameters such as age, sex (male vs. female), and disease stage at initial diagnosis on TIMER (https://cistrome.shinyapps.io/timer/) (17). Meta survival analysis of EBI3 as a prognostic marker on the overall survival was conducted on GENT2 (http://gent2.appex.kr/gent2/) (18). The included database in the meta-survival analysis were GSE19234 (19), GSE22153, and GSE22154 (20).

Spatial distribution of TILs. The data of spatial distribution patterns of TILs and computed indices of TILs clustering patterns (Ball and Hall, Banfield and Raftery, C, and determinant ratio) used in Saltz et al. (21) were downloaded from the Genomic Data Commons (GDC) Data Portal (https://gdc.cancer.gov/about-data/publications/tilmap). Samples of ‘Indeterminate’ pattern were excluded. The distribution pattern of TILs between EBI3 high and low patients was compared by Fisher’s exact test.

Estimated fractions of TILs. The estimated TILs fraction by the quanTIseq method (22) was downloaded from TIMER2.0 (http://timer.comp-genomics.org/) (23).

Pigmentation-related gene signature. The following gene set was used as pigmentation-related gene signature (24): GPR143, SLC45A2, OCA2, MC1R, PMEL, RAB27A, MLANA, TYRP1, DCT, TYR, SNCA, MITF, GPNMB, TRPM1, MCOLN3, RAB38, MITF. Spearman correlation analysis was conducted on Gene Expression Profiling Interactive Analysis 2 (GEPIA2, http://gepia2.cancer-pku.cn/) (25).

Statistical analysis. Spearman’s rho was used for correlation analyses. For graphs and statistical analyses (Mann-Whitney test, Fisher’s exact test, chi-square test, and principal component analysis) were used. Analyses were performed using the R freeware (http://www.r-project.org) and GraphPad Prism (v. 9.2.0) software (GraphPad Software Inc., CA, USA). All p-values were two-sided, and a p-value ≤0.05 was considered statistically significant.

Results

Prognostic EBI3 gene expression in metastatic melanoma. Clinicopathologic characteristics of the patients were shown in Table I. The parameters except for sex (p=0.015) and pT stage (p=0.0001) showed no significant difference (Table I). The survival analysis with EBI3 gene expression data was performed in the metastatic melanoma dataset from TCGA. The EBI3-high patients had longer overall survival (OS) [hazard ratio (HR)=0.586, 95% confidence interval (CI)=0.438-0.783, p=0.0004, Figure 1A]. A similar prognostic role was found in another independent cohort (GSE65904), revealing that the EBI3 gene expression was a favorable factor on disease-specific survival (DSS) (HR=0.589, 95% CI=0.398-0.871, p=0.0068, Figure 1B). Moreover, the meta-survival analysis of EBI3 on GENT2 (18) revealed that the higher EBI3 expression correlated with better OS in both fixed and random effect model (HR=0.84, 95% CI=0.72-0.97, HR=0.82, 95% CI=0.68-0.99, respectively, Figure 2). In TCGA dataset, the multivariate Cox regression analysis also showed that EBI3 gene expression was an independent favorable prognostic factor (HR=0.737, 95% CI=0.652–0.834, p<0.0001, Table II).

View this table:
Table I.

Clinicopathological characteristics between Epstein-Barr virus-induced gene 3 (EBI3) high and low patients on TCGA dataset.

Figure 1.
Figure 1.

Kaplan-Meier curves showing the overall survival (OS) in TCGA cohort (A) and disease-specific survival (DSS) in GSE65904 (B). The higher EBI3 mRNA expression was defined as ‘EBI3 High’, and the others were as ‘EBI3 Low’ stratified by the median level of mRNA expression. HR, Hazard ratio; CI, confidence interval.

Figure 2.
Figure 2.

Meta-survival analysis of EBI3 mRNA levels on the overall survival generated in GENT2 using GSE19234, GSE22153, and GSE22154. TE, Treatment effect; seTE, standard error of treatment estimate; HR, hazard ratio; CI, confidence interval.

