CCL19 has potential to be a potential prognostic biomarker and a modulator of tumor immune microenvironment (TIME) of breast cancer: a comprehensive analysis based on TCGA database

Results

Analysis procedure

Firstly, we downloaded the transcriptome profiling and corresponding clinical materials of 1109 breast tumor samples and 113 normal breast samples from the TCGA database. Secondly, we evaluated the proportion of immune/stromal component in TME using ESTIMATE algorithm and the relationship between Immune/Stromal Score and survival rates or clinic–pathological characteristics of BC patients. Thirdly, a total of 223 DEGs were obtained based on the immune score and stromal score. GO and KEGG enrichment analysis and PPI network were performed to learn the specific biological functions of these DEGs. Univariate COX regression was used to assess the effects of these DEGs on BC patients’ survival. Finally, CCL19 came to our eyes. And we focused on the association between the expression of CCL19 and the survival or clinic–pathological characteristics of BC patients. In addition, GSEA was carried out to find the biological functions of CCL19. CIBERSORT algorithm was applied to evaluate the TIC profile and correlation analysis was used to evaluate the relationship between the expression of CCL19 and the proportion of TICs in TIME. The analysis procedure was shown in Figure 1.

Figure 1. The analysis procedure of this study.

Immune component in TME was associated the survival of BC patients

In order to explore the underlying relationship between the ratio of immune or stromal component in TME and the survival rate of BC patients, we first established the immune/stromal component evaluation system. The higher score reflected the larger amount of the immune or stromal component in TME. And then survival analysis was carried out by Kaplan–Meier plot and log-rank tests. The results showed that Immune Score was significantly associated with the survival rate of BC patients (Figure 2B; p=0.008), which meant that the higher Immune Score predicted the better survival. Yet, neither Stromal nor ESTIMATE Score was significantly related with the survival rate of BC patients (Figure 2A, 2C; p=0.687, 0.339). The above results implied that the immune component in TME might be more suitable for indicating the prognosis of BC patients.

Figure 2. The association of Immune/Stromal/ESTIMATE score and the survival or clinic-pathological characteristics of breast cancer patients. (A–C) Kaplan–Meier survival analysis was applied to evaluate the associations between Immune/Stromal/ESTIMATE Score and the survival of breast cancer patients. Patients were divided into two groups, high score group and low score group, compared with the median. P-value and the number of samples in each group were displayed in the diagram; (D–H) The associations of Stromal Score with age, stage and TNM classification of breast cancer patients; (I–M) The associations of Immune Score with age, stage and TNM classification of breast cancer patients; (N–R) The associations of ESTIMATE Score with age, stage and TNM classification of breast cancer patients.

Both immune and stromal component were significantly associated with the specific clinic-pathological features of BC patients

To further determine the associations between scores and clinic-pathological features of BC patients, we analyzed the corresponding clinical materials (Supplementary Table 1) of BC patients, including the age, stages and TNM classification. First, we found that Stromal, Immune and ESTIMATE Score were significantly associated with the age of BC patients, which might indicate that the component of TME changed with age (Figure 2D, 2I, 2N; p=0.014, 0.03, 0.0068). For other clinic-pathological features, we found that there was significant difference in the Stromal Score in stage I compared with stage II (Figure 2E, p=0.017). There was also significant difference in the Stromal Score in stage T1 compared with stage T2 or T3, in stage N0 compared with stage N2 (Figure 2F, 2G, p=0.00023, 0.022, 0.015). However, for Immune Score, we were unable to find any difference in stages or TNM classifications (Figure 2J–2M). In addition, for ESTIMATE Score, there was also significant difference between T1 and T2 or T4 (Figure 2P, p=0.016, 0.029). Nonetheless, none of Immune, Stromal or ESTIMATE Score was related with the distant metastasis of BC (Figure 2H, 2M, 2F; p>0.05). From above, we concluded that the proportion of immune and stromal component might be associated with the early progression of BC, instead of the distant metastasis.

