CXCL9在乳腺癌中的表达及其与肿瘤免疫浸润特征的相关性研究

doi:10.3969/j.issn.1674-8115.2023.07.008

上海交通大学学报（医学版） ›› 2023, Vol. 43 ›› Issue (7): 860-872.doi: 10.3969/j.issn.1674-8115.2023.07.008

• 论著 · 基础研究 • 上一篇

CXCL9在乳腺癌中的表达及其与肿瘤免疫浸润特征的相关性研究

杜少倩(), 陶梦玉, 曹源, 王红霞, 胡孝渠, 范广建(), 臧丽娟()

上海交通大学医学院附属第一人民医院肿瘤中心，上海 201600

收稿日期:2022-12-23 接受日期:2023-06-11 出版日期:2023-07-28 发布日期:2023-07-28
通讯作者: 范广建,臧丽娟 E-mail:dushaoqian@163.com;gjfan@shsmu.edu.cn;lou19941205@163.com
作者简介:杜少倩（1997—），女，硕士生；电子信箱：dushaoqian@163.com。
基金资助:
上海申康医院发展中心临床三年行动计划(SHDC2020CR2065B);浙江省自然科学基金(LY20H160010)

CXCL9 expression in breast cancer and its correlation with the characteristics of tumor immunoinfiltration

DU Shaoqian(), TAO Mengyu, CAO Yuan, WANG Hongxia, HU Xiaoqu, FAN Guangjian(), ZANG Lijuan()

Department of Oncology Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 201600, China

Received:2022-12-23 Accepted:2023-06-11 Online:2023-07-28 Published:2023-07-28
Contact: FAN Guangjian,ZANG Lijuan E-mail:dushaoqian@163.com;gjfan@shsmu.edu.cn;lou19941205@163.com
Supported by:
Clinical Research Plan of Shanghai Hospital Development Center(SHDC2020CR2065B);Natural Science Foundation of Zhejiang Province(LY20H160010)

摘要/Abstract

摘要：

目的·探究C-X-C模体趋化因子配体9（C-X-C motif chemokine ligand 9，CXCL9）表达对乳腺癌患者预后的影响及其与肿瘤浸润免疫细胞（tumor-infiltrating immune cell，TIIC）的相关性。方法·从癌症基因组图谱（The Cancer Genome Atlas，TCGA）数据库获得乳腺癌患者的1 100例癌组织和112例癌旁组织的转录组数据，并采用CIBERSORT反卷积算法分析乳腺癌免疫微环境中TIIC亚群比例及其对患者的预后影响。分别从TCGA数据库、ImmPort数据库和GEPIA2数据平台下载差异表达基因、免疫相关基因和乳腺癌预后相关基因，运用R语言分析该3类基因集的相交关系，以筛选目的基因。根据已下载的转录组数据，分析CXCL9的正相关基因、CXCL9 mRNA在乳腺癌组织和癌旁组织中的表达差异及其对患者预后的影响。利用STRING数据平台对CXCL9进行蛋白质相互作用（protein-protein interaction，PPI）网络分析，利用R语言对CXCL9正相关基因和PPI网络获得的互作蛋白对应的基因行基因本体数据库（Gene Ontology，GO）功能分析和京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genome，KEGG）通路分析。运用Spearman相关系数分析CXCL9 mRNA表达与TIIC亚群、免疫检查点相关基因的相关性。收集60例乳腺癌患者的石蜡组织样本并制成组织芯片，采用免疫组织化学染色（immunohistochemistry staining，IHC）分析芯片中CXCL9表达和CD8 ⁺ T细胞浸润的相关性。采用多重荧光免疫组织化学染色（multiplex immunohistochemistry staining，mIHC）分析芯片中乳腺癌组织间质内CXCL9⁺细胞的类型。采用Kaplan-Meier（KM）生存曲线分析CXCL9 mRNA表达、CD8⁺T细胞浸润双因素对乳腺癌患者预后的影响。结果·CIBERSORT算法分析显示乳腺癌免疫微环境中TIIC亚群的分布比例差异大，其对患者的预后影响也大不相同。绘制上述3类基因集的韦恩图，筛选出目的基因CXCL9。获得与CXCL9正相关的前150个基因。在乳腺癌4种分子分型中CXCL9 mRNA的表达水平高于癌旁组织（均P=0.000），且其高表达与患者的良好预后相关（HR=0.624，P=0.013）。通过PPI网络分析共获得41个互作蛋白。GO和KEGG分析的结果显示，CXCL9及其相关基因主要富集在免疫调控相关的生物功能和通路。Spearman相关系数分析的结果显示，CXCL9 mRNA表达与CD8⁺T细胞的浸润比例呈正相关，与M2型巨噬细胞的浸润比例呈负相关，且与多数免疫检查点基因的表达呈正相关（均P<0.05）。IHC实验结果显示，相较于癌旁组织，乳腺癌组织中CXCL9的表达较高，且伴随CD8 ⁺ T细胞浸润比例增加（P=0.000）；mIHC结果发现，乳腺癌间质中CXCL9在部分CD68⁺肿瘤相关巨噬细胞和CD11c⁺ 树突状细胞内表达。KM生存曲线显示，当CXCL9高表达时，CD8⁺ T细胞高浸润才能延长乳腺癌患者的生存期。结论·CXCL9可作为乳腺癌患者良好预后的生物标志物，在乳腺癌微环境中CXCL9的高表达与CD8⁺T细胞的浸润比例呈正相关并可能激活其抗肿瘤作用。同时，CXCL9的表达可能与肿瘤组织中募集淋巴细胞至肿瘤微环境发挥抗肿瘤免疫应答功能密切相关。

