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Downloaded from the TCGA Data Portal Expressed Genes (Degs) Between Primary Melanoma and (Https​://Tcga-Data.Nci.Nih.Gov/Tcga/) [10]

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Downloaded from the TCGA Data Portal Expressed Genes (Degs) Between Primary Melanoma and (Https​://Tcga-Data.Nci.Nih.Gov/Tcga/) [10] Huang et al. Cancer Cell Int (2020) 20:195 https://doi.org/10.1186/s12935-020-01271-2 Cancer Cell International PRIMARY RESEARCH Open Access Identifcation of immune-related biomarkers associated with tumorigenesis and prognosis in cutaneous melanoma patients Biao Huang1,2,3† , Wei Han1,2,3†, Zu‑Feng Sheng1,2,3 and Guo‑Liang Shen1,2* 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 metasta‑ sis, 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 diferential expression levels of hub genes between SKCM tissue and normal tissues from the First Afliated Hospital of Soochow University cohort. Results: A total of 308 diferentially expressed genes (DEGs) and 12 hub genes were found signifcantly diferentially expressed between SKCM and normal skin tissues. Functional annotation indicated that infammatory response, immune response was closely associated with SKCM tumorigenesis. KEGG pathways in hub genes include IL‑10 sign‑ aling 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 signifcantly elevated CXCL9, CXCL10, CXCL13, CCL4 and CCL5 proteins expressed in the SKCM than in the normal tissues. Moreo‑ ver, our fndings 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 infltration 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 signifcantly related to longer SKCM survival time. Conclusion: In summary, this study mainly identifed fve chemokine members (CXCL9, CXCL10, CXCL13, CCL4, CCL5) associated with SKCM tumorigenesis, progression, prognosis and immune infltrations, which might help us evaluate several immune‑related targets for cutaneous melanoma therapy. Keywords: Cutaneous melanoma, Biomarker, Prognosis, Chemokines, Immune, Infltration Background *Correspondence: [email protected] Skin cutaneous melanoma (SKCM) accounts for only †Biao Huang and Wei Han contribute equally 1 Department of Burn and Plastic Surgery, The First Afliated Hospital 2% of total skin cancers. However, due to its high degree of Soochow University, No. 188 Shizi Street, Suzhou 215000, People’s of malignancy and invasiveness, it causes over 72% of Republic of China deaths in skin carcinoma [1]. Te incidence of cutane- Full list of author information is available at the end of the article ous malignant melanoma continues to increase annually © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/publi cdoma in/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Huang et al. Cancer Cell Int (2020) 20:195 Page 2 of 15 [2]. Melanoma has become a serious public health prob- patients with primary melanoma (PM) and normal skin lem, bringing great economic burden for society [3]. It is (NS), (b) detection of gene level in tissue or blood sam- well known that melanoma is associated with multiple ples. Exclusion criteria included: (a) clinical data without risk factors especially the sun exposure [4]. Te general survival time and outcome, and (b) datasets with small progression models of SKCM are from melanocyte to sample sizes (n < 15). Finally, three datasets were eligible: melanoma in situ, to invasive melanoma [5]. However, accession numbers GSE15605 (46 PM samples and 16 extensive research has explored the mechanism of recur- NS samples), GSE46517 (31 PM samples and 8 NS sam- rence and metastasis, the tumorigenesis of cutaneous ples) and GSE114445 (16 PM samples and 6 NS samples). melanoma remains unclear. For validation, gene expression profles of 472 melanoma In the present study, we analyzed the diferentially patients were downloaded from the TCGA data portal expressed genes (DEGs) between primary melanoma and (https ://tcga-data.nci.nih.gov/tcga/) [10]. Clinical char- normal skin to explore the potential tumorigenesis mech- acteristics of patients were also obtained, including age, anism of SKCM. Our results mainly identifed several Clark level, Breslow depth, survival time, and outcome. chemokine family members (CXCL9, CXCL10, CXCL13, All of the clinicopathological characteristics of SKCM CCL4, CCL5) which were