Skip to main content
Download PDF

CC chemokine receptors are prognostic indicators of gastric cancer and are associated with immune infiltration

BMC Medical Genomics volume 17, Article number: 1 (2024) Cite this article

Abstract

Background

CC chemokine receptors are responsible for regulating the tumor microenvironment (TME) and participating in carcinogenesis and tumor advancement. However, no functional study has investigated CC chemokine receptors in gastric cancer (GC) prognosis, risk, immunotherapy, or other treatments.

Methods

We conducted a bioinformatics analysis on GC data using online databases, including the Human Protein Atlas (HPA), Kaplan-Meier (KM) plotter, GeneMANIA, MethSurv, the University of ALabama at Birmingham CANcer (UALCAN) Data Analysis Portal, Gene Set Cancer Analysis (GSCA), cBioportal, and Tumor IMmune Estimation Resource (TIMER).

Results

We noted that CC chemokine receptor expression correlated with survival in GC. CC chemokine receptor expression was also strongly linked to different tumor-infiltrating immune cells. Additionally, CC chemokine receptors were found to be broadly drug-resistant in GC.

Conclusion

Our study identifed CC chemokine receptor expression helped in predicting the prognosis of patients diagnosed with GC. The expression level of the CC chemokine receptors was also positively related to multiple tumor-infiltrating lymphocytes (TILs). These findings provide evidence to monitor patients with GC using CC chemokine receptors, which can be used as an effective biomarker for predicting the disease prognosis and be regarded as a therapeutic target for modulating the tumor immune microenvironment.

Peer Review reports

Introduction

Among cancers, GC is a common malignancy in China. GC ranks sixth and third in terms of cancer incidence and death, respectively [1, 2]. Currently, surgical resection is considered the main form of treatment for early-stage GC patients; however, most patients are inoperable at the time of diagnosis [3,4,5]. The GC patients show a low 5-year overall survival (OS) rate since a majority of these patients get diagnosed in their advanced stages of GC [6,7,8]. These issues have highlighted the need for identifying novel biomarkers for early diagnosis, prediction of metastatic progression, and prognosis of GC patients.

Furthermore, the infiltration of various immune cells in the TME is an important factor for determining the malignant tumor genesis, development, metastasis, and therapy resistance of cancer [9,10,11]. In existing studies receptor tyrosine kinases (RKTs) (e.g., EGFR, FGFR2, HER2, and MET), PD-L1, claudin 18.2 are frequently overexpressed [12].

CC chemokine receptors, or the beta chemokine receptors, belong to the G protein-linked receptor superfamily called the seven-transmembrane domain receptors [13]. These receptors are a type of membrane protein that can specifically bind to the CC chemokine family of the cytokines. According to the International Union of Immunological Societies (IUIS), which is a World Health Organization (WHO) Subcommittee on Chemokine Nomenclature, there are 10 CC chemokine receptors, namely CCR1-CCR10 [14, 15]. These CC chemokine receptors are involved in many biological activities, like recruiting immune cells, regulating leukocyte chemotaxis, tumorigenesis, inflammation, parenchymal remodeling, and cancer progression [16,17,18,19,20]. Several types of cells like immune cells, tumor cells, peripheral blood cells, and stromal cells, express CC chemokine receptors. These receptors assist in determining the composition of the tumor stroma and are associated with tumor growth, metastasis, and angiogenesis. Thus, they directly or indirectly influence the progression of cancer, therapeutic effects, and the resulting clinical outcome [21,22,23,24]. Predictive biomarkers are the mainstay of precision medicine.

Although several researchers have studied different types of CC chemokine receptors in the past and also described their expression and functions, it is unclear how the CC chemokine receptors work as targets and indicators in GC. In this study, we used publicly-accessible bioinformatics datasets to examine the survival and function of CC chemokine receptors in GC to determine its prognostic mechanism.

Materials and methods

Kaplan-Meier (KM) plotter

We used the KM plotter (http://www.kmplot.com) to examine the prognostic role of CC chemokine receptors in GC [25]. The researchers also calculated the Log-rank P-value and the hazard ratio (HR) at the 95% confidence interval (CI).