关键词: 乳腺癌, C-X-C模体趋化因子配体9, 肿瘤免疫浸润, 生物信息学分析, CD8⁺T细胞

Abstract:

Objective ·To explore the effect of C-X-C motif chemokine ligand 9 (CXCL9) expression on the prognosis of breast cancer patients and its correlation with tumor-infiltrating immune cells (TIICs). Methods ·Transcriptome data of 1 100 breast tumor tissues and 112 adjacent tissues were obtained from The Cancer Genome Atlas (TCGA) database. CIBERSORT deconvolution algorithm was used to analyze the proportion of TIIC subgroups in breast cancer immune microenvironment and its effect on the prognosis of patients. Differentially expressed genes, immune-related genes and breast cancer prognostic-related genes were downloaded from TCGA database, ImmPort database and GEPIA2 data platform, respectively. The intersection relationships of the three gene sets were analyzed by using R language, and the target genes were screened. Based on the downloaded transcriptome data, CXCL9 positive-related genes,the difference of CXCL9 mRNA expression in breast cancer tissues and adjacent tissues and its effect on the prognosis of patients were analyzed. STRING data platform was used to analyze the protein-protein interaction (PPI) network of CXCL9. Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis were performed on CXCL9 positive correlation genes and the genes corresponding to the interacting proteins obtained from the PPI network by using R language. Spearman correlation coefficient was used to analyze the correlation between CXCL9 mRNA expression and TIIC subgroups and immune checkpoint-related genes. Paraffin tissue samples of 60 clinical breast cancer patients were collected and made into tissue chips. The correlation between CXCL9 expression and CD8⁺ T cells infiltration in the tissue chips was detected by immunohistochemical staining (IHC). The types of CXCL9⁺ cells in breast cancer interstitium were analyzed by multiplex immunohistochemistry staining (mIHC). Kaplan-Meier (KM) survival curve was used to analyze the effect of CXCL9 mRNA expression and CD8⁺ T cell infiltration on the prognosis of breast cancer patients. Results ·CIBERSORT algorithm analysis showed that the distribution proportion of TIIC subgroups in breast cancer immune microenvironment varied greatly, and their effect on patients′ prognosis was also different. The Venn diagram of three types of gene sets was drawn, and CXCL9 was screened out. The top 150 positive correlation genes with CXCL9 were obtained. CXCL9 mRNA expression levels in four molecular types of breast cancer were higher than those in adjacent tissues (all P=0.000), and their high expressions were significantly associated with good prognosis of patients (P=0.013). A total of 41 interacting proteins were obtained through PPI network analysis. GO and KEGG analysis showed that CXCL9 and its related genes were mainly enriched in biological functions and pathways related to immune regulation. Spearman correlation coefficient analysis showed that the expression level of CXCL9 mRNA was positively correlated with CD8⁺ T cells infiltration ratio, negatively correlated with M2-type macrophages infiltration ratio, and positively correlated with most immune checkpoint genes expression (all P<0.05). IHC experiments showed that CXCL9 was highly expressed in breast cancer tissues compared with adjacent tissues, accompanied by an increased percentage of CD8⁺ T cells infiltration (P=0.000). mIHC results showed that CXCL9 was expressed in some CD68⁺ tumor-associated macrophages (TAMs) and CD11c⁺ dendritic cells (DCs) in the stroma of breast cancer. KM survival curve showed that when CXCL9 was highly expressed, CD8⁺ T cells high infiltration could prolong the survival of breast cancer patients. Conclusion ·CXCL9 can be used as a biomarker for good prognosis of breast cancer patients. The high expression of CXCL9 in the microenvironment of breast cancer is positively correlated with the infiltration ratio of CD8⁺ T cells and may activate its anti-tumor effect. The expression of CXCL9 may be closely related to the recruitment of lymphocytes into the tumor microenvironment for anti-tumor immune response.

