Prognostic value of the expression of chemokines and their receptors in regional lymph nodes of melanoma patients

Abstract Chemokines and their receptors have been reported to drive immune cells into tumours or to be directly involved in the promotion or inhibition of the development of tumours. However, their expression in regional lymph node (LN) tissues in melanoma patients remains unknown. The present study investigated the relationship between the expression of mRNA of chemokines and their receptors and clinicopathology of the regional LN tissues of skin cutaneous melanoma (SKCM) patients available in The Cancer Genome Atlas. The relationship between chemokines and their receptors and the composition of immune cells within the tumour was analysed. In SKCM regional LN tissues, the high expression of 32 types of chemokines and receptors, namely CCL2, 4‐5, 7‐8, 13, 22‐25, CCR1‐9, CXCL9‐13, 16, CXCR3, 5, 6, XCL1‐2 and XCR1 in LN was associated with favourable patient prognosis. Conversely, high expression of CXCL17 was an indicator of poor prognosis. The expression of mRNA for CXCL9‐11, 13, CXCR3, 6, CCL2, 4, 5, 7, 8, 25, CCR1, 2, 5, and XCL1, 2 in regional LN tissues was positively correlated with the fraction of CD8‐positive T cells and M1 macrophages, and was negatively correlated with M0 macrophages. CCR4, 6‐9, CCL13, 22, 23 and XCR1 were positively correlated with the fraction of memory B cells and naive T cells, and negatively correlated with M0 macrophages and resting mast cells, suggesting that chemokines and their receptors may affect the prognosis of patients by guiding immune cells into the tumour microenvironment to eliminate tumour cells.


| INTRODUC TI ON
In many malignancies, including skin cutaneous melanoma (SKCM), enhanced infiltration of the tumour by an immune cell is typically associated with good prognosis. 1,2 Tumour-infiltrating lymphocytes (TIL) represent the response of the host organism to the tumour. When a tumour develops, the body can react by mobilizing the immune system, and the prognosis of the patient depends on whether the immune cells can generate an effective anti-tumour response. The destruction of the tumour is dependent on the ability of immune cells to migrate to the site of its location and infiltration of the cancerous tissue. Tumour microenvironment (TME) comprises diverse cells types, including cancer stromal cells, fibroblasts, lymphocytes, granulocytes, macrophages, mast 3 In most cases, the presence of B lymphocytes, cytotoxic CD8-positive T lymphocytes, NKs, 'M1-like' macrophages and high numbers of DCs are indicative of a favourable outcome. CD8-positive T cells are the main effector of anti-tumour immunity. They recognize and destruct tumour cells carrying specific antigens, which are the product of the expression of mutated genes. 4,5 Conversely, 'M2-like' macrophages, granulocytes, MCs, MDSCs, immature DCs, regulatory T cells (Tregs) and TH17 lymphocyte high density are associated with poor prognosis. 6 Lymph nodes (LNs) are an integral part of the immune system in humans and are essential for the maintenance of effective immune responses. LNs are penetrated by networks of fibres formed by fibroblastic reticular cells (FRCs). These structures provide a basis for the transport of small molecules such as chemokines and soluble antigens with molecular mass less than 70 kD, enabling the mediation of inflammatory response or immune function by chemokines. 7 Chemokines, the largest family of cytokines, constitute a class of low molecular weight secreted proteins capable of inducing directional migration of cells. When immune cells and tissue cells, including fibroblasts, endothelial cells and epidermal cells are induced by stimuli such as growth factors, interferons, and viral and bacterial products, different chemokines can be secreted. 8,9 In the TME, both tumour and immune cells express chemokines, which can lead to the spread of tumour cells. On the other hand, chemokines can promote the entry of specific immune cells into tumours, facilitating the anti-tumour response and improving the prognosis of patients. 1,10 In this regard, the outcome of the disease in SKCM patients has been demonstrated to depend on the infiltration of lymphocytes into the tumour in SKCM patients, a process that is affected by chemokine or cytokine gradients. 11,12 The generation of an effective anti-tumour immune response depends on the synergy between different immune cells, and their transport and distribution are co-ordinated by the interaction between chemokines and their receptors. For example, CCL19 and CCL21 chemokines activate naive T cells, B cells, F I G U R E 1 Study flowchart mature DC cells and NK cells via the CCR7 receptor, inducing their migration to secondary lymphoid organs (SLO). 13 Immature DCs express CXCR1, CCR1, CCR2 and CCR6 receptors, and inflammatory chemokines acting as their ligands recruit these cells to the site of inflammation. 14 B cells express the chemokine receptor CXCR5, and the ligand of CXCR5 promotes the homing of B cells into LNs. 14 CD8-positive T cells express the chemokine receptor CXCR3, which, when bound by the chemokine ligands CXCL9 and 10, drives their migration to the tumour. 15 Increased levels of CXCL9-11 are associated with a higher number of CD8-positive T cells infiltrating the tumour, decreased metastatic activity and improved survival of cancer patients. 16 CIBERSORT, an analytical tool for estimating the relative abundance of different cell types based on RNA transcripts, allows calculating cell infiltration in tissues based on gene expression profile data. In comparison with traditional methods, CIBERSORT has the advantage of simultaneous assessment of multiple types of infiltrating cells. This approach is not affected by the expression of the same surface marker by different cell types. Moreover, samples can be easily processed and stored in a standardized manner, alleviating problems that negatively affect the quality of data collected at different times and locations. 17,18 The results obtained using CIBERSORT to calculate lymphocytic infiltration are consistent with the data generated by flow cytometry, and this methodology has been applied to the study of multiple diseases. 17,18 The basic matrix in CIBERSORT, LM22, permits the relative quantitation of 22 cell types, including T cells, naive and memory B cells, plasma cells and subpopulations of myeloid cells. 19 The research on the role of chemokines in SKCM is sporadic.