View this table:
Table II.

Multivariate Cox regression analysis with Epstein-Barr virus-induced gene 3 (EBI3) mRNA expression levels on the overall survival on TCGA dataset.

Spatial pattern of tumor-infiltrating lymphocytes by EBI3 gene expression. EBI3-encoded protein is immunomodulatory by forming heterodimers relating to IL-27 and IL-35 (26). Therefore, the current study investigated the profile of TILs between EBI3 high vs. low groups. First, the spatial patterns of TILs (21) were investigated; in the EBI3 high group, about 73% (108/148) of the patients had a brisk distribution pattern (i.e., brisk multifocal or brisk focal). Contrarily, in the EBI3 low group, only 47% (69/146) of the included patients had a brisk pattern. The distribution pattern of TILs between EBI3 high and low patients was significantly different (p=9.203e-06, Fisher’s exact test, Figure 3A).

Figure 3.
Figure 3.

Spatial profile of TILs between EBI3 high and low patients. (A) Stacked bar plot of the % of patients with spatial patterns of TILs (brisk or non-brisk/absent). *p<0.05 by Fisher’s exact test; (B-C) Adjusted Banfield and Raftery index (B) and Determinant ratio (C) of clustering patterns of TILs. *p<0.05 by Mann-Whitney test.

Spatial patterns of TIL clustering can be described by several indices such as Ball and Hall, Banfield and Raftery, C, and determinant ratio (21). In the melanoma dataset of TCGA, better prognosis correlated with a higher Banfield and Raftery index (21). In the current study, the adjusted Banfield-Raftery and adjusted determinant ratio indices were significantly higher in the EBI3 high patients (Figure 3B-C), suggesting that EBI3 high patients had favorable TIL clusters in the tumor tissue. In contrast, the other indices (adjusted Ball and Hall and adjusted C) were not significantly different (data not shown).

Antitumor TILs profile and activity with EBI3 gene expression. EBI3 supports antitumor immunity with dominant CD8+ T cells over regulatory T cells (Treg) in vivo (26). Therefore, it was hypothesized that EBI3 expression in human melanoma tissue is also associated with TILs. Fractions of TILs were estimated by the quanTIseq method (22). In TCGA dataset, EBI3 high patients had a profound shift of TIL populations compared to EBI3 low patients as shown by the principal component analysis (Figure 4A). The EBI3 mRNA level positively correlated with the fractions of B cells, CD8+ T cells, Treg, M1/M2 macrophages, and CD8+ T cells/Treg ratio. In contrast, a negative correlation was observed with dendritic cells and NK cells (Figure 4B). Moreover, consistent with the in vivo data (26), the ratio of CD8+ T cells to Tregs was significantly higher in EBI3 high patients (Figure 4C).

Figure 4.
Figure 4.

(A) Principal component analysis of estimated TILs showing the shifted TILs population in EBI3-high tumors compared to EBI3-low tumors; (B) Spearman correlation of EBI3 gene expression with the TILs fractions estimated by the quanTIseq method. *p<0.05; (C) Comparison of the ratio of CD8+ T cell to Treg between EBI3 high and low patients. *p<0.05 by Mann-Whitney test; (D) Spearman correlation of EBI3 expression with pigmentation-related gene set. R, Spearman’s rho; TPM, transcripts per million.

Correlation of EBI3 expression with pigmentation of the melanoma tumors. Melanogenesis in melanoma cells can contribute to tumor behavior, immunosuppressive tumor microenvironment, and patients’ survival (27-31). Intrigued by these studies, the correlation of melanogenesis with EBI3 expression using the pigmentation-related gene set (24) was analyzed. EBI3 expression in tumors was negatively correlated with the pigmentation-related gene set (Spearman’s rho=–0.28) (Figure 4D).