DEGs were significantly related with immune-related activities

In order to obtain the DEGs profile in TME, we performed the difference analysis between the high Stromal/Immune Score group and the low Stromal/Immune Score group. Heat map was plotted to show the gene profile in TME (Figure 3A, 3B). In detail, there were a total of 773 DEGs obtained from the Stromal Score group (high Stromal Score vs. low Stromal Score), among which 634 DEGs were up-regulated and 139 DEGs were down-regulated (Figure 3A and Supplementary Table 2). Additionally, there were a total of 832 DEGs obtained from the Immune Score group (high Immune Score vs. low Immune Score), among which 729 DEGs were up-regulated and 103 DEGs were down-regulated (Figure 3B and Supplementary Table 3). Next, we conducted the intersection analysis to acquire the commonly up-regulated or down-regulated DEGs in Immune and Stromal Score groups. The Venn diagram showed that 193 DEGs were up-regulated and 30 DEGs were down-regulated in both Immune Score group and Stromal Score group (Figure 3C). Thus, these 223 DEGs (Supplementary Table 4) might take a great part in modulating the status of TME. As a result, GO and KEGG enrichment analyses were further performed to assess the detailed functions of these 223 DEGs. Go enrichment analysis indicated that 223 DEGs greatly participated in lymphocyte activation, T cell activation and T cell differentiation (Figure 3D). Similarly, KEGG enrichment analysis revealed that 223 DEGs were significantly associated with specific immune-related activities, such as cell adhesion molecules, cytokine-cytokine receptor interaction and chemokine signaling pathway (Figure 3E–3G). From above, these 223 DEGs seemed to be closely related with immune-related activities and might be a primary feature of TME in BC.

Figure 3. Heatmaps, venn plots, GO and KEGG enrichment analyses. (A, B) Heatmaps of DEGs obtained by comparison between high Stromal/Immune Score samples with low Stromal/Immune Score samples. p<0.05 and log2^FC >1 were set up to search the DEGs; (C) Venn plots displayed the commonly up-regulated or down-regulated DEGs shared by Immune Score and Stromal Score; (D) GO enrichment analysis of 223 DEGs, p<0.05 was considered to be statistically significant; (E–G) KEGG enrichment analysis of 223 DEGs, p<0.05 was considered to be statistically significant; BP: biological process; CC: cell component; MF: molecular function.

PPI network and univariate COX regression analysis of DEGs

To learn more about the potential interaction mechanisms of these 223 DEGs, PPI network was first constructed with the help of STRING database and Cytoscape software. The specific interaction network of DEGs was showed in Figure 4A. The top 10 DEGs, which shared the leading nodes, were ranked in Figure 4B. We next conducted the univariate COX regression analysis to acquire the DEGs, which were significantly associated with BC patients’ survival. As a result, 32 DEGs were found to be closely related with the hazard ratio (HR) of BC patients’ survival (Figure 4C, p<0.05; Supplementary Table 5). Finally, the intersection analysis was used to find out the DEGs, which shared the leading nodes in PPI and were significantly associated with patients’ survival (Figure 4D). Luckily, C-C motif chemokine 19 (CCL19) emerged.

Figure 4. PPI network construction and univariate COX regression analysis. (A) PPI network was constructed with interaction confidence value >0.95; (B) The top 10 DEGs which shared the leading nodes in PPI network; (C) Univariate COX regression analysis of 223 DEGs. p<0.05 was considered to be statistically significant; (D) Venn plot displayed the DEGs which shared the top 10 leading nodes in PPI network and were significant related with patients’ survival. p<0.05 was considered to be statistically significant.