cells (MCs), natural killer cells (NKs), dendritic cells (DCs) and myeloid-derived suppressor cells (MDSCs).
The number of studies on chemokines and chemokine receptors in metastatic regional LN tissue is limited as well. In view of the paucity of relevant information, the present study focused on the

| Data acquisition
The SKCM gene expression data set available on the TCGA web-  (Table 1).

| Evaluation of immune cell components using CIBERSORT
The CIBERSORT website (http://ciber sort.stanf ord.edu) provides R language computing source code, as well as the basic matrix (LM22).
The R language programs include preprocessCore and BiocManager package. The statistical rank was set to 1000 (recommended value is >100) in the R language program, and quantile normalization was disabled. Subsequently, the lymphocyte infiltration ratio of 22 distinct cell types was calculated, with the sum of fractions equal 1.
P-value was determined for the tissue infiltration score in each patient, and P < .05 were considered to be statistically significant.

| LASSO scores for chemokine and its receptor mRNA expression
Survival analysis and univariate Cox regression were used to screen for chemokines and receptors with prognostic value, since strong multicollinearity may be present between chemokines and their receptors.
Therefore, the LASSO scoring based on the selected chemokines and receptors was performed first, and subsequently, multi-factor Cox regression coefficients were calculated to establish a risk-scoring model. 20

| Statistics
To compare differences between groups calculated by survival analysis or Cox regression, continuous variables (including age, chemokine and its receptor mRNA expression value, and the LASSO score) had to be converted into two categorical variables. For the expression of chemokine and its receptor mRNA, and for the LASSO TA B L E 2 Compare of the differences between the expression of RNA of chemokines and their receptors and clinicopathological data  score, the 'survminer' package in the R language was used to calculate the best cut-off value higher than the cut-off value for the high expression group and lower than the cut-off value for the low expression group.
Heatmaps were drawn using EXCEL 2016 (Microsoft Corp) and Adobe Illustrator (Adobe Inc). The R software was used to perform the screening of genetic and clinical data screening, as well as statistical calculations. Wilcoxon rank sum test was used to determine differences in clinical pathology and mRNA expression data; P < .01 was considered statistically significant.
Correlation between chemokines and their receptors was established by Spearman rank correlation analysis using the 'cor.
test' function in R software; P < .01 was considered statistically significant. For survival analyses, the Kaplan-Meier method with log-rank test was used and the survival curves were plotted by the R software.

| Relationship between the expression of chemokines and their receptors mRNA and clinicopathological data
The expression of chemokines and their receptors mRNA in SKCM regional LN tissues did not differ significantly among the T, M, N and AJCC staging (P > .01; Table 2).