Discussion

The current study reported a novel prognostic role of EBI3 in metastatic melanoma associated with the distinct brisk pattern of TILs distribution and population characterized by increased CD8+ T cells over Treg. To the best of the author’s knowledge, the present report is the first to describe EBI3 as a new biomarker in metastatic melanoma.

This study provides clues to the potential mechanism underlying the favorable prognosis associated with TILs in EBI3-high patients. In the EBI3-high patients, the favorable spatial pattern of TILs (i.e., brisk pattern) was observed. The brisk phenotype of TILs distribution is enriched with CD8+ T cells compared to the non-brisk pattern (21). Consistent with this, the current study showed the increased CD8+ T cells over regulatory T cells in EBI3-high tumors, in which almost three-quarters of the patients had a brisk pattern of TILs. The spatial pattern of TILs can be indicative of immune response (21). Primarily, the brisk diffuse infiltration of TILs is associated with moderate to strong immune responses (21). Thus, the gene expression of EBI3 in the tumor can be an indicator of the nature and effectiveness of the immune response. Another evidence of EBI3-related favorable prognosis is the specific population of TILs, especially CD8+ T cells. Previous in vivo experiments have shown that EBI3 is associated with increased CD8+ T cells over regulatory T cells (26), consistent with the results of this study (Figure 4C). CD8+ T cells are a key for antitumor immunity and prognosis in cutaneous melanoma (32, 33). TILs from EBI3–/– mice have failed to produce IFNγ (26). Regarding antitumor T-cell response, EBI3–/– mice were shown to have a phenotype of IL-27-deficiency rather than IL-35-deficiency (26). IL-27 inhibits the inducible T regulatory cells and the expression of Foxp3, CD25, and the immune checkpoint cytotoxic T lymphocyte-associated protein 4 (CTLA-4) (8, 9, 26). Contrary to IL-27, IL-35 can limit antitumor immunity by inducing Treg (7, 10). As for cytotoxic T cell (CTL) activity, IL-27 promotes CTL accumulation and inhibits tumor growth by increased CTL survival and effector functions (34-36). Taken together, the observed longer survival in EBI3-high tumors might be due to IL-27-related antitumor activity by TILs.

The role of EBI3 in oncology has been studied in breast (11), colorectal (6), lung (12), and cervical cancer (13). Interestingly, these previous studies revealed that higher EBI3 expression is an unfavorable prognostic factor, whereas we found the opposite in melanoma. Contrary to IL-27, IL-35 can explain the progression of cancers by recruiting Treg (37) or promoting tumor cell proliferation (12, 38). Therefore, further studies including preclinical models on the role of endogenous EBI3, IL-27 or IL-35 in melanoma cells or tumor microenvironment would be necessary to understand the opposite prognostic role among melanoma and the other cancers. Moreover, the current study showed the evidence of the link between EBI3 expression and melanogenesis in tumors. Melanogenesis can affect the tumor microenvironment in an immunosuppressive way (30) and amelanotic melanoma was correlated with longer survival (27, 31). The results of this study suggest that the unfavorable prognosis in pigmented melanoma patients is partly explained by EBI3-low tumor microenvironment.

The main limitation of the current study arises from the fact that it was based only on the public database TCGA. Hence, important information such as the growth phase (vertical or not) of the tumors and metastatic status (visceral or lymph node) is missing. Moreover, future studies are necessary to provide experimental evidence on the role of EBI3 on the tumor microenvironment in association with TILs, IL-27, and IL-35.

In conclusion, EBI3 is a novel biomarker in metastatic melanoma with a favorable distribution pattern of increased CD8+ T cells infiltration. The current study could contribute to a better understanding of a favorable tumor microenvironment of melanoma. Further studies are warranted to clarify the role of endogenous EBI3, IL-27 or IL-35 in the tumor microenvironment of melanoma.

Footnotes

  • Conflicts of Interest

    None.

  • Received August 11, 2021.
  • Revision received November 13, 2021.
  • Accepted November 15, 2021.

