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Published in: Cancer Cell International 1/2020

01-12-2020 | Melanoma | Primary research

Identification of immune-related biomarkers associated with tumorigenesis and prognosis in cutaneous melanoma patients

Authors: Biao Huang, Wei Han, Zu-Feng Sheng, Guo-Liang Shen

Published in: Cancer Cell International | Issue 1/2020

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Abstract

Background

Skin cutaneous melanoma (SKCM) is one of the most malignant and aggressive cancers, causing about 72% of deaths in skin carcinoma. Although extensive study has explored the mechanism of recurrence and metastasis, the tumorigenesis of cutaneous melanoma remains unclear. Exploring the tumorigenesis mechanism may help identify prognostic biomarkers that could serve to guide cancer therapy.

Method

Integrative bioinformatics analyses, including GEO database, TCGA database, DAVID, STRING, Metascape, GEPIA, cBioPortal, TRRUST, TIMER, TISIDB and DGIdb, were performed to unveil the hub genes participating in tumor progression and cancer-associated immunology of SKCM. Furthermore, immunohistochemistry (IHC) staining was performed to validate differential expression levels of hub genes between SKCM tissue and normal tissues from the First Affiliated Hospital of Soochow University cohort.

Results

A total of 308 differentially expressed genes (DEGs) and 12 hub genes were found significantly differentially expressed between SKCM and normal skin tissues. Functional annotation indicated that inflammatory response, immune response was closely associated with SKCM tumorigenesis. KEGG pathways in hub genes include IL-10 signaling and chemokine receptors bind chemokine signaling. Five chemokines members (CXCL9, CXCL10, CXCL13, CCL4, CCL5) were associated with better overall survival and pathological stages. IHC results suggested that significantly elevated CXCL9, CXCL10, CXCL13, CCL4 and CCL5 proteins expressed in the SKCM than in the normal tissues. Moreover, our findings suggested that IRF7, RELA, NFKB1, IRF3 and IRF1 are key transcription factors for CCL4, CCL5, CXCL10. In addition, the expressions of CXCL9, CXCL10, CXCL13, CCL4 and CCL5 were positively correlated with infiltration of six immune cells (B cell, CD8+T cells, CD4+T cells, macrophages, neutrophils, dendritic cells) and 28 types of TILs. Among them, high levels of B cells, CD8+T cells, neutrophils and dendritic cells were significantly related to longer SKCM survival time.

