[CD40LG is a novel immune- and stroma-related prognostic biomarker in the tumor microenvironment of invasive breast cancer]

Abstract

Objective: To identify tumor microenvironment (TME)- related genes associated with the occurrence of invasive breast cancer as potential prognostic biomarkers and therapeutic targets.

Methods: RNA transcriptome data and clinically relevant data were retrieved from TCGA database, and the StromalScore and ImmuneScore were calculated using the ESTIMATE algorithm. The differentially expressed genes (DEGs) were screened by taking the intersection. A protein- protein interaction network was established, and univariate COX regression analysis was used to identify the core genes among the DEGs. A core gene was selected for GSEA and CIBERSORT analysis to determine the function of the core gene and the proportion of tumor-infiltrating immune cells, respectively. Western blotting and qRT-PCR were performed to verify the expression level of CD40LG in breast cancer cell lines and clinical specimens.

Results: A total of 1222 samples (124 normal and 1098 tumor samples) were extracted from TCGA for analysis, from which 487 DEGs were identified. These genes were mainly enriched in immune-related pathways, and crossover analysis identified 11 key genes (CD40LG, ITK, CD5, CD3E, SPN, IL7R, CD48, CCL19, CD2, CD52, and CD2711) associated with breast cancer TME status. CD40LG was selected as the core gene, whose high expression was found to be associated with a longer overall survival of breast cancer patients (P=0.002), and its expression level differed significantly with TNM stage and tumor size (P < 0.05). GSEA and CIBERSORT analyses indicated that CD40LG expression level was associated with immune activity in the TME. Western blotting and qRT-PCR showed that the protein and mRNA expression of CD40LG were significantly lower in breast cancer cells and cancer tissues than in normal breast cells and adjacent tissues.

Conclusions: The high expression of CD40LG in TME is positively correlated with the survival of patients with invasive breast cancer, suggesting its value as a potential new biomarker for predicting prognosis of the patients.

Keywords: CD40LG; TCGA; breast cancer; tumor microenvironment; tumor-infiltrating immune cells.

图 1

本研究的流程图 Flow chart of this study.

图 2

ImmuneScore, StromalScore, and ESTIMATEScore与BRCA患者生存率的关系 Association of ImmuneScore, StromalScore, and ESTIMATEScorewith survival rate of BRCA patients. Kaplan-Meier survival analysis was performed in BRCA patients with high and low scores. A: ImmuneScores (P=0.011). B: StromalScores (P=0.784). C: ESTIMATEScores (P=0.142).

图 3

BRCA患者ImmuneScore（A）, StromalScore（B）and ESTIMATEScore（C）与年龄、性别、T分期、N分期、M分期和病理分期的关系 Correlation of ImmuneScore (A), StromalScore (B), ESTIMATEScore (C) with age, sex, T stage, N stage, M stage and pathological stage of the patients with BRCA.

图 4

DEGs的热图、火山图、维恩图及GO和KEGG富集分析 Heatmap, Volcano plots, Venn diagrams, and GO and KEGG enrichment analyses of the differentially expressed genes (DEGs). A-C: Heatmap and Volcano plots of DEGs were obtained by comparing the high score group ImmuneScore (A, B) and the low score group StromalScore(C, D) (FDR adjustment P < 0.05, | log FC| > 1). E, F: Venn diagrams of upregulated (E) and downregulated (F) DEGs shared by the ImmuneScore and StromalScore analyses. G, H: Bubble diagram of GO (G) and KEGG (H) enrichment analysis (P < 0.05 vs Q < 0.05).

图 5

PPI网络与COX回归分析 PPI network and COX regression analysis. A, B: The PPI network (A) is established with a minimum confidence value of 0.9, and the visualization was carried out using Cytoscape (B). MNC algorithm was used to identify the core genes in PPI network. C, D: The red node represents the gene with higher MNC score, the yellow node represents the gene with lower MNC score (C), and the first 30 genes with higher score (D). E: Univariate COX regression analysis with P < 0.05 as standard. F: Venn diagram after intersection between the first 30 genes from PPI network and the results of univariate COX regression analysis.

图 6

CD40LG的表达与患者生存时间及临床特征的关系 Correlation of CD40LG expression with survival time and clinical characteristics of patients. A, B: There was a significant difference in the expression of CD40LG between tumor tissues and normal tissues. C: Survival analysis of BRCA patients with low and high expression of CD40LG. D-G: Kruskal-Wallis rank sum test for the correlation between CD40LG expression and pathological stage, T stage, N stage and M stage. ^***P < 0.001, ^**P < 0.01.

图 7

CD40LG高表达BRCA样本的GSEA分析 GSEA of BRCA samples with high CD40LG expression.

图 8

CD40LG高表达组与低表达组TIC比例的差异及其与CD40LG表达的相关性 Differences in proportions of TICs between high and low CD40LG expression groups and correlations of TICs with CD40LG expression. A: The proportions of 22 immune cell types in tumor tissues with high CD40LG expression (red) and low expression (green) are compared. B: Scatter plot showing the Pearson correlation between the proportion of the 12 most significant TICs and CD40LG expression. The blue line represents the most suitable linear model (B).

图 9

CD40LG的差异表达验证 Validation of differential expression of CD40LG. A, B: CD40LG mRNA expression level in cancer tissue and adjacent tissue (A) and the corresponding proliferation curve and dissolution curve (B). C, D: CD40LG mRNA expression levels in normal breast cells and different breast cancer cells (C) and the corresponding proliferation curve and dissolution curve (D). E, F: Protein expression level of CD40LG in normal breast cells and different breast cancer cells determined by Western blotting. G, H: Protein expression level of CD40LG in cancer tissues and adjacent tissues determined by Western blotting. ^*P < 0.05, ^***P < 0.001.