Human protein atlas (HPA)

We used the immunohistochemical data obtained from the HPA repository (https://www.proteinatlas.org/) for determining the expression levels of the CC chemokine receptors in gastric tissues [26]. To analyze the relevant spatial protein expression patterns, immunohistochemically labeled tissue sections as well as the Single Cell Type in HPA data, which was based on the single-cell RNA sequence (scRNAseq) data derived from the Peripheral Blood Mononuclear Cells (PBMCs) and tissue samples of 25 patients, were used.

Tumor IMmune Estimation Resource (TIMER)

We used the TIMER algorithm dataset (https://cistrome.shinyapps.io/timer/) to investigate the correlation between the CC chemokine receptor expression level in normal and GC patients [27]. The TIMER dataset includes information regarding 32 different cancer types and contains 10,897 samples derived from The Cancer Genome Atlas (TCGA). It also includes data related to the immune cells like CD8 + T cells, CD4 + T cells, macrophages, neutrophils, B cells, and dendritic cells.

GeneMANIA

The protein-gene interactions, pathways, and functions of the CC chemokine receptors, along with all their related interaction were predicted with the help of GeneMANIA (http://www.genemania.org) [28].

cBioPortal

The cBioPortal (http://cbioportal.org) is a simple online application that allows users to find multifaceted cancer genomic datasets and retrieve information from over 5000 tumor specimens from more than 20 cancer-related studies [29]. The cBioPortal dataset was utilized to investigate CC chemokine receptors’ mutation in GC. The frequency and the types of genomic alteration of CC chemokine receptors were investigated. The genomic changes in CC chemokine receptors included deep deletion, mRNA up-modulation, copy number amplification, and missense mutation with unclear significance, among others.

The UALCAN Data Analysis Portal

UALCAN (http://ualcan.path.uab.edu/), which is an interactive web tool that helps in analyzing cancer data, was used to examine the correlation between the CC chemokine receptor expression level and DNA methylation status among GC patients [30].

Gene Set Cancer Analysis

GSCA (http://bioinfo.life.hust.edu.cn/GSCA/#/) is an optimized version of GSCALite, which is a database that helps in searching, analyzing, and exploring the gene set cancer analysis associated with immune infiltration, mRNA expression, mutation, and drug resistance [31]. The GSCA combines the data corresponding to 10,000 multi-dimensional genomic data points for the 33 different cancer types described in the TCGA and more than 750 small molecule drug-related data derived from the Genomics of Drug Sensitivity in Cancer (GDSC) portal and the Cancer Therapeutics Response Portal (CTRP).

MethSurv

We investigated the prognostic significance of the single CpG methylation status of CC chemokine receptors in the GC patients using MethSurv (https://biit.cs.ut.ee/methsurv/). It is a web platform that helps in survival analysis based on the CpG methylation pattern [32].

Results

mRNA expression levels of the CC chemokine receptors in GC patients

We studied the differential expression of the CC chemokine receptors in GC and normal tissue samples using the UALCAN repository. The results indicated that CCR10 and CCR6 expression levels were significantly lowered in the GC tissues, whereas the CCR1, CCR4, CCR5, and CCR8 gene expression levels were significantly elevated in the GC tissues than in normal gastric tissues (p < 0.05). (Fig. 1).

Fig. 1
figure 1

Differential expression of the CC chemokine receptors in differing disease states using UALCAN

Full size image

The expression levels of the CCR2, CCR3, CCR4, CCR6, CCR7, CCR8, CCR9, and CCR10 proteins in STAD were also determined using HPA (Fig. 2). CCR1 and CCR5 were not found in HPA. We found that CCR4 and CCR8 stained more prominently in tumor tissues. CCR6 and CCR10 immunostaining was less pronounced in tumor tissues.

Fig. 2
figure 2

Immunohistochemistry images presenting the expression of the CC chemokine receptors in the GC tissues and normal tissues using HPA

Full size image

Prognostic potential of CC chemokine receptors in GC

The OS curves for the expression of the CC chemokine receptor are shown in Fig. 3 for the KM plotter dataset, which is on the basis of the Affymetrix microarrays. The high expression levels of CCR4 (p = 0.00065), CCR5 (p = 0.019), CCR7 (p = 0.016), CCR8 (p = 0.00041), CCR9 (p = 0.00014), and CCR10 (p = 0.00017) indicate shorter OS, while the high expression levels of CCR3 (p = 0.0069) and CCR6 (p = 0.0032) indicate a longer OS in GC patients.