Conclusion

In summary, this study mainly identified five chemokine members (CXCL9, CXCL10, CXCL13, CCL4, CCL5) associated with SKCM tumorigenesis, progression, prognosis and immune infiltrations, which might help us evaluate several immune-related targets for cutaneous melanoma therapy.
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Literature
1.
go back to reference Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30.CrossRef Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30.CrossRef
2.
go back to reference Davies MA, Flaherty KT. Melanoma in 2017: moving treatments earlier to move further forwards. Nat Rev Clin Oncol. 2018;15(2):75–6.PubMedCrossRef Davies MA, Flaherty KT. Melanoma in 2017: moving treatments earlier to move further forwards. Nat Rev Clin Oncol. 2018;15(2):75–6.PubMedCrossRef
3.
go back to reference Tripp MK, Watson M, Balk SJ, Swetter SM, Gershenwald JE. State of the science on prevention and screening to reduce melanoma incidence and mortality: the time is now. CA Cancer J Clin. 2016;66(6):460–80.PubMedPubMedCentralCrossRef Tripp MK, Watson M, Balk SJ, Swetter SM, Gershenwald JE. State of the science on prevention and screening to reduce melanoma incidence and mortality: the time is now. CA Cancer J Clin. 2016;66(6):460–80.PubMedPubMedCentralCrossRef
4.
go back to reference Grossman DC, Curry SJ, Owens DK, Barry MJ, Caughey AB, Davidson KW, et al. Behavioral counseling to prevent skin cancer: US preventive services task force recommendation statement. JAMA Dermatol. 2018;319(11):1134–42. Grossman DC, Curry SJ, Owens DK, Barry MJ, Caughey AB, Davidson KW, et al. Behavioral counseling to prevent skin cancer: US preventive services task force recommendation statement. JAMA Dermatol. 2018;319(11):1134–42.
5.
go back to reference Schadendorf D, van Akkooi ACJ, Berking C, Griewank KG, Gutzmer R, Hauschild A, et al. Melanoma. Lancet Diabetes Endo. 2018;392(10151):971–84. Schadendorf D, van Akkooi ACJ, Berking C, Griewank KG, Gutzmer R, Hauschild A, et al. Melanoma. Lancet Diabetes Endo. 2018;392(10151):971–84.
6.
go back to reference Atretkhany KN, Drutskaya MS, Nedospasov SA, Grivennikov SI, Kuprash DV. Chemokines, cytokines and exosomes help tumors to shape inflammatory microenvironment. Pharmacol Ther. 2016;168:98–112.PubMedCrossRef Atretkhany KN, Drutskaya MS, Nedospasov SA, Grivennikov SI, Kuprash DV. Chemokines, cytokines and exosomes help tumors to shape inflammatory microenvironment. Pharmacol Ther. 2016;168:98–112.PubMedCrossRef
9.
go back to reference Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.PubMedPubMedCentralCrossRef Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.PubMedPubMedCentralCrossRef
10.
go back to reference Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol. 2015;19:A68–77. Tomczak K, Czerwińska P, Wiznerowicz M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol. 2015;19:A68–77.
11.
go back to reference Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 2007;8(9):R183.PubMedPubMedCentralCrossRef Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, et al. The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 2007;8(9):R183.PubMedPubMedCentralCrossRef
12.
go back to reference Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–52 (Database issue).PubMedCrossRef Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–52 (Database issue).PubMedCrossRef
13.
go back to reference Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431–2.PubMedCrossRef Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431–2.PubMedCrossRef
14.
go back to reference Bandettini WP, Kellman P, Mancini C, Booker OJ, Vasu S, Leung SW, et al. MultiContrast Delayed Enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn Reson. 2012;14:83.PubMedPubMedCentralCrossRef Bandettini WP, Kellman P, Mancini C, Booker OJ, Vasu S, Leung SW, et al. MultiContrast Delayed Enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn Reson. 2012;14:83.PubMedPubMedCentralCrossRef
15.
go back to reference Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.PubMedPubMedCentralCrossRef Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.PubMedPubMedCentralCrossRef
16.
go back to reference Bindea G, Galon J, Mlecnik B. CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 2013;29(5):661–3.PubMedPubMedCentralCrossRef Bindea G, Galon J, Mlecnik B. CluePedia Cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 2013;29(5):661–3.PubMedPubMedCentralCrossRef
17.
go back to reference Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45:W98–102.PubMedPubMedCentralCrossRef Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45:W98–102.PubMedPubMedCentralCrossRef
18.
go back to reference Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signaling. 2013;6(269):pl1.CrossRef Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signaling. 2013;6(269):pl1.CrossRef
19.