Key words: breast cancer, C-X-C motif chemokine ligand 9 (CXCL9), tumor immunoinfiltration, bioinformatics analysis, CD8⁺T cell

中图分类号:

R737.9

杜少倩, 陶梦玉, 曹源, 王红霞, 胡孝渠, 范广建, 臧丽娟. CXCL9在乳腺癌中的表达及其与肿瘤免疫浸润特征的相关性研究[J]. 上海交通大学学报（医学版）, 2023, 43(7): 860-872.

DU Shaoqian, TAO Mengyu, CAO Yuan, WANG Hongxia, HU Xiaoqu, FAN Guangjian, ZANG Lijuan. CXCL9 expression in breast cancer and its correlation with the characteristics of tumor immunoinfiltration[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2023, 43(7): 860-872.

图/表 7

图1 乳腺癌TME中TIIC亚群的差异分析Note： A. Proportions of 22 TIIC subgroups in the microenvironment of breast cancer and adjacent tissues. B. Comparison of the distributions of TIIC subgroups in breast cancer tissues and adjacent tissues by heatmap (①P=0.010, ②P=0.001, ③P>0.05). C. Differential analysis of 22 types of TIICs in different molecular types of breast cancer (①P=0.010, ②P=0.001, ③P>0.05).

Fig 1 Differential analysis of TIIC subgroups in breast cancer TME

图2 TME中TIIC浸润水平对乳腺癌患者预后影响

Fig 2 Effect of TIICs infiltration ratio in TME on the prognosis of breast cancer patients

图3 目的基因的筛选Note： A. Volcano plot of DEGs between breast cancer tissues and adjacent tissues. B. Venn diagram of the target gene screened in the three gene sets.