CCL19 was significantly associated with the survival and specific clinic-pathological features of BC patients

CCL19, a ligand of the chemokine receptor C–C chemokine receptor type 7 (CCR7), is often expressed in the T-cell zones, including lymph nodes and thymus [12]. Recently, researches revealed that the overexpression of CCL19 in serum was associated with the progression of melanoma [13, 14]. In addition, CCL19 functioned as an immune-modulator for colon cancer therapy by closely communicating with DCs, T cells and B cells, thus regulating the adaptive immune responses [15]. However, there are few researches focusing on the role of CCL19 in BC. In the present study, we collected the tissues of carcinoma in situ and infiltrating breast carcinoma, and examined the expression of CCL19 through IHC. The result showed that CCL19 was highly expressed in tumor than in mesenchyme (Supplementary Figure 1A, 1B). The survival analysis suggested that the expression of CCL19 was positively related with the survival of BC patients (Figure 5A, p=0.009). As for the clinic-pathological features, the expression of CCL19 was significantly associated with ages, stages, T classifications and N classifications of BC patients (Figure 5B–5E, p<0.05). However, it was not connected with M classification (Figure 5F, p>0.05), which was in accordance with the difference analysis between Score and clinic-pathological features (Figure 2H, 2M, 2F; p>0.05). From above, we might conclude that CCL19 was likely to be a prognostic factor and participate in the early progression of BC.

Figure 5. The survival analysis, differential analysis and GSEA of CCL19. (A) The survival analysis between the expression of CCL19 and the survival of breast cancer patients; patients were divided into two groups, high CCL19 expression group and low CCL19 expression group, compared with the median; (B–F) The associations between the expression of CCL19 with age, stage, and TNM classification of breast cancer patients; p value was displayed in the diagram; (G–J) GSEA for genes in high CCL19 expression group in different gene sets, p<0.05 and FDR<0.05 were considered to be statistically significant.

CCL19 greatly participated in immune-related activities

Given that CCL19 was significantly associated with the survival and specific clinic-pathological features of BC patients, we further carried out GSEA to explore the underlying mechanisms of CCL19. First, BC samples were divided into two groups according to the expression of CCL19, including CCL19 up-regulated group and CCL19 down-regulated group, compared with the median. For C2 collection, the KEGG gene sets database, genes in CCL19 up-regulated group were obviously enriched in immune-related activities, including B cell receptor signaling pathway, cell adhesion molecules cams, chemokine signaling pathway and so on (Figure 5G). For hallmark gene sets, genes in CCL19 up-regulated group were primarily enriched in allograft rejection, complement, IL2 stat5 signaling and so on (Figure 5H). For C7 collection defined by MSigDB, the immunologic gene sets, a variety of immune functional gene sets were enriched in the high CCL19 expression group (Figure 5I). At last, for C5 collection, the gene ontology sets, the genes in CCL19 up-regulated group were also enriched in immune-related activities, such as adaptive immune response, cytokine binding, cytokine receptor activity and so on (Figure 5J). Thus, we concluded that CCL19 greatly participated in the immune-related activities in BC patients and might be a modulator of TME.

CCL19 was associated with TICs in TIME

Since we had found that CCL19 greatly participated in immune-related activities and might play an important role in the regulation of TME, we speculated that there were underlying connections between CCL19 and TIME. CIBERSORT algorithm was first applied to evaluate the proportion of TICs in BC samples (Figure 6A). The associations between TICs were displayed in Figure 6B. The difference analysis was performed to verify the associations between the expression of CCL19 and 22 TICs. The violin plot suggested that 15 TICs were significantly associated with the expression of CCL19 in BC samples (Figure 6C and Supplementary Table 6). Next, the correlation test showed that 10 TICs were positively related with the expression of CCL19 (Figure 7A–7J and Supplementary Table 6), and 6 TICs were negatively related with the expression of CCL19 (Figure 7K–7P and Supplementary Table 6). Finally, the intersection analysis between the difference test and correlation test revealed that 15 TICs were significantly associated with the expression level of CCL19 (Figure 6D and Supplementary Table 6). As a consequence, CCL19 was significantly associated with multiple TICs in TIME and might play an important role in modulating the TIME of BC through communicating with various TICs.