go back to reference Han H, Cho JW, Lee S, Yun A, Kim H, Bae D, et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2018;46:D380–6.PubMedCrossRef Han H, Cho JW, Lee S, Yun A, Kim H, Bae D, et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2018;46:D380–6.PubMedCrossRef
20.
go back to reference Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–10.PubMedPubMedCentralCrossRef Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–10.PubMedPubMedCentralCrossRef
21.
go back to reference Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35(20):4200–2.PubMedCrossRef Ru B, Wong CN, Tong Y, Zhong JY, Zhong SSW, Wu WC, et al. TISIDB: an integrated repository portal for tumor-immune system interactions. Bioinformatics. 2019;35(20):4200–2.PubMedCrossRef
22.
go back to reference Wagner AH, Coffman AC, Ainscough BJ, Spies NC, Skidmore ZL, Campbell KM, et al. DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res. 2016;44:D1036–44.PubMedCrossRef Wagner AH, Coffman AC, Ainscough BJ, Spies NC, Skidmore ZL, Campbell KM, et al. DGIdb 2.0: mining clinically relevant drug-gene interactions. Nucleic Acids Res. 2016;44:D1036–44.PubMedCrossRef
23.
go back to reference Euvrard S, Kanitakis J, Claudy A. Skin cancers after organ transplantation. N Engl J Med. 2003;348(17):1681–91.PubMedCrossRef Euvrard S, Kanitakis J, Claudy A. Skin cancers after organ transplantation. N Engl J Med. 2003;348(17):1681–91.PubMedCrossRef
24.
go back to reference Weir HK, Thompson TD, Soman A, Møller B, Leadbetter S, White MC. Meeting the healthy people 2020 objectives to reduce cancer mortality. Prev Chronic Dis. 2015;12:E104.PubMedPubMedCentralCrossRef Weir HK, Thompson TD, Soman A, Møller B, Leadbetter S, White MC. Meeting the healthy people 2020 objectives to reduce cancer mortality. Prev Chronic Dis. 2015;12:E104.PubMedPubMedCentralCrossRef
25.
go back to reference Pelster MS, Amaria RN. Combined targeted therapy and immunotherapy in melanoma: a review of the impact on the tumor microenvironment and outcomes of early clinical trials. Ther Adv Med Oncol. 2019;11:1758835919830826.PubMedPubMedCentralCrossRef Pelster MS, Amaria RN. Combined targeted therapy and immunotherapy in melanoma: a review of the impact on the tumor microenvironment and outcomes of early clinical trials. Ther Adv Med Oncol. 2019;11:1758835919830826.PubMedPubMedCentralCrossRef
26.
go back to reference Lebbé C, Meyer N, Mortier L, Marquez-Rodas I, Robert C, Rutkowski P, et al. Evaluation of two dosing regimens for nivolumab in combination with ipilimumab in patients with advanced melanoma: results from the phase iiib/iv checkmate 511 trial. J Clin Oncol. 2019;37(11):867–75.PubMedPubMedCentralCrossRef Lebbé C, Meyer N, Mortier L, Marquez-Rodas I, Robert C, Rutkowski P, et al. Evaluation of two dosing regimens for nivolumab in combination with ipilimumab in patients with advanced melanoma: results from the phase iiib/iv checkmate 511 trial. J Clin Oncol. 2019;37(11):867–75.PubMedPubMedCentralCrossRef
27.
go back to reference Tucci M, Passarelli A, Mannavola F, Felici C, Stucci LS, Cives M, et al. Immune system evasion as hallmark of melanoma progression: the role of dendritic cells. Front Oncol. 2019;9:1148.PubMedPubMedCentralCrossRef Tucci M, Passarelli A, Mannavola F, Felici C, Stucci LS, Cives M, et al. Immune system evasion as hallmark of melanoma progression: the role of dendritic cells. Front Oncol. 2019;9:1148.PubMedPubMedCentralCrossRef
29.
go back to reference Smith LK, Boukhaled GM, Condotta SA, Mazouz S, Guthmiller JJ, Vijay R, et al. Interleukin-10 directly inhibits CD8 T cell function by enhancing N-glycan branching to decrease antigen sensitivity. Immunity. 2018;48(2):299–312.e295.PubMedPubMedCentralCrossRef Smith LK, Boukhaled GM, Condotta SA, Mazouz S, Guthmiller JJ, Vijay R, et al. Interleukin-10 directly inhibits CD8 T cell function by enhancing N-glycan branching to decrease antigen sensitivity. Immunity. 2018;48(2):299–312.e295.PubMedPubMedCentralCrossRef
30.
go back to reference Groom JR, Richmond J, Murooka TT, Sorensen EW, Sung JH, Bankert K, et al. CXCR3 chemokine receptor-ligand interactions in the lymph node optimize CD+ T helper 1 cell differentiation. Immunity. 2012;37(6):1091–103.PubMedPubMedCentralCrossRef Groom JR, Richmond J, Murooka TT, Sorensen EW, Sung JH, Bankert K, et al. CXCR3 chemokine receptor-ligand interactions in the lymph node optimize CD+ T helper 1 cell differentiation. Immunity. 2012;37(6):1091–103.PubMedPubMedCentralCrossRef
31.
go back to reference Griffith JW, Sokol CL, Luster AD. Chemokines and chemokine receptors: positioning cells for host defense and immunity. Annu Rev Immunol. 2014;32:659–702.PubMedCrossRef Griffith JW, Sokol CL, Luster AD. Chemokines and chemokine receptors: positioning cells for host defense and immunity. Annu Rev Immunol. 2014;32:659–702.PubMedCrossRef
32.