found related to better over- patients were shown in Table 1. all survival (OS) in SKCM patients. Chemokine family members are a group of low-molecular weight cytokines Identifcation of DEGs which were involved in many biological processes includ- Te DEGs between primary melanoma and normal skin ing angiogenesis, tumor development and metastasis, were identifed using GEO2R (http://www.ncbi.nlm.nih. and the migration of leukocytes [6, 7]. In addition, we gov/geo/geo2r ) based on limma package with the thresh- found that their expression levels were positively associ- old of|logFC| > 1 and P-value < 0.05. Next, the online ated with infltration of immune cells (CD4 +T, CD8+T, Venn software (http://bioin forma tics.psb.ugent .be/ B-cell, macrophages, neutrophils, dendritic cells) and webto ols/Venn/) was applied to detect the overlap DEGs tumor infltrating lymphocytes (TILs). Tese immune among three datasets. infltration cells play important roles in tumor microen- vironment and can directly or indirectly regulate tumor DAVID database immunity and modulate tumor immunological for anti- To elucidate the biological functions of the overlapping tumor efects [7, 8]. Terefore, our results may identify DEGs, we performed functional annotation and path- several immune-related biomarkers that may serve to way enrichment analysis via DAVID (Te Database for guide SKCM therapy. Annotation, Visualization and Integrated Discovery, http://david .ncifc rf.gov/,versi on 6.8) online tool [11]. Methods P-value < 0.05 was considered as the cutof value. GO Patients and variables enrichment and KEGG pathway results were visualized A total of 46 melanoma and 46 normal tissues were as bubble charts by R software. obtained from 92 patients at the Department of Burn and Plastic Surgery, the First Afliated Hospital of Soochow STRING and cytoscape University (FAHSU, Suzhou, China) from March 2015 STRING (http://strin g-db.org, version 11.0) database was to August 2019. None of the patients had received radio- used to predict the PPI network of DEGs and analyze the therapy or chemotherapy before operation. Tissue sam- interactions between proteins [12]. An interaction with ples, including cutaneous melanoma and normal tissue, a combined score > 0.4 was recognized as statistical sig- were collected during surgery and fxed in 4% paraform- nifcance. Te molecular interaction networks were visu- aldehyde, available from FAHSU tissue bank. Clinical alized using the Cytoscape (version 3.7.0) and the most data was available to obtain from hospital records. Tis signifcant model in the PPI network was narrowed down research was supported by the Independent Ethics Com- by MCODE, with the following criteria: degree cutof = 2, mittee (IEC) of the FAHSU and all patients were well node score cutof = 0.2, k-core = 2, max depth = 100 [13, informed of storing and upcoming use of their resected 14]. specimens for further research purposes. Functional enrichment GEO and TCGA datasets Metascape (https ://metas cape.org) was used to fur- Expression profling of SKCM patients with clinical infor- ther verify the function enrichment of hub genes [15]. mation was obtained from the Gene Expression Omni- P < 0.05 was set as the cutof value. Hub genes pathway bus (GEO) database (http://www.ncbi.nlm.nih.gov/geo) analysis was performed and visualized by ClueGO (ver- [9]. Among the inclusion criteria were (a) diagnosis of sion 2.5.4) and CluePedia (version 1.5.4), the plug‐in of Huang et al. Cancer Cell Int (2020) 20:195 Page 3 of 15 Table 1 Clinicopathological characteristics of SKCM based on the data from TCGA database. Student’s t test patients was used to analyze the correlation between the expres- Characteristics FAHSU GEO cohort (N 93) TCGA sion and pathological stages. P value < 0.05 was consid- = cohort cohort ered statistically signifcant. (N 46) (N 472) = = N (%) Immunohistochemistry (IHC) Age Protein expression levels of hub genes were measured 60 years 21 (45.6) 53 (56.9) 258 (54.7) using IHC staining and rabbit polyclonal anti-CCL4 ≤ > 60 years 25 (54.4) 40 (43.1) 214 (45.3) antibody (ab235978), anti-CCL5 antibody (ab9679), Gender anti-CXCL9 antibody (ab9720), anti-CXCL10 antibody Male 30 (65.2) 55 (59.1) 297 (61.9) (ab9807), anti-CXCL13 antibody (ab272874).
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