Figure 6. TIC profile and differential analysis. (A) Barplot showed the proportion of 22 TICs in breast cancer samples; (B) The diagram showed the associations between 22 TICs; each spot indicated the p value of the association between two TICs; (C) Violin plot showed the correlation between 22 TICs and the expression of CCL19; p<0.05 was considered to be statistically significant; (D) Venn plot indicated there were 15 TICs shared by the difference test and correlation test showed in violin plots (C) and scatter plots (Figure 7), respectively.

Figure 7. Scatter plots indicated the correlation between 16 TICs and the expression of CCL19, p<0.05 was considered to be statistically significant. (A) The correlation between Tregs and the expression of CCL19; (B) The correlation between T cells follicular helper and the expression of CCL19; (C) The correlation between T cells gamma delta and the expression of CCL19; (D) The correlation between Macrophages M1 and the expression of CCL19; (E) The correlation between Dendritic cells resting and the expression of CCL19; (F) The correlation between T cells CD4 memory activated and the expression of CCL19; (G) The correlation between T cells CD8 and the expression of CCL19; (H) The correlation between T cells CD4 memory resting and the expression of CCL19; (I) The correlation between B cells memory and the expression of CCL19; (J) The correlation between B cells naive and the expression of CCL19; (K) The correlation between Neutrophils and the expression of CCL19; (L) The correlation between Mast cells activated and the expression of CCL19; (M) The correlation between Macrophages M2 and the expression of CCL19; (N) The correlation between Dendritic cells activated and the expression of CCL19; (O) The correlation between NK cells resting and the expression of CCL19; (P) The correlation between Macrophages M0 and the expression of CCL19.

Author Contributions

(I) Conception and design: Jinyan Wang; Fen Wang; Yan Yang; (II) Administrative support: Yajun Ma; Quan’an Zhang; (III) Provision of study materials or patients: Jinyan Wang; Dongmei Qin; Zhaohui Yang; Meili Chen; (IV) Collection and assembly of data: Jinyan Wang; Dongmei Qin; Hui Yang; Wen Jiang; (V) Data analysis and interpretation: Jinyan Wang; Dongmei Qin; (VI) Manuscript writing: Jinyan Wang; Lingling Ye; Li Wan (VII) Final approval of manuscript: All authors.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Funding

This research was funded by the National Nature Science Foundation of China, grant number 82103032, Medical Research Grant of Jiangsu Commission of Health, grant number M2020010, the Medical Science and Technology Development Foundation of Nanjing, grant number YKK21224, the Science Foundation of Jiangsu Health vocational college, grant number JKC201948, the Science and Technology Development Fund of Nanjing Medical University, grant number NMUB2019235, the Research and development fund of Kangda College of Nanjing Medical University, grant number KD2020KYJJZD006 and Youth Innovation Research Fund project of Jiangning Hospital, grant number JNYYZXKY202023.

