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. 2021 Feb 6;21(1):126.
doi: 10.1186/s12885-021-07837-1.

Novel immune-related genes in the tumor microenvironment with prognostic value in breast cancer

Wen Tan  1 , Maomao Liu  1 , Liangshan Wang  1 , Yang Guo  2 , Changsheng Wei  2 , Shuqi Zhang  2 , Chengyu Luo  2 , Nan Liu  3
Affiliations

Novel immune-related genes in the tumor microenvironment with prognostic value in breast cancer

Wen Tan et al. BMC Cancer. .

Abstract

Background: Breast cancer is one of the most frequently diagnosed cancers among women worldwide. Alterations in the tumor microenvironment (TME) have been increasingly recognized as key in the development and progression of breast cancer in recent years. To deeply comprehend the gene expression profiling of the TME and identify immunological targets, as well as determine the relationship between gene expression and different prognoses is highly critical.

Methods: The stromal/immune scores of breast cancer patients from The Cancer Genome Atlas (TCGA) were employed to comprehensively evaluate the TME. Then, TME characteristics were assessed, overlapping genes of the top 3 Gene Ontology (GO) terms and upregulated differentially expressed genes (DEGs) were analyzed. Finally, through combined analyses of overall survival, time-dependent receiver operating characteristic (ROC), and protein-protein interaction (PPI) network, novel immune related genes with good prognosis were screened and validated in both TCGA and GEO database.

Results: Although the TME did not correlate with the stages of breast cancer, it was closely associated with the subtypes of breast cancer and gene mutations (CDH1, TP53 and PTEN), and had immunological characteristics. Based on GO functional enrichment analysis, the upregulated genes from the high vs low immune score groups were mainly involved in T cell activation, the external side of the plasma membrane, and receptor ligand activity. The top GO terms of the upregulated DEGs from the high vs low immune score groups exhibited better prognosis in breast cancer; 15 of them were related to good prognosis in breast cancer, especially CD226 and KLRC4-KLRK1.

Conclusions: High CD226 and KLRC4-KLRK1 expression levels were identified and validated to correlate with better overall survival in specific stages or subtypes of breast cancer. CD226, KLRC4-KLRK1 and other new targets seem to be promising avenues for promoting antitumor targeted immunotherapy in breast cancer.

Keywords: Breast cancer; Immune-related gene; Immunotherapy; Prognostic signature; Tumor microenvironment.

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Conflict of interest statement

The authors declare that there are no financial or commercial conflicts of interest.

Figures

Fig. 1
Fig. 1
Stromal scores and immune scores were closely correlated with breast cancer. (A) Violin plot of stromal scores in different stages of breast cancer; (B) violin plot of immune scores in different stages of breast cancer; (C) violin plot of stromal scores in four subtypes of breast cancer; (D) violin plot of immune scores in four subtypes of breast cancer. *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; and ****p ≤ 0.0001
Fig. 2
Fig. 2
Gene mutations were associated with stromal scores and immune scores. (A) Distribution of stromal scores for BRCA1 mutant and BRCA1 wildtype; (B) distribution of stromal scores for BRCA2 mutant and BRCA2 wildtype; (C) distribution of stromal scores for CDH1 mutant and CDH1 wildtype; (D) distribution of stromal scores for PTEN mutant and PTEN wildtype; (E) distribution of stromal scores for TP53 mutant and TP53 wildtype; (F) distribution of immune scores for BRCA1 mutant and BRCA1 wildtype; (G) distribution of immune scores for BRCA2 mutant and BRCA2 wildtype; (H) distribution of immune scores for CDH1 mutant and CDH1 wildtype; (I) distribution of immune scores for PTEN mutant and PTEN wildtype; (J) distribution of immune scores for TP53 mutant and TP53 wildtype. *p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; and ****p ≤ 0.0001
Fig. 3
Fig. 3
Differential gene expression profiles in breast cancer and immune score and stromal score grouping. (A) Heatmap of the top 100 upregulated genes in the high vs low stromal score groups; (B) heatmap of the top 100 upregulated genes in the high vs low immune score groups; (C) volcano plot of differential genes in the high vs low stromal score groups; (D) volcano plot of differential genes in the high vs low immune score groups
Fig. 4
Fig. 4
Differentially expressed genes (DEGs) for Gene Ontology (GO) enrichment and Kyto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. (A) GO enrichment analysis of the upregulated genes in the high vs low stromal score groups; (B) GO enrichment analysis of the upregulated genes in the high vs low immune score groups; (C) KEGG pathway analysis of the upregulated genes in the high vs low stromal score groups; (D) KEGG pathway analysis of the upregulated genes in the high vs low immune score groups; (E) Venn diagrams showing the overlapping 1 genes; (F) Venn diagram showing the overlapping 2 genes
Fig. 5
Fig. 5
Protein-protein interaction (PPI) analysis of the overlapping immune-related genes. (A) PPI network of 31 screened genes, which included 29 nodes and 110 edges; (B) PPI network of 15 screened genes. The logFC value of the gene expression was used to reflect the color of each node, and the size of the node represents the number of proteins interacting with the designated protein
Fig. 6
Fig. 6
Kaplan-Meier survival curves for the 15 screened genes with good prognosis in breast cancer. The relationship between the expression of (A) CD226, (B) KLRD1, (C) KLRC4-KLRK1, (D) IL2, (E) KLRK1, (F) ITK, (G) SPN, (H) SLAMF1, (I) CD1C, (J) FASLG, (K) CD40LG, (L) TBX21, (M) IL7, (N) LAT, or (O) ITGAX and overall survival in breast cancer. P < 0.05 in the log-rank test
Fig. 7
Fig. 7
CD226, KLRD1 and KLRC4-KLRK1 had higher predictive accuracies than the other screened genes. Time-dependent ROC analyses were performed to compare the 15 screened gene signatures: (A) CD226, (B) KLRD1, (C) KLRC4-KLRK1, (D) IL2, (E) KLRK1, (F) ITK, (G) SPN, (H) SLAMF1, (I) CD1C, (J) FASLG, (K) CD40LG, (L) TBX21, (M) IL7, (N) LAT, and (O) ITGAX in predicting 1-year, 3-year and 5-year overall survival
Fig. 8
Fig. 8
High expression of CD226 and KLRC4-KLRK1 results in a better prognosis in breast cancer. The survival probability differences between high and low CD226 expression in (A) stage II, (B) stage III, and (C) the luminal B subtype of breast cancer. The survival probability differences between high and low KLRC4-KLRK1 expression in (D) stage II, (E) stage III, and (F) the luminal B subtype of breast cancer. P < 0.05 in the log-rank test
Fig. 9
Fig. 9
Validating the prognostic genes identified in the TCGA database are equally significant in GEO database. The relationship between (A) the expression of CD226 or (B) the expression of KLRC4-KLRK1 and breast cancer prognosis in the GSE42568 dataset; the relationship between (C) the expression of CD226 or (D) the expression of KLRC4-KLRK1 and breast cancer prognosis in the GSE20685 dataset; the relationship between the expression of CD226 and (E) stage I, (F) stage II, and (G) stage III breast cancer prognosis in the GSE20685 dataset; the relationship between the expression of KLRC4-KLRK1 and (H) stage I, (I) stage II, (J) stage III breast cancer prognosis in the GSE20685 dataset. P < 0.05 in the log-rank test
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