Fig. 3
figure 3

OS curves for the CC chemokine receptor expression in GC using the KM plotter

Full size image

The first-progression survival (FPS) curves for the CC chemokine receptor expression in GC are presented in Fig. 4. The high expression levels of CCR8 (p = 0.00012), CCR9 (p = 0.0014), and CCR10 (p = 0.023) indicate shorter FPS, while high expression levels of CCR3 (p = 0.0012) and CCR6 (p = 0.0051) indicate longer FPS in GC cases.

Fig. 4
figure 4

FPS curves for CC chemokine receptor expression in GC using the KM plotter

Full size image

Post-progression survival (PPS) CC chemokine receptor expression in GC are presented in Fig. 5. The high expression levels of CCR1 (p = 0.00054), CCR2 (p = 0.008), CCR5 (p = 4.4e-06), CCR7 (6.4e-05), CCR8 (1.7e-05), CCR9 (p = 0.0022), and CCR10 (p = 5.2e-05) indicate shorter PPS, whereas the high expression levels of CCR3 (p = 0.00053) and CCR6 (p = 0.035) indicate longer PPS in GC cases.

Fig. 5
figure 5

PPS curves for CC chemokine receptor expression in GC using the KM plotter

Full size image

Correlation analysis between the DNA methylation status and CC chemokine receptor expression levels

We determined the predictive value of the DNA methylation status of the CC chemokine receptors in GC using MethSurv. Figure 6 presents the heat map of DNA methylation status in the CC chemokine receptors. The results showed that the cg10335493 gene coding for CCR1, cg11313065 gene coding for CCR2, cg24693555 gene coding for CCR3, cg21366834 gene coding for CCR4, cg15239694 gene coding for CCR5, cg19668990 gene coding for CCR6, cg11729107 gene coding for CCR7, cg11492964 gene coding for CCR8, cg14558191 gene coding for CCR9, and cg06864083 gene coding for CCR10 displayed the highest levels of DNA methylation. We found that 4 CpGs of CCR1, 1 CpG of CCR2, 1 CpG of CCR3, 4 CpGs of CCR4, 2 CpGs of CCR5, 2 CpGs of CCR6, 1 CpG of CCR7, 1 CpG of CCR8, 3 CpGs of CCR9 and 1 CpG of CCR10 were significantly associated with prognosis in GC patients (Table 1).

Fig. 6
figure 6

Heat maps of the DNA methylation expression levels of the CC chemokine receptor genes using MethSurv

Full size image
Table 1 The Prognostic Value of Single CpG of CC Chemokine Receptors in GC by MethSurv (P < 0.05)
Full size table

Correlation analysis between CC chemokine receptor expression level and the infiltrating immune cells

We investigated the relationships between CC chemokine receptor expressions and 6 types of infiltrating immune cells (neutrophils, macrophages, B cells, dendritic cells, CD4 T cells, and CD8 + T cells) with the aid of the TIMER database. The results are presented in Fig. 7. The results showed that the CC cytokine receptor expression level was positively correlated with the infiltration of 6 types of immune cells, except for CCR2 and CCR10 in B cells.

Fig. 7
figure 7

Relationship between the CC chemokine receptor expression levels and the immune cell infiltration level using TIMER

Full size image

For determining the expression of the CC chemokine receptors in the tumor-infiltrating immune cells in GC, the researchers used the Single Cell Type in HPA; the results are presented in Fig. 8. Except for CCR3, the expression level of CCR1 was high in macrophages; CCR2 expression level was high in macrophages and plasma cells; CCR4, CCR5, and CCR8 expression levels were high in T cells; CCR6 and CCR7 expression levels were high in T cells and B cells; CCR9 expression level was high in macrophages and T cells, and CCR10 expression level was high in plasma cells.

Fig. 8
figure 8

Relationship between the CC chemokine receptor expression levels and the tumor-infiltrating immune cells in GC using HPA

Full size image

Genetic alterations and Gene Interaction analyses of the CC chemokine receptors in GC patients

We also conducted a thorough examination of the molecular properties of the CC chemokine receptors. Firstly, cBioPortal was used to examine the genetic alterations of the CC chemokine receptors. The gene alterations of CCR1, CCR2, CCR3, CCR4, CCR5, CCR6, CCR7, CCR8, CCR9, and CCR10 were seen to be 2.2%, 1.8%, 4%, 0.7%, 1.7%, 3%, 7%, 1.4%, 2.2%, and 1.5% of the LUAD samples, respectively (Fig. 9A). Additionally, GeneMANIA results showed that the functions of the above CC chemokine receptors were correlated with the cytokine receptor activity, G-protein coupled receptor activity, G-protein coupled chemoattractant receptor activity, and the chemokine-mediated signaling pathways (Fig. 9B).