| The effect of expression of chemokines and their receptors mRNA on the survival of SKCM patients
To compare the survival of SKCM patients with high and low ex- Note: P < .01 was considered statistically significant.
Cox regression was performed, and survival curves were plotted (Table 3;

| The relationship between LASSO scores for the expression of mRNA of chemokines and their receptors and the survival of SKCM patients
Univariate screening of the 32 chemokine/receptor (CCL2, 4-5, 7-8, CCL22-25, CCR1-9, CXCL2-3, 5, 9-13, 16, XCL1-2, and XCR1), for which higher expression was associated with a better prognosis. As there was a significant correlation between these 32 genes (Tables S1-S3, Figure S1), multicollinearity between chemokines/receptors leads to bias in multivariate COX analysis. Therefore, LASSO score was calculated before multivariate COX analysis. A total of four chemokine/ receptor gene pairs were included in the LASSO score: CCL8, CCL2, CXCL10 and CCL16 ( Figure 7A groups into a high LASSO score group and a low LASSO score group. Survival curves were plotted and indicated that the median survival time in the high LASSO score group was 1460, and 5107 days in the low LASSO score group (P < .001 by the log-rank test; Figure 7C).

| Relationship between the chemokine/ receptor and infiltration of tumour by immune cells
The CIBERSORT algorithm was utilized to calculate the infiltration scores of 22 types of immune cells in LN tissue of SKCM patients.
We then calculated the relationship between chemokine/receptor and tumour immune cell fraction (Figure 8; Table S3).

| Multivariate survival analysis
Univariate COX regression analysis showed that the survival of SKCM patients differed in a statistically significant manner with age T, N, CXCL17 and LASSO scores (P < .01). However, the patients with the M1 stage pathology were too small to evaluate the statistical significance of potential differences. Multivariate analysis showed that T stage, N stage, CXCL17 and LASSO scores had independent prognostic value (P < .01) ( Table 4).

| D ISCUSS I ON
Chemokines, the key mediators of the immune response, are essential for the recruitment of many different types of cells to TME. 9 To identify their functions in SCKM, we first was assessed between the survival of the patients and the expression of chemokines and their receptors genes, using the data for SCKM regional LN tissue available in the public TCGA database. The survival prognosis of the patients with high expression of 32 chemokines and receptors (CCL2, 4-5, 7-8, CCL22-25; CCR1-9; CXCL2-3, 5, 9-13, 16; XCL1-2, XCR1) was found to be better than in the low expression group.
Subsequently, a multivariate COX analysis was performed since the strong correlation among these 32 chemokines/receptors might have compromised the validity of the multivariate COX regression analysis due to multiple linearity. Additionally, LASSO regression based on these 32 chemokines was computed, and analysis was performed using a comprehensive LASSO score. This approach has shown that the LASSO score was an independent prognostic factor. Also, this analysis explained the basis for the absence of a significant difference in chemokine/receptor classification in different pathological stages, suggesting that these 32 chemokine/ receptor genes have independent survival prognostic significance.
Among the chemokines and receptors analysed, we found CCR4, Tumour-associated MCs can release a variety of cytokines, chemokines and growth factors, promoting tumour development by enhancing angiogenesis and remodelling tumour extracellular matrix. [27][28][29] The current study demonstrated also that CXCL9-11, CXCR3, 6, Immunotherapy is currently used to treat cancer, but this type of therapy is effective only for specific populations of patients. [41][42][43][44] Novel, more powerful treatments are urgently needed.
Understanding the role of chemokines in tumour resistance to immunologic defences of the body is essential for the development of new targeted therapeutics in the future.

ACK N OWLED G EM ENTS
This work was supported in part by the National Natural Science Foundation of China (No: 81760344). We analysed raw data that are published in The Cancer Genome Atlas (TCGA) (https ://cance rgeno me.nih.gov).

CO N FLI C T O F I NTE R E S T
There are no conflicts of interest.

AUTH O R CO NTR I B UTI O N S
ZX and PF.Q designed the experiments; LQ and MH.Y analysed the data; XT.F, PF.Q and ZX wrote the paper. All other authors participated in revising the paper and finalizing the paper. All authors read and approved the final manuscript.

DATA AVA I L A B I L I T Y S TAT E M E N T
All data sets generated/analysed for this study are included in the manuscript and the Supplementary Files.