References

• 1. Siegel RL, Miller KD, Goding Sauer A, Fedewa SA, Butterly LF, Anderson JC, Cercek A, Smith RA, Jemal A. Colorectal cancer statistics, 2020. CA Cancer J Clin. 2020; 70:145–64. https://doi.org/10.3322/caac.21601 [PubMed]
• 2. Lu H, Xie Y, Tran L, Lan J, Yang Y, Murugan NL, Wang R, Wang YJ, Semenza GL. Chemotherapy-induced S100A10 recruits KDM6A to facilitate OCT4-mediated breast cancer stemness. J Clin Invest. 2020; 130:4607–23. https://doi.org/10.1172/JCI138577 [PubMed]
• 3. Wang J, Zhang Q, Wang D, Yang S, Zhou S, Xu H, Zhang H, Zhong S, Feng J. Microenvironment-induced TIMP2 loss by cancer-secreted exosomal miR-4443 promotes liver metastasis of breast cancer. J Cell Physiol. 2020; 235:5722–35. https://doi.org/10.1002/jcp.29507 [PubMed]
• 4. Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010; 140:883–99. https://doi.org/10.1016/j.cell.2010.01.025 [PubMed]
• 5. Bareche Y, Buisseret L, Gruosso T, Girard E, Venet D, Dupont F, Desmedt C, Larsimont D, Park M, Rothé F, Stagg J, Sotiriou C. Unraveling Triple-Negative Breast Cancer Tumor Microenvironment Heterogeneity: Towards an Optimized Treatment Approach. J Natl Cancer Inst. 2020; 112:708–19. https://doi.org/10.1093/jnci/djz208 [PubMed]
• 6. Baba Y, Nomoto D, Okadome K, Ishimoto T, Iwatsuki M, Miyamoto Y, Yoshida N, Baba H. Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma. Cancer Sci. 2020; 111:3132–41. https://doi.org/10.1111/cas.14541 [PubMed]
• 7. Gajewski TF, Schreiber H, Fu YX. Innate and adaptive immune cells in the tumor microenvironment. Nat Immunol. 2013; 14:1014–22. https://doi.org/10.1038/ni.2703 [PubMed]
• 8. Zhang L, Chen J, Cheng T, Yang H, Li H, Pan C. Identification of the key genes and characterizations of Tumor Immune Microenvironment in Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). J Cancer. 2020; 11:4965–79. https://doi.org/10.7150/jca.42531 [PubMed]
• 9. Yu H, Zhu X, Lin H, Pan H, Zhao F, Zhu M, Sun L, Chai W, Yao M, Yan M. A new risk model comprising genes highly correlated with CD133 identifies different tumor-immune microenvironment subtypes impacting prognosis in hepatocellular carcinoma. Aging (Albany NY). 2020; 12:12234–50. https://doi.org/10.18632/aging.103409 [PubMed]
• 10. Zhao B, Wang Y, Wang Y, Chen W, Liu PH, Kong Z, Dai C, Wang Y, Ma W. Systematic identification, development, and validation of prognostic biomarkers involving the tumor-immune microenvironment for glioblastoma. J Cell Physiol. 2021; 236:507–22. https://doi.org/10.1002/jcp.29878 [PubMed]
• 11. Savas P, Salgado R, Denkert C, Sotiriou C, Darcy PK, Smyth MJ, Loi S. Clinical relevance of host immunity in breast cancer: from TILs to the clinic. Nat Rev Clin Oncol. 2016; 13:228–41. https://doi.org/10.1038/nrclinonc.2015.215 [PubMed]
• 12. Yoshida R, Nagira M, Imai T, Baba M, Takagi S, Tabira Y, Akagi J, Nomiyama H, Yoshie O. EBI1-ligand chemokine (ELC) attracts a broad spectrum of lymphocytes: activated T cells strongly up-regulate CCR7 and efficiently migrate toward ELC. Int Immunol. 1998; 10:901–10. https://doi.org/10.1093/intimm/10.7.901 [PubMed]