Fig. 9
figure 9

A CC chemokine receptor expression in GC from the cBioportal database. B The interactive analysis of CC chemokine receptors in the GC tissues using GeneMANIA databases

Full size image

Drug sensitivity of the CC chemokine receptors in GC patients

Using GSCA, CC chemokine receptors were observed to exhibit resistance to multiple drugs (Fig. 10A-B). Using CTRP drug sensitivity testing, the researchers found that CCR1/2/3/4/5/6/7/9/10 were resistant to multiple drugs (Fig. 10A), such as sotrastaurin, belinostat, and LRRK2-IN-1. According to GDSC drug sensitivity tests, they found that CCR1/2/3/4/5/6/7/8/9/10 were resistant to various drugs (Fig. 10B), such as 5-Fluorouracil, I-BET-762, and Vorinostat.

Fig. 10
figure 10

The drug sensitivity of CC chemokine receptors in GC. A Correlation between gene expression levels and sensitivity to CTRP drugs (Top 30). B Relationship between gene expression levels and sensitivity to GDSC drugs (Top 30)

Full size image

Discussion

GC can severely affect the health and lives of people across the globe, owing to its high morbidity and mortality rates [33,34,35]. A majority of the patients do not show any early symptoms of GC, while some display nonspecific upper gastrointestinal symptoms that resemble gastric ulcers. The most recent National Comprehensive Cancer Network guidelines place more emphasis on precision therapies, especially prognostic biomarkers, drug targets, and immune-related genes, due to the advancements in biomedical detection technologies and a better comprehension of the GC-related immune microenvironment [36].

There are 10 CC chemokine receptor subtypes, making them the largest subdivision in the chemokine superfamily. All ten are G protein-coupled receptors with a 7-transmembrane region, and they primarily trigger the signal transduction pathway through the Gi proteins [22]. A collection of genes on chromosome 3p21 in humans encode the proteins CCR1, CCR2, CCR3, CCR4, CCR5, CCR8, CCR9, and CCR10. A gene on the long Chromosome 6 arm (6q27) codes for CCR6, and a gene on the Chromosome 17q21.2 codes for CCR7. CCRs mediate different forms of immunological responses and are significantly and differently expressed on many of the leukocyte subsets [37]. They play a role in many inflammatory and autoimmune diseases, including osteoarthritis, inflammatory bowel disease, and multiple sclerosis [21, 38,39,40]. Several studies have highlighted the significant role played by the CC chemokine receptors in mediating the chronic inflammatory response, as well as leukocyte recruitment, angiogenesis, and metastasis of tumors [41, 42]. The CC chemokine receptors can be regarded as a potential pharmacological target [22]. A few CC chemokine receptor antagonists and/or inhibitors have exhibited remarkable anti-tumor efficacy in preclinical studies and clinical trials [41, 43]. However, none of the researchers, to date, have characterized the prognostic values and the biological roles of the CC chemokine receptors in GC patients.