References

  1. ↵
    1. Miller AJ and
    2. Mihm MC Jr.
    : Melanoma. N Engl J Med 355(1): 51-65, 2006. PMID: 16822996. DOI: 10.1056/NEJMra052166
    OpenUrlCrossRefPubMed
  2. ↵
    1. Schadendorf D,
    2. van Akkooi ACJ,
    3. Berking C,
    4. Griewank KG,
    5. Gutzmer R,
    6. Hauschild A,
    7. Stang A,
    8. Roesch A and
    9. Ugurel S
    : Melanoma. Lancet 392(10151): 971-984, 2018. PMID: 30238891. DOI: 10.1016/S0140-6736(18)31559-9
    OpenUrlCrossRefPubMed
  3. ↵
    1. Antohe M,
    2. Nedelcu RI,
    3. Nichita L,
    4. Popp CG,
    5. Cioplea M,
    6. Brinzea A,
    7. Hodorogea A,
    8. Calinescu A,
    9. Balaban M,
    10. Ion DA,
    11. Diaconu C,
    12. Bleotu C,
    13. Pirici D,
    14. Zurac SA and
    15. Turcu G
    : Tumor infiltrating lymphocytes: The regulator of melanoma evolution. Oncol Lett 17(5): 4155-4161, 2019. PMID: 30944610. DOI: 10.3892/ol.2019.9940
    OpenUrlCrossRefPubMed
  4. ↵
    1. Azimi F,
    2. Scolyer RA,
    3. Rumcheva P,
    4. Moncrieff M,
    5. Murali R,
    6. McCarthy SW,
    7. Saw RP and
    8. Thompson JF
    : Tumor-infiltrating lymphocyte grade is an independent predictor of sentinel lymph node status and survival in patients with cutaneous melanoma. J Clin Oncol 30(21): 2678-2683, 2012. PMID: 22711850. DOI: 10.1200/JCO.2011.37.8539
    OpenUrlAbstract/FREE Full Text
  5. ↵
    1. Binnewies M,
    2. Roberts EW,
    3. Kersten K,
    4. Chan V,
    5. Fearon DF,
    6. Merad M,
    7. Coussens LM,
    8. Gabrilovich DI,
    9. Ostrand-Rosenberg S,
    10. Hedrick CC,
    11. Vonderheide RH,
    12. Pittet MJ,
    13. Jain RK,
    14. Zou W,
    15. Howcroft TK,
    16. Woodhouse EC,
    17. Weinberg RA and
    18. Krummel MF
    : Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med 24(5): 541-550, 2018. PMID: 29686425. DOI: 10.1038/s41591-018-0014-x
    OpenUrlCrossRefPubMed
  6. ↵
    1. Liang Y,
    2. Chen Q,
    3. Du W,
    4. Chen C,
    5. Li F,
    6. Yang J,
    7. Peng J,
    8. Kang D,
    9. Lin B,
    10. Chai X,
    11. Zhou K and
    12. Zeng J
    : Epstein-Barr virus-induced gene 3 (EBI3) blocking leads to induce antitumor cytotoxic T lymphocyte response and suppress tumor growth in colorectal cancer by bidirectional reciprocal-regulation STAT3 signaling pathway. Mediators Inflamm 2016: 3214105, 2016. PMID: 27247488. DOI: 10.1155/2016/3214105
    OpenUrlCrossRefPubMed
  7. ↵
    1. Collison LW,
    2. Chaturvedi V,
    3. Henderson AL,
    4. Giacomin PR,
    5. Guy C,
    6. Bankoti J,
    7. Finkelstein D,
    8. Forbes K,
    9. Workman CJ,
    10. Brown SA,
    11. Rehg JE,
    12. Jones ML,
    13. Ni HT,
    14. Artis D,
    15. Turk MJ and
    16. Vignali DA
    : IL-35-mediated induction of a potent regulatory T cell population. Nat Immunol 11(12): 1093-1101, 2010. PMID: 20953201. DOI: 10.1038/ni.1952
    OpenUrlCrossRefPubMed
  8. ↵
    1. Huber M,
    2. Steinwald V,
    3. Guralnik A,
    4. Brüstle A,