go back to reference House IG, Savas P, Lai J, Chen AXY, Oliver AJ, Teo ZL, et al. Macrophage-derived CXCL9 and CXCL10 are required for antitumor immune responses following immune checkpoint blockade. Clin Cancer Res. 2020;26(2):487–504.PubMedCrossRef House IG, Savas P, Lai J, Chen AXY, Oliver AJ, Teo ZL, et al. Macrophage-derived CXCL9 and CXCL10 are required for antitumor immune responses following immune checkpoint blockade. Clin Cancer Res. 2020;26(2):487–504.PubMedCrossRef
33.
go back to reference Doron H, Amer M, Ershaid N, Blazquez R, Shani O, Lahav TG, et al. Inflammatory activation of astrocytes facilitates melanoma brain tropism via the CXCL10-CXCR3 signaling axis. Cell Rep. 2019;28(7):1785–1798.e1786.PubMedCrossRef Doron H, Amer M, Ershaid N, Blazquez R, Shani O, Lahav TG, et al. Inflammatory activation of astrocytes facilitates melanoma brain tropism via the CXCL10-CXCR3 signaling axis. Cell Rep. 2019;28(7):1785–1798.e1786.PubMedCrossRef
34.
go back to reference Curran MA, Montalvo W, Yagita H, Allison JP. PD-1 and CTLA-4 combination blockade expands infiltrating T cells and reduces regulatory T and myeloid cells within B16 melanoma tumors. Proc Natl Acad Sci USA. 2010;107(9):4275–80.PubMedCrossRef Curran MA, Montalvo W, Yagita H, Allison JP. PD-1 and CTLA-4 combination blockade expands infiltrating T cells and reduces regulatory T and myeloid cells within B16 melanoma tumors. Proc Natl Acad Sci USA. 2010;107(9):4275–80.PubMedCrossRef
35.
go back to reference Harlin H, Meng Y, Peterson AC, Zha Y, Tretiakova M, Slingluff C, et al. Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. Cancer Res. 2009;69(7):3077–85.PubMedCrossRef Harlin H, Meng Y, Peterson AC, Zha Y, Tretiakova M, Slingluff C, et al. Chemokine expression in melanoma metastases associated with CD8+ T-cell recruitment. Cancer Res. 2009;69(7):3077–85.PubMedCrossRef
36.
go back to reference Hong M, Puaux AL, Huang C, Loumagne L, Tow C, Mackay C, et al. Chemotherapy induces intratumoral expression of chemokines in cutaneous melanoma, favoring T-cell infiltration and tumor control. Cancer Res. 2011;71(22):6997–7009.PubMedCrossRef Hong M, Puaux AL, Huang C, Loumagne L, Tow C, Mackay C, et al. Chemotherapy induces intratumoral expression of chemokines in cutaneous melanoma, favoring T-cell infiltration and tumor control. Cancer Res. 2011;71(22):6997–7009.PubMedCrossRef
37.
go back to reference Noman MZ, Berchem G, Janji B. Targeting autophagy blocks melanoma growth by bringing natural killer cells to the tumor battlefield. Autophagy. 2018;14(4):730–2.PubMedPubMedCentralCrossRef Noman MZ, Berchem G, Janji B. Targeting autophagy blocks melanoma growth by bringing natural killer cells to the tumor battlefield. Autophagy. 2018;14(4):730–2.PubMedPubMedCentralCrossRef
38.
go back to reference Lu X, Yarbrough WG. Negative regulation of RelA phosphorylation: emerging players and their roles in cancer. Cytokine Growth Factor Rev. 2015;26(1):7–13.PubMedCrossRef Lu X, Yarbrough WG. Negative regulation of RelA phosphorylation: emerging players and their roles in cancer. Cytokine Growth Factor Rev. 2015;26(1):7–13.PubMedCrossRef
39.
go back to reference Cartwright T, Perkins ND, Wilson L. NFKB1: a suppressor of inflammation, ageing and cancer. FEBS J. 2016;283(10):1812–22.PubMedCrossRef Cartwright T, Perkins ND, Wilson L. NFKB1: a suppressor of inflammation, ageing and cancer. FEBS J. 2016;283(10):1812–22.PubMedCrossRef
41.
go back to reference Rivadeneira DB, DePeaux K, Wang Y, Kulkarni A, Tabib T, Menk AV, et al. Oncolytic viruses engineered to enforce leptin expression reprogram tumor-infiltrating T cell metabolism and promote tumor clearance. Immunity. 2019;51(3):548–560.e544.PubMedCrossRef Rivadeneira DB, DePeaux K, Wang Y, Kulkarni A, Tabib T, Menk AV, et al. Oncolytic viruses engineered to enforce leptin expression reprogram tumor-infiltrating T cell metabolism and promote tumor clearance. Immunity. 2019;51(3):548–560.e544.PubMedCrossRef
42.
go back to reference Thomas NE, Busam KJ, From L, Kricker A, Armstrong BK, Anton-Culver H, et al. Tumor-infiltrating lymphocyte grade in primary melanomas is independently associated with melanoma-specific survival in the population-based genes, environment and melanoma study. J Clin Oncol. 2013;31(33):4252–9.PubMedPubMedCentralCrossRef Thomas NE, Busam KJ, From L, Kricker A, Armstrong BK, Anton-Culver H, et al. Tumor-infiltrating lymphocyte grade in primary melanomas is independently associated with melanoma-specific survival in the population-based genes, environment and melanoma study. J Clin Oncol. 2013;31(33):4252–9.PubMedPubMedCentralCrossRef
Metadata
Title
Identification of immune-related biomarkers associated with tumorigenesis and prognosis in cutaneous melanoma patients
Authors
Biao Huang
Wei Han
Zu-Feng Sheng
Guo-Liang Shen
Publication date
01-12-2020
Publisher
BioMed Central
Published in
Cancer Cell International / Issue 1/2020
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-020-01271-2

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