• 13. Cristiani CM, Turdo A, Ventura V, Apuzzo T, Capone M, Madonna G, Mallardo D, Garofalo C, Giovannone ED, Grimaldi AM, Tallerico R, Marcenaro E, Pesce S, et al. Accumulation of Circulating CCR7⁺ Natural Killer Cells Marks Melanoma Evolution and Reveals a CCL19-Dependent Metastatic Pathway. Cancer Immunol Res. 2019; 7:841–52. https://doi.org/10.1158/2326-6066.CIR-18-0651 [PubMed]
• 14. Fujimura T, Tanita K, Sato Y, Lyu C, Kambayashi Y, Fujisawa Y, Uchi H, Yamamoto Y, Otsuka A, Yoshino K, Matsushita S, Funakoshi T, Fukushima S, et al. Immune checkpoint inhibitor-induced vitiligo in advanced melanoma could be related to increased levels of CCL19. Br J Dermatol. 2020; 182:1297–300. https://doi.org/10.1111/bjd.18721 [PubMed]
• 15. Liu X, Wang B, Li Y, Hu Y, Li X, Yu T, Ju Y, Sun T, Gao X, Wei Y. Powerful Anticolon Tumor Effect of Targeted Gene Immunotherapy Using Folate-Modified Nanoparticle Delivery of CCL19 To Activate the Immune System. ACS Cent Sci. 2019; 5:277–89. https://doi.org/10.1021/acscentsci.8b00688 [PubMed]
• 16. Radstake TR, van der Voort R, ten Brummelhuis M, de Waal Malefijt M, Looman M, Figdor CG, van den Berg WB, Barrera P, Adema GJ. Increased expression of CCL18, CCL19, and CCL17 by dendritic cells from patients with rheumatoid arthritis, and regulation by Fc gamma receptors. Ann Rheum Dis. 2005; 64:359–67. https://doi.org/10.1136/ard.2003.017566 [PubMed]
• 17. Viola A, Contento RL, Molon B. T cells and their partners: The chemokine dating agency. Trends Immunol. 2006; 27:421–7. https://doi.org/10.1016/j.it.2006.07.004 [PubMed]
• 18. Bromley SK, Mempel TR, Luster AD. Orchestrating the orchestrators: chemokines in control of T cell traffic. Nat Immunol. 2008; 9:970–80. https://doi.org/10.1038/ni.f.213 [PubMed]
• 19. Buonamici S, Trimarchi T, Ruocco MG, Reavie L, Cathelin S, Mar BG, Klinakis A, Lukyanov Y, Tseng JC, Sen F, Gehrie E, Li M, Newcomb E, et al. CCR7 signalling as an essential regulator of CNS infiltration in T-cell leukaemia. Nature. 2009; 459:1000–4. https://doi.org/10.1038/nature08020 [PubMed]
• 20. Comerford I, Harata-Lee Y, Bunting MD, Gregor C, Kara EE, McColl SR. A myriad of functions and complex regulation of the CCR7/CCL19/CCL21 chemokine axis in the adaptive immune system. Cytokine Growth Factor Rev. 2013; 24:269–83. https://doi.org/10.1016/j.cytogfr.2013.03.001 [PubMed]
• 21. Cheng HW, Onder L, Cupovic J, Boesch M, Novkovic M, Pikor N, Tarantino I, Rodriguez R, Schneider T, Jochum W, Brutsche M, Ludewig B. CCL19-producing fibroblastic stromal cells restrain lung carcinoma growth by promoting local antitumor T-cell responses. J Allergy Clin Immunol. 2018; 142:1257–71.e4. https://doi.org/10.1016/j.jaci.2017.12.998 [PubMed]
• 22. Itakura M, Terashima Y, Shingyoji M, Yokoi S, Ohira M, Kageyama H, Matui Y, Yoshida Y, Ashinuma H, Moriya Y, Tamura H, Harigaya K, Matushima K, et al. High CC chemokine receptor 7 expression improves postoperative prognosis of lung adenocarcinoma patients. Br J Cancer. 2013; 109:1100–8. https://doi.org/10.1038/bjc.2013.440 [PubMed]
• 23. Adachi K, Kano Y, Nagai T, Okuyama N, Sakoda Y, Tamada K. IL-7 and CCL19 expression in CAR-T cells improves immune cell infiltration and CAR-T cell survival in the tumor. Nat Biotechnol. 2018; 36:346–51. https://doi.org/10.1038/nbt.4086 [PubMed]
• 24. Hamanishi J, Mandai M, Matsumura N, Baba T, Yamaguchi K, Fujii S, Konishi I. Activated local immunity by CC chemokine ligand 19-transduced embryonic endothelial progenitor cells suppresses metastasis of murine ovarian cancer. Stem Cells. 2010; 28:164–73. https://doi.org/10.1002/stem.256 [PubMed]