    5. Kleemann P,
    6. Rosenplänter C,
    7. Decker T and
    8. Lohoff M
    : IL-27 inhibits the development of regulatory T cells via STAT3. Int Immunol 20(2): 223-234, 2008. PMID: 18156621. DOI: 10.1093/intimm/dxm139
    OpenUrlCrossRefPubMed
  9. ↵
    1. Neufert C,
    2. Becker C,
    3. Wirtz S,
    4. Fantini MC,
    5. Weigmann B,
    6. Galle PR and
    7. Neurath MF
    : IL-27 controls the development of inducible regulatory T cells and Th17 cells via differential effects on STAT1. Eur J Immunol 37(7): 1809-1816, 2007. PMID: 17549733. DOI: 10.1002/eji.200636896
    OpenUrlCrossRefPubMed
  10. ↵
    1. Turnis ME,
    2. Sawant DV,
    3. Szymczak-Workman AL,
    4. Andrews LP,
    5. Delgoffe GM,
    6. Yano H,
    7. Beres AJ,
    8. Vogel P,
    9. Workman CJ and
    10. Vignali DA
    : Interleukin-35 limits anti-tumor immunity. Immunity 44(2): 316-329, 2016. PMID: 26872697. DOI: 10.1016/j.immuni.2016.01.013
    OpenUrlCrossRefPubMed
  11. ↵
    1. Jiang J and
    2. Liu X
    : Upregulated EBI3 correlates with poor outcome and tumor progression in breast cancer. Oncol Res Treat 41(3): 111-115, 2018. PMID: 29485413. DOI: 10.1159/000484935
    OpenUrlCrossRefPubMed
  12. ↵
    1. Nishino R,
    2. Takano A,
    3. Oshita H,
    4. Ishikawa N,
    5. Akiyama H,
    6. Ito H,
    7. Nakayama H,
    8. Miyagi Y,
    9. Tsuchiya E,
    10. Kohno N,
    11. Nakamura Y and
    12. Daigo Y
    : Identification of Epstein-Barr virus-induced gene 3 as a novel serum and tissue biomarker and a therapeutic target for lung cancer. Clin Cancer Res 17(19): 6272-6286, 2011. PMID: 21849417. DOI: 10.1158/1078-0432.CCR-11-0060
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Hou YM,
    2. Dong J,
    3. Liu MY and
    4. Yu S
    : Expression of Epstein-Barr virus-induced gene 3 in cervical cancer: Association with clinicopathological parameters and prognosis. Oncol Lett 11(1): 330-334, 2016. PMID: 26870212. DOI: 10.3892/ol.2015.3849
    OpenUrlCrossRefPubMed
  14. ↵
    1. Cerami E,
    2. Gao J,
    3. Dogrusoz U,
    4. Gross BE,
    5. Sumer SO,
    6. Aksoy BA,
    7. Jacobsen A,
    8. Byrne CJ,
    9. Heuer ML,
    10. Larsson E,
    11. Antipin Y,
    12. Reva B,
    13. Goldberg AP,
    14. Sander C and
    15. Schultz N
    : The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2(5): 401-404, 2012. PMID: 22588877. DOI: 10.1158/2159-8290.CD-12-0095
    OpenUrlAbstract/FREE Full Text
  15. ↵
    1. Gao J,
    2. Aksoy BA,
    3. Dogrusoz U,
    4. Dresdner G,
    5. Gross B,
    6. Sumer SO,
    7. Sun Y,
    8. Jacobsen A,
    9. Sinha R,
    10. Larsson E,
    11. Cerami E,
    12. Sander C and
    13. Schultz N
    : Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 6(269): pl1, 2013. PMID: 23550210. DOI: 10.1126/scisignal.2004088
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Cirenajwis H,
    2. Ekedahl H,
    3. Lauss M,
    4. Harbst K,
    5. Carneiro A,
    6. Enoksson J,
    7. Rosengren F,