Fig 3 Screening of target gene

图4 目的基因的表达水平对乳腺癌患者的预后影响Note： A. Expression of CXCL9 mRNA in different types of tumor and their corresponding normal tissues (①P=0.050, ②P=0.010, ③P=0.001). ACC—adrenocortical carcinoma; BLCA—bladder urothelial carcinoma; BRCA—breast invasive carcinoma; CESC—cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL—cholangiocarcinoma; COAD—colon adenocarcinoma; DLBC—lymphoid neoplasm diffuse large B-cell lymphoma; ESCA—esophageal carcinoma; GBM—glioblastoma multiforme; HNSC—head and neck squamous cell carcinoma; KICH—kidney chromophobe; KIRC—kidney renal clear cell carcinoma; KIRP—kidney renal papillary cell carcinoma; LAML—acute myeloid leukemia; LGG—brain lower grade glioma; LIHC—liver hepatocellular carcinoma; LUAD—lung adenocarcinoma; LUSC—lung squamous cell carcinoma; MESO—mesothelioma; OV—ovarian serous cystadenocarcinoma; PAAD—pancreatic adenocarcinoma; PCPG—pheochromocytoma and paraganglioma; PRAD—prostate adenocarcinoma; READ—rectum adenocarcinoma; SARC—sarcoma; SKCM—skin cutaneous melanoma; STAD—stomach adenocarcinoma; TGCT—testicular germ cell tumor; THCA—thyroid carcinoma; THYM—thymoma; UCEC—uterine corpus endometrial carcinoma; UCS—uterine carcinosarcoma; UVM—uveal melanoma. B. Boxplot of CXCL9 mRNA expression in four molecular types of breast cancer and adjacent tissues. C/D. Observation (C) and statistical analysis (D) of differential expression of CXCL9 in breast cancer tissues and adjacent tissues by IHC. E. Detection of the effect of CXCL9 mRNA expression on prognosis of breast cancer patients by KM survival curve.

Fig 4 Effect of the expression of target genes on the prognosis of breast cancer patients

图5 CXCL9的PPI网络构建和 CXCL9 及其相关基因的GO功能分析和KEGG通路分析Note： A. PPI network of CXCL9. Red indicates proteins with CXCL9 interaction score >0.9, and blue indicates proteins with CXCL9 interaction score >0.7. PF4—platelet factor 4; PF4V1—platelet factor 4 variant 1; MAP2K3/6—dual specificity mitogen-activated protein kinase kinase 3/6; GNAI2—guanine nucleotide-binding protein G(i) subunit alpha-2; GNG2—guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-2; GNB1—guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1; SFTPC—surfactant protein C; CCR1—C-C chemokine receptor type 1; CXCR1—C-X-C chemokine receptor type 1; CX3CR1—CX3C chemokine receptor 1; CCL5—C-C motif chemokine ligand 5; DPP4—dipeptidyl peptidase-4; GPR29—chemokine (C-C motif) receptor 6; XCR1—chemokine XC receptor 1; C1QA/B—complement component 1, Q subcomponent, A chain/B chain; IDO1—indoleamine 2,3-dioxygenase 1; SLAMF8—SLAM family member 8; ADAMDEC1—adam-like, decysin 1; GBP5—guanylate-binding protein 5. B/C. GO function analysis (B) and KEGG pathway analysis (C) of CXCL9 and its related genes.

Fig 5 PPI network construction of CXCL9 and GO functional analysis and KEGG pathway analysis of CXCL9 and its related genes

图6 乳腺癌中 CXCL9 mRNA表达与TIIC浸润水平的相关性分析Note： A. Spearman correlation analysis of TIICs infiltration ratio and CXCL9 mRNA expression. B. Observation of the relationship between CXCL9 expression and CD8+ T cells infiltration ratio by IHC. S—stroma; T—tumor. C. Correlation between CXCL9 expression and the number of CD8+ T cells infiltration by beeswarm plot. D. Fluorescence images of CXCL9+ cell species in breast cancer interstitium. Yellow indicates CXCL9, orange indicates CD68, green indicates CD11c, red indicates CK7, and blue indicates DNA. The left dashed box indicates the field of upper 3 small figures, and the right one indicates the field of lower 3 small figures. The arrows indicate CXCL9+CD68+ cells or CXCL9+CD11c+ cells.

Fig 6 Correlation between CXCL9 mRNA expression and TIICs infiltration ratio in breast cancer

图7 CXCL9 mRNA表达水平和CD8+ T细胞浸润比例对乳腺癌患者预后的影响Note： A. Correlation between CXCL9 mRNA and immune checkpoint genes. B. Heatmap of various TIICs infiltration ratio in relation to CXCL9 and immune checkpoint genes. C. Effect of combination of high and low expression of CXCL9 mRNA and high and low infiltration of CD8+ T cells on the prognosis of breast cancer patients.

Fig 7 Effect of CXCL9 mRNA expression level and CD8+ T cells infiltration ratio on the prognosis of breast cancer patients

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