In this study, the researchers studied the CC chemokine receptor expression level in GC patients and discovered that six genes were expressed differentially in gastric tumor tissues in comparison to the normal tissues (the results indicated that CCR1, CCR4, CCR5, and CCR8 genes were upregulated, whereas the CCR6 and CCR10 genes were downregulated). They also examined the prognostic significance of the CC chemokine receptors in GC patients. The findings demonstrated a substantial correlation between low OS rate and the higher expression levels of genes like CCR4, CCR5, CCR7, CCR8, CCR9, and CCR10 and the lower expression levels of CCR3 and CCR6. The low expression levels of CCR3 and CCR6 were significantly related to a worse FPS, whereas the high expression levels of CCR8, CCR9, and CCR10 led to a poor FPS in GC patients. With regard to the PPS, a higher expression level of the CCR1, CCR2, CCR5, CCR7, CCR8, CCR9, and CCR10 genes and a lower expression level of the CCR3 and CCR6 genes were seen to be significantly related to a worse PPS. In summary, it shows that CCR4, CCR5, CCR8, and CCR10 are harmful factors for humans and CCR6 is a protective factor. Previous research has shown that CCR4 and its ligands were associated with increased tumor recurrence and impaired OS in patients with GC [44]. CCR5 expression was associated with lymph node metastasis and a worse prognosis in patients with GC and shown to be an independent indicator of a poor prognosis in GC [45]. CCR6 expression is deregulated in some human malignancies and may be involved in the tumor progression. zhang et al. showed that CCR6 was highly expressed in GC tissues and was a factor for poor prognosis, which is different from what we found in our study, probably due to different selection of specimens, and more samples are needed to clarify [46]. An upregulated expression of CCR8 in GC tissues was associated with tumor grade, nodal metastasis, and OS. Tumor-infiltrated Tregs with higher expression of CCR8 produced more IL10 molecules in vitro [47]. No studies of CCR10 in GC at this time.

And for the prognostic value of the DNA methylation of CC chemokine receptors 4 CpGs of CCR1, 1 CpG of CCR2, 1 CpG of CCR3, 4 CpGs of CCR4, 2 CpGs of CCR5, 2 CpGs of CCR6, 1 CpG of CCR7, 1 CpG of CCR8, 3 CpGs of CCR9 and 1 CpG of CCR10 were significantly associated with prognosis in GC patients. Furthermore, the researchers also studied the molecular properties of the CC chemokine receptors in GC patients. The onset and progression of GC is a multi-step multi-factor process that also includes genetic variations. Hence, in this study, the researchers investigated the genetic alterations that took place in the GC patients compared to the normal samples. Their results revealed that the CCR genes in the GC patients showed a 0.7-4% genetic variation. They used the GeneMANIA software and noted that the networks associated with these CCRs were mainly associated with the shared protein domains, physical interactions, co-expression, predicted, and co-localization.

A few researchers noted that the presence of the TILs in the GC patients was related to better prognosis; additionally, it could be regarded as an indicator of an effective immune response against the tumors [48]. In this study, the researchers determined the relationship between the CCRs and the infiltration of the different kinds of immune cells in GC patients. They noted that the expression levels of the 10 CCRs were positively linked to the infiltration of 6 different immune cells, except for CCR2 and CCR10 in B cells. The researchers used single-cell analysis to further elucidate the role of the expression levels of the CCRs in the tumor-infiltrating immune cells in GC and observed that the T-cells and the macrophages were highly expressed in the CCRs when the CCR10 expression was high in plasma cells.

Finally, The researchers evaluated drug-sensitive CC chemokine receptors in GC and found that no sensitive drugs are currently available. However, insensitive drugs, such as 5-Fluorouracil, can be avoided in clinical practice.

Our results suggest that some CC chemokine receptor variants are deleterious for GC patients. Thus, the researchers can predict the prognosis and evaluate the immune microenvironment by detecting the expression level of the CC chemokine receptors in GC patients. Therefore, the relevance of CC chemokine receptors in immune system function might explain why the presence of high CC chemokine receptors is an unfavorable prognostic factor in patients with GC. CC chemokine receptors are a potentially novel and valuable biomarker in GC.

However, there may be some restrictions on this study. To identify the relationships between various immune cells and the CC chemokine receptors, sequencing information from public libraries was examined. Therefore, additional experimental validation is required. In addition, as a promising prognostic predictor and prospective immunotherapy target, the possible effects and mechanisms of CC chemokine receptors in GC merit further investigation.

Data Availability

The datasets generated and/or analysed during the current study are available in the Human Protein Atlas (HPA) (https://www.proteinatlas.org/), Kaplan–Meier plotter(http://www.kmplot.com), GeneMANIA(http://www.genemania.org), MethSurv(https://biit.cs.ut.ee/methsurv/), UALCAN(http://ualcan.path.uab.edu/), GSCA(http://bioinfo.life.hust.edu.cn/GSCA/#/), cBioportal(http://cbioportal.org), and TIMER(https://cistrome.shinyapps.io/timer/).