    8. Werner-Hartman L,
    9. Törngren T,
    10. Kvist A,
    11. Fredlund E,
    12. Bendahl PO,
    13. Jirström K,
    14. Lundgren L,
    15. Howlin J,
    16. Borg Å,
    17. Gruvberger-Saal SK,
    18. Saal LH,
    19. Nielsen K,
    20. Ringnér M,
    21. Tsao H,
    22. Olsson H,
    23. Ingvar C,
    24. Staaf J and
    25. Jönsson G
    : Molecular stratification of metastatic melanoma using gene expression profiling: Prediction of survival outcome and benefit from molecular targeted therapy. Oncotarget 6(14): 12297-12309, 2015. PMID: 25909218. DOI: 10.18632/oncotarget.3655
    OpenUrlCrossRefPubMed
  17. ↵
    1. Li T,
    2. Fan J,
    3. Wang B,
    4. Traugh N,
    5. Chen Q,
    6. Liu JS,
    7. Li B and
    8. Liu XS
    : TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 77(21): e108-e110, 2017. PMID: 29092952. DOI: 10.1158/0008-5472.CAN-17-0307
    OpenUrlAbstract/FREE Full Text
  18. ↵
    1. Park SJ,
    2. Yoon BH,
    3. Kim SK and
    4. Kim SY
    : GENT2: an updated gene expression database for normal and tumor tissues. BMC Med Genomics 12(Suppl 5): 101, 2019. PMID: 31296229. DOI: 10.1186/s12920-019-0514-7
    OpenUrlCrossRefPubMed
  19. ↵
    1. Bogunovic D,
    2. O’Neill DW,
    3. Belitskaya-Levy I,
    4. Vacic V,
    5. Yu YL,
    6. Adams S,
    7. Darvishian F,
    8. Berman R,
    9. Shapiro R,
    10. Pavlick AC,
    11. Lonardi S,
    12. Zavadil J,
    13. Osman I and
    14. Bhardwaj N
    : Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. Proc Natl Acad Sci USA 106(48): 20429-20434, 2009. PMID: 19915147. DOI: 10.1073/pnas.0905139106
    OpenUrlAbstract/FREE Full Text
  20. ↵
    1. Jönsson G,
    2. Busch C,
    3. Knappskog S,
    4. Geisler J,
    5. Miletic H,
    6. Ringnér M,
    7. Lillehaug JR,
    8. Borg A and
    9. Lønning PE
    : Gene expression profiling-based identification of molecular subtypes in stage IV melanomas with different clinical outcome. Clin Cancer Res 16(13): 3356-3367, 2010. PMID: 20460471. DOI: 10.1158/1078-0432.CCR-09-2509
    OpenUrlAbstract/FREE Full Text
  21. ↵
    1. Saltz J,
    2. Gupta R,
    3. Hou L,
    4. Kurc T,
    5. Singh P,
    6. Nguyen V,
    7. Samaras D,
    8. Shroyer KR,
    9. Zhao T,
    10. Batiste R,
    11. Van Arnam J, Cancer Genome Atlas Research Network.,
    12. Shmulevich I,
    13. Rao AUK,
    14. Lazar AJ,
    15. Sharma A and
    16. Thorsson V
    : Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep 23(1): 181-193.e7, 2018. PMID: 29617659. DOI: 10.1016/j.celrep.2018.03.086
    OpenUrlCrossRefPubMed
  22. ↵
    1. Finotello F,
    2. Mayer C,
    3. Plattner C,
    4. Laschober G,
    5. Rieder D,
    6. Hackl H,
    7. Krogsdam A,
    8. Loncova Z,
    9. Posch W,
    10. Wilflingseder D,
    11. Sopper S,
    12. Ijsselsteijn M,
    13. Brouwer TP,
    14. Johnson D,
    15. Xu Y,
    16. Wang Y,
    17. Sanders ME,
    18. Estrada MV,
    19. Ericsson-Gonzalez P,
    20. Charoentong P,