• 25. Iida Y, Yoshikawa R, Murata A, Kotani H, Kazuki Y, Oshimura M, Matsuzaki Y, Harada M. Local injection of CCL19-expressing mesenchymal stem cells augments the therapeutic efficacy of anti-PD-L1 antibody by promoting infiltration of immune cells. J Immunother Cancer. 2020; 8:e000582. https://doi.org/10.1136/jitc-2020-000582 [PubMed]
• 26. Worbs T, Hammerschmidt SI, Förster R. Dendritic cell migration in health and disease. Nat Rev Immunol. 2017; 17:30–48. https://doi.org/10.1038/nri.2016.116 [PubMed]
• 27. Förster R, Davalos-Misslitz AC, Rot A. CCR7 and its ligands: balancing immunity and tolerance. Nat Rev Immunol. 2008; 8:362–71. https://doi.org/10.1038/nri2297 [PubMed]
• 28. Hwang H, Shin C, Park J, Kang E, Choi B, Han JA, Do Y, Ryu S, Cho YK. Human breast cancer-derived soluble factors facilitate CCL19-induced chemotaxis of human dendritic cells. Sci Rep. 2016; 6:30207. https://doi.org/10.1038/srep30207 [PubMed]
• 29. Zhou R, Sun J, He C, Huang C, Yu H. CCL19 suppresses gastric cancer cell proliferation, migration, and invasion through the CCL19/CCR7/AIM2 pathway. Hum Cell. 2020; 33:1120–32. https://doi.org/10.1007/s13577-020-00375-1 [PubMed]
• 30. Xu Z, Zhu C, Chen C, Zong Y, Feng H, Liu D, Feng W, Zhao J, Lu A. CCL19 suppresses angiogenesis through promoting miR-206 and inhibiting Met/ERK/Elk-1/HIF-1α/VEGF-A pathway in colorectal cancer. Cell Death Dis. 2018; 9:974. https://doi.org/10.1038/s41419-018-1010-2 [PubMed]
• 31. Baran K, Kiszałkiewicz J, Migdalska-Sęk M, Jabłoński S, Kordiak J, Antczak A, Góralska K, Brzeziańska-Lasota E. An assessment of the relationship between the expression of CCR7/CCL19 axis and selected regulatory miRNAs in non-small cell lung cancer. Mol Biol Rep. 2019; 46:5389–96. https://doi.org/10.1007/s11033-019-04993-3 [PubMed]
• 32. Kim HJ, Park J, Lee SK, Kim KR, Park KK, Chung WY. Loss of RUNX3 expression promotes cancer-associated bone destruction by regulating CCL5, CCL19 and CXCL11 in non-small cell lung cancer. J Pathol. 2015; 237:520–31. https://doi.org/10.1002/path.4597 [PubMed]
• 33. O’Connor T, Zhou X, Kosla J, Adili A, Garcia Beccaria M, Kotsiliti E, Pfister D, Johlke AL, Sinha A, Sankowski R, Schick M, Lewis R, Dokalis N, et al. Age-Related Gliosis Promotes Central Nervous System Lymphoma through CCL19-Mediated Tumor Cell Retention. Cancer Cell. 2019; 36:250–67.e9. https://doi.org/10.1016/j.ccell.2019.08.001 [PubMed]
• 34. Qian J, Xu X, Ding J, Yin R, Sun Y, Xue C, Wang J, Ding C, Yu S, Liu X, Hu S, Wang C, Cong Y, Ding Z. Newcastle disease virus-like particles induce DC maturation through TLR4/NF-κB pathway and facilitate DC migration by CCR7-CCL19/CCL21 axis. Vet Microbiol. 2017; 203:158–66. https://doi.org/10.1016/j.vetmic.2017.03.002 [PubMed]
• 35. Carlsten M, Levy E, Karambelkar A, Li L, Reger R, Berg M, Peshwa MV, Childs RW. Efficient mRNA-Based Genetic Engineering of Human NK Cells with High-Affinity CD16 and CCR7 Augments Rituximab-Induced ADCC against Lymphoma and Targets NK Cell Migration toward the Lymph Node-Associated Chemokine CCL19. Front Immunol. 2016; 7:105. https://doi.org/10.3389/fimmu.2016.00105 [PubMed]