Abbreviations

TME:

Tumor microenvironment

GC:

Gastric cancer

HPA:

Human Protein Atlas

KM:

Kaplan-Meier

UALCAN:

University of ALabama at Birmingham CANcer

GSCA:

Gene Set Cancer Analysis

TIMER:

Tumor IMmune Estimation Resource

TILs:

Tumor-infiltrating lymphocytes

OS:

Overall survival

scRNAseq:

single-cell RNA sequence

PBMCs:

Peripheral Blood Mononuclear Cells

TCGA:

The Cancer Genome Atlas

GDSC:

Genomics of Drug Sensitivity in Cancer

CTRP:

Cancer Therapeutics Response Portal

FPS:

First-progression survival

PPS:

Post-progression survival

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;68(1):7–30.

    Article  PubMed  Google Scholar 

  2. Global Burden of Disease Cancer Collaboration, Fitzmaurice C, Akinyemiju TF, Al Lami FH, Alam T, Alizadeh-Navaei R, et al. Global, Regional, and National Cancer incidence, mortality, years of Life Lost, Years lived with disability, and disability-adjusted life-years for 29 Cancer groups, 1990 to 2016: a systematic analysis for the global burden of Disease Study. JAMA Oncol. 2018;4(11):1553–68.

    Article  Google Scholar 

  3. Song Z, Wu Y, Yang J, Yang D, Fang X. Progress in the treatment of advanced gastric cancer. Tumour Biol. 2017;39(7):1010428317714626.

    Article  PubMed  Google Scholar 

  4. Ferro A, Peleteiro B, Malvezzi M, Bosetti C, Bertuccio P, Levi F, et al. Worldwide trends in gastric cancer mortality (1980–2011), with predictions to 2015, and incidence by subtype. Eur J Cancer. 2014;50(7):1330–44.

    Article  PubMed  Google Scholar 

  5. Saka M, Morita S, Fukagawa T, Katai H. Present and future status of gastric cancer surgery. Jpn J Clin Oncol. 2011;41(3):307–13.

    Article  PubMed  Google Scholar 

  6. Dassen AE, Dikken JL, van de Velde CJ, Wouters MW, Bosscha K, Lemmens VE. Changes in treatment patterns and their influence on long-term survival in patients with stages I-III gastric cancer in the Netherlands. Int J Cancer. 2013;133(8):1859–66.

    Article  CAS  PubMed  Google Scholar 

  7. Sugano K. Screening of gastric cancer in Asia. Best Pract Res Clin Gastroenterol. 2015;29(6):895–905.

    Article  PubMed  Google Scholar 

  8. Van Cutsem E, Sagaert X, Topal B, Haustermans K, Prenen H. Gastric cancer. Lancet. 2016;388(10060):2654–64.

    Article  PubMed  Google Scholar 

  9. Pitt JM, Marabelle A, Eggermont A, Soria JC, Kroemer G, Zitvogel L. Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy. Ann Oncol. 2016;27(8):1482–92.

    Article  CAS  PubMed  Google Scholar 

  10. Kalinski P, Talmadge JE. Tumor Immuno-Environment in Cancer Progression and Therapy. Adv Exp Med Biol. 2017;1036:1–18.

    Article  CAS  PubMed  Google Scholar 

  11. Wang M, Zhao J, Zhang L, Wei F, Lian Y, Wu Y, et al. Role of tumor microenvironment in tumorigenesis. J Cancer. 2017;8(5):761–73.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Röcken C. Predictive biomarkers in gastric cancer. J Cancer Res Clin Oncol. 2023;149(1):467–81.

    Article  PubMed  Google Scholar 

  13. Trzaskowski B, Latek D, Yuan S, Ghoshdastider U, Debinski A, Filipek S. Action of molecular switches in GPCRs–theoretical and experimental studies. Curr Med Chem. 2012;19(8):1090–109.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. IUIS/WHO Subcommittee on Chemokine Nomenclature. Chemokine/chemokine receptor nomenclature. Cytokine. 2003;21(1):48–9.

    Article  Google Scholar 

  15. International Union of Immunological Societies/World Health Organization. Subcommittee on chemokine nomenclature. Chemokine/chemokine receptor nomenclature. J Leukoc Biol. 2001;70(3):465–6.