    21. Balko J,
    22. de Miranda NFDCC and
    23. Trajanoski Z
    : Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. Genome Med 11(1): 34, 2019. PMID: 31126321. DOI: 10.1186/s13073-019-0638-6
    OpenUrlCrossRefPubMed
  23. ↵
    1. Li T,
    2. Fu J,
    3. Zeng Z,
    4. Cohen D,
    5. Li J,
    6. Chen Q,
    7. Li B and
    8. Liu XS
    : TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res 48(W1): W509-W514, 2020. PMID: 32442275. DOI: 10.1093/nar/gkaa407
    OpenUrlCrossRefPubMed
  24. ↵
    1. Hölzel M,
    2. Landsberg J,
    3. Glodde N,
    4. Bald T,
    5. Rogava M,
    6. Riesenberg S,
    7. Becker A,
    8. Jönsson G and
    9. Tüting T
    : A preclinical model of malignant peripheral nerve sheath tumor-like melanoma is characterized by infiltrating mast cells. Cancer Res 76(2): 251-263, 2016. PMID: 26511633. DOI: 10.1158/0008-5472.CAN-15-1090
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Tang Z,
    2. Kang B,
    3. Li C,
    4. Chen T and
    5. Zhang Z
    : GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 47(W1): W556-W560, 2019. PMID: 31114875. DOI: 10.1093/nar/gkz430
    OpenUrlCrossRefPubMed
  26. ↵
    1. Liu Z,
    2. Liu JQ,
    3. Shi Y,
    4. Zhu X,
    5. Liu Z,
    6. Li MS,
    7. Yu J,
    8. Wu LC,
    9. He Y,
    10. Zhang G and
    11. Bai XF
    : Epstein-Barr virus-induced gene 3-deficiency leads to impaired antitumor T-cell responses and accelerated tumor growth. Oncoimmunology 4(7): e989137, 2015. PMID: 26140252. DOI: 10.4161/2162402X.2014.989137
    OpenUrlCrossRefPubMed
  27. ↵
    1. Brożyna AA,
    2. Jóźwicki W,
    3. Roszkowski K,
    4. Filipiak J and
    5. Slominski AT
    : Melanin content in melanoma metastases affects the outcome of radiotherapy. Oncotarget 7(14): 17844-17853, 2016. PMID: 26910282. DOI: 10.18632/oncotarget.7528
    OpenUrlCrossRefPubMed
    1. Slominski A,
    2. Kim TK,
    3. Brożyna AA,
    4. Janjetovic Z,
    5. Brooks DL,
    6. Schwab LP,
    7. Skobowiat C,
    8. Jóźwicki W and
    9. Seagroves TN
    : The role of melanogenesis in regulation of melanoma behavior: melanogenesis leads to stimulation of HIF-1α expression and HIF-dependent attendant pathways. Arch Biochem Biophys 563: 79-93, 2014. PMID: 24997364. DOI: 10.1016/j.abb.2014.06.030
    OpenUrlCrossRefPubMed
    1. Slominski A,
    2. Zbytek B and
    3. Slominski R
    : Inhibitors of melanogenesis increase toxicity of cyclophosphamide and lymphocytes against melanoma cells. Int J Cancer 124(6): 1470-1477, 2009. PMID: 19085934. DOI: 10.1002/ijc.24005
    OpenUrlCrossRefPubMed
  30. ↵
    1. Slominski RM,
    2. Zmijewski MA and
    3. Slominski AT
    : The role of melanin pigment in melanoma. Exp Dermatol 24(4): 258-259, 2015. PMID: 25496715. DOI: 10.1111/exd.12618
    OpenUrlCrossRefPubMed
  31. ↵
    1. Brożyna AA,
    2. Jóźwicki W,
    3. Carlson JA and
    4. Slominski AT