    Article  Google Scholar 

  16. Rodríguez-Fernández JL, Criado-García O. The chemokine receptor CCR7 uses distinct signaling modules with biased functionality to regulate dendritic cells. Front Immunol. 2020;11:528.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Roubeix C, Dominguez E, Raoul W, Guillonneau X, Paques M, Sahel JA, et al. Mo-derived perivascular macrophage recruitment protects against endothelial cell death in retinal vein occlusion. J Neuroinflammation. 2019;16(1):157.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wang T, Zhou Q, Zeng H, Zhang H, Liu Z, Shao J, et al. CCR8 blockade primes anti-tumor immunity through intratumoral regulatory T cells destabilization in muscle-invasive bladder cancer. Cancer Immunol Immunother. 2020;69(9):1855–67.

    Article  PubMed  Google Scholar 

  19. Kadomoto S, Izumi K, Mizokami A. The CCL20-CCR6 Axis in Cancer Progression. Int J Mol Sci. 2020;21(15):5186.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Lokeshwar BL, Kallifatidis G, Hoy JJ. Atypical chemokine receptors in tumor cell growth and metastasis. Adv Cancer Res. 2020;145:1–27.

    Article  PubMed  Google Scholar 

  21. Groblewska M, Litman-Zawadzka A, Mroczko B. The role of selected Chemokines and their receptors in the development of Gliomas. Int J Mol Sci. 2020;21(10):3704.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. White GE, Iqbal AJ, Greaves DR. CC chemokine receptors and chronic inflammation–therapeutic opportunities and pharmacological challenges. Pharmacol Rev. 2013;65(1):47–89.

    Article  CAS  PubMed  Google Scholar 

  23. Nishimura M, Kuboi Y, Muramoto K, Kawano T, Imai T. Chemokines as novel therapeutic targets for inflammatory bowel disease. Ann N Y Acad Sci. 2009;1173:350–6.

    Article  CAS  PubMed  Google Scholar 

  24. Trivedi PJ, Adams DH. Chemokines and chemokine receptors as therapeutic targets in inflammatory bowel disease; Pitfalls and Promise. J Crohns Colitis. 2018;12(suppl2):641–S652.

    Article  Google Scholar 

  25. Györffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat. 2010;123(3):725–31.

    Article  PubMed  Google Scholar 

  26. Asplund A, Edqvist PH, Schwenk JM, Pontén F. Antibodies for profiling the human proteome-the human protein atlas as a resource for cancer research. Proteomics. 2012;12(13):2067–77.

    Article  CAS  PubMed  Google Scholar 

  27. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Vlasblom J, Zuberi K, Rodriguez H, Arnold R, Gagarinova A, Deineko V, et al. Novel function discovery with GeneMANIA: a new integrated resource for gene function prediction in Escherichia coli. Bioinformatics. 2015;31(3):306–10.

    Article  CAS  PubMed  Google Scholar 

  29. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.

    Article  PubMed  Google Scholar 

  30. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK, et al. UALCAN: a portal for facilitating Tumor Subgroup Gene expression and survival analyses. Neoplasia. 2017;19(8):649–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Liu CJ, Hu FF, Xia MX, Han L, Zhang Q, Guo AY. GSCALite: a web server for gene set cancer analysis. Bioinformatics. 2018;34(21):3771–2.

    Article  CAS  PubMed  Google Scholar 

  32. Modhukur V, Iljasenko T, Metsalu T, Lokk K, Laisk-Podar T, Vilo J. MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data. Epigenomics. 2018;10(3):277–88.

    Article  CAS  PubMed  Google Scholar 

  33. Sano T. Gastric cancer: Asia and the world. Gastric Cancer. 2017;20(Suppl 1):1–2.

    Article  PubMed  Google Scholar 

  34. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68(6):394–424.

    Article  PubMed  Google Scholar 

  35. Deng Y, Wang M, Zhou L, Zheng Y, Li N, Tian T, et al. Global burden of larynx cancer, 1990–2017: estimates from the global burden of disease 2017 study. Aging. 2020;12(3):2545–83.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Guidelines Version NCCN. 2.2021. Available from: https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1434. Accessed June 30, 2021.

  37. Murphy PM. Chemokine receptors: structure, function and role in microbial pathogenesis. Cytokine Growth Factor Rev. 1996;7(1):47-64.

  38. Fantuzzi L, Tagliamonte M, Gauzzi MC, Lopalco L. Dual CCR5/CCR2 targeting: opportunities for the cure of complex disorders. Cell Mol Life Sci. 2019;76(24):4869-86.