    : Melanogenesis affects overall and disease-free survival in patients with stage III and IV melanoma. Hum Pathol 44(10): 2071-2074, 2013. PMID: 23791398. DOI: 10.1016/j.humpath.2013.02.022
    OpenUrlCrossRefPubMed
  32. ↵
    1. Donizy P,
    2. Kaczorowski M,
    3. Halon A,
    4. Leskiewicz M,
    5. Kozyra C and
    6. Matkowski R
    : Paucity of tumor-infiltrating lymphocytes is an unfavorable prognosticator and predicts lymph node metastases in cutaneous melanoma patients. Anticancer Res 35(1): 351-358, 2015. PMID: 25550571.
    OpenUrlAbstract/FREE Full Text
  33. ↵
    1. Oble DA,
    2. Loewe R,
    3. Yu P and
    4. Mihm MC Jr.
    : Focus on TILs: prognostic significance of tumor infiltrating lymphocytes in human melanoma. Cancer Immun 9: 3, 2009. PMID: 19338264.
    OpenUrlAbstract/FREE Full Text
  34. ↵
    1. Liu Z,
    2. Liu JQ,
    3. Talebian F,
    4. Wu LC,
    5. Li S and
    6. Bai XF
    : IL-27 enhances the survival of tumor antigen-specific CD8+ T cells and programs them into IL-10-producing, memory precursor-like effector cells. Eur J Immunol 43(2): 468-479, 2013. PMID: 23225163. DOI: 10.1002/eji.201242930
    OpenUrlCrossRefPubMed
    1. Morishima N,
    2. Owaki T,
    3. Asakawa M,
    4. Kamiya S,
    5. Mizuguchi J and
    6. Yoshimoto T
    : Augmentation of effector CD8+ T cell generation with enhanced granzyme B expression by IL-27. J Immunol 175(3): 1686-1693, 2005. PMID: 16034109. DOI: 10.4049/jimmunol.175.3.1686
    OpenUrlAbstract/FREE Full Text
  36. ↵
    1. Murugaiyan G and
    2. Saha B
    : IL-27 in tumor immunity and immunotherapy. Trends Mol Med 19(2): 108-116, 2013. PMID: 23306374. DOI: 10.1016/j.molmed.2012.12.002
    OpenUrlCrossRefPubMed
  37. ↵
    1. Zeng JC,
    2. Zhang Z,
    3. Li TY,
    4. Liang YF,
    5. Wang HM,
    6. Bao JJ,
    7. Zhang JA,
    8. Wang WD,
    9. Xiang WY,
    10. Kong B,
    11. Wang ZY,
    12. Wu BH,
    13. Chen XD,
    14. He L,
    15. Zhang S,
    16. Wang CY and
    17. Xu JF
    : Assessing the role of IL-35 in colorectal cancer progression and prognosis. Int J Clin Exp Pathol 6(9): 1806-1816, 2013. PMID: 24040445.
    OpenUrlPubMed
  38. ↵
    1. Nicholl MB,
    2. Ledgewood CL,
    3. Chen X,
    4. Bai Q,
    5. Qin C,
    6. Cook KM,
    7. Herrick EJ,
    8. Diaz-Arias A,
    9. Moore BJ and
    10. Fang Y
    : IL-35 promotes pancreas cancer growth through enhancement of proliferation and inhibition of apoptosis: evidence for a role as an autocrine growth factor. Cytokine 70(2): 126-133, 2014. PMID: 25073578. DOI: 10.1016/j.cyto.2014.06.020
    OpenUrlCrossRefPubMed
Back to top

In this issue

Print
Download PDF
Article Alerts
Email Article
Citation Tools
Reprints and Permissions
Epstein-Barr Virus-induced Gene 3 as a Novel Biomarker in Metastatic Melanoma With Infiltrating CD8+ T Cells: A Study Based on The Cancer Genome Atlas (TCGA)
SATORU YONEKURA
Anticancer Research Jan 2022, 42 (1) 511-517; DOI: 10.21873/anticanres.15509
Twitter logo Facebook logo Mendeley logo

Jump to section

Keywords