    Article  CAS  PubMed  Google Scholar 

  39. Raghu H, Lepus CM, Wang Q, Wong HH, Lingampalli N, Oliviero F, et al. CCL2/CCR2, but not CCL5/CCR5, mediates monocyte recruitment, inflammation and cartilage destruction in osteoarthritis. Ann Rheum Dis. 2017;76(5):914–22.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Scheu S, Ali S, Ruland C, Arolt V, Alferink J. The C-C Chemokines CCL17 and CCL22 and their receptor CCR4 in CNS autoimmunity. Int J Mol Sci. 2017;18(11):2306.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Mollica Poeta V, Massara M, Capucetti A, Bonecchi R. Chemokines and chemokine receptors: new targets for Cancer Immunotherapy. Front Immunol. 2019;10:379.

    Article  CAS  PubMed  Google Scholar 

  42. Rizeq B, Malki MI. The role of CCL21/CCR7 Chemokine Axis in breast Cancer progression. Cancers (Basel). 2020;12(4):1036.

    Article  CAS  PubMed  Google Scholar 

  43. Vandercappellen J, Van Damme J, Struyf S. The role of CXC chemokines and their receptors in cancer. Cancer Lett. 2008;267(2):226–44.

    Article  PubMed  Google Scholar 

  44. Lee JH, Cho YS, Lee JY, et al. The chemokine receptor CCR4 is expressed and associated with a poor prognosis in patients with gastric cancer. Ann Surg. 2009;249(6):933–41.

    CAS  PubMed  Google Scholar 

  45. Ryu H, Baek SW, Moon JY, Jo IS, Kim N, Lee HJ. C-C motif chemokine receptors in gastric cancer. Mol Clin Oncol. 2018;8(1):3–8.

    Article  CAS  PubMed  Google Scholar 

  46. Zhang XG, Song BT, Liu FJ, Sun D, Wang KX, Qu H. CCR6 overexpression predicted advanced biological behaviors and poor prognosis in patients with gastric cancer. Clin Transl Oncol. 2016;18(7):700–7.

  47. Zhang Z, Wang G, Shao X, et al. A Novel Prognostic Biomarker CCR8 for gastric Cancer and Anti-CCR8 blockade attenuate the immunosuppressive capacity of Tregs in Vitro. Cancer Biother Radiopharm. 2023;38(6):415–24.

  48. Liu YJ, Li JP, Zhang Y, Nie MJ, Zhang YH, Liu SL, et al. FSTL3 is a prognostic biomarker in gastric Cancer and is correlated with M2 macrophage infiltration. Onco Targets Ther. 2021;14:4099–117.

Download references

Acknowledgements

N/A.

Funding

No funding support.

Author information

Authors and Affiliations

  1. Department of Integrated Therapy, Fudan University Shanghai Cancer Center, Shanghai, 200032, China

    Xinghe Liao

  2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China

    Xinghe Liao

  3. Department of General Surgery, the First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China

    Yong Yang

  4. Department of Radiology, the First people’s Hospital of Taicang City, Taicang Affiliated Hospital of Soochow University, Taicang City, 215400, Jiangsu Province, China

    Lihuan Wang

  5. Department of Gastrointestinal Surgery, the First people’s Hospital of Taicang City, Taicang Affiliated Hospital of Soochow University, Taicang City, 215400, Jiangsu Province, China

    Zhiyuan Kong & Weiping Li

Authors
  1. Xinghe Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yong Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lihuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhiyuan Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Weiping Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XHL, YY and WPL drafted the manuscript, collected the data and reviewed the literature. LHW provided experimental concepts and design and collected the data. ZYK substantively revised the manuscript. All authors confirmed and approved the final manuscript.

Corresponding author

Correspondence to Weiping Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The Human Protein Atlas (HPA), Kaplan–Meier plotter, GeneMANIA, MethSurv, UALCAN, GSCA, cBioportal, and TIMER belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open source data, so there are no ethical issues and other conflicts of interest.

Consent for publication

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access 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://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liao, X., Yang, Y., Wang, L. et al. CC chemokine receptors are prognostic indicators of gastric cancer and are associated with immune infiltration. BMC Med Genomics 17, 1 (2024). https://doi.org/10.1186/s12920-023-01690-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12920-023-01690-w

Keywords

BMC Medical Genomics

ISSN: 1755-8794

Contact us