Immune-related gene data-based molecular subtyping related to the prognosis of breast cancer patients

Abstract

Background: Breast cancer (BC), which is the most common malignant tumor in females, is associated with increasing morbidity and mortality. Effective treatments include surgery, chemotherapy, radiotherapy, endocrinotherapy and molecular-targeted therapy. With the development of molecular biology, immunology and pharmacogenomics, an increasing amount of evidence has shown that the infiltration of immune cells into the tumor microenvironment, coupled with the immune phenotype of tumor cells, will significantly affect tumor development and malignancy. Consequently, immunotherapy has become a promising treatment for BC prevention and as a modality that can influence patient prognosis.

Methods: In this study, samples collected from The Cancer Genome Atlas (TCGA) and ImmPort databases were analyzed to investigate specific immune-related genes that affect the prognosis of BC patients. In all, 64 immune-related genes related to prognosis were screened, and the 17 most representative genes were finally selected to establish the prognostic prediction model of BC (the RiskScore model) using the Lasso and StepAIC methods. By establishing a training set and a test set, the efficiency, accuracy and stability of the model in predicting and classifying the prognosis of patients were evaluated. Finally, the 17 immune-related genes were functionally annotated, and GO and KEGG signal pathway enrichment analyses were performed.

Results: We found that these 17 genes were enriched in numerous BC- and immune microenvironment-related pathways. The relationship between the RiskScore and the clinical characteristics of the sample and signaling pathways was also analyzed.

Conclusions: Our findings indicate that the prognostic prediction model based on the expression profiles of 17 immune-related genes has demonstrated high predictive accuracy and stability in identifying immune features, which can guide clinicians in the diagnosis and prognostic prediction of BC patients with different immunophenotypes.

Keywords: Breast cancer; Immunotherapy; TCGA database.

Fig. 1

The relationships between the p values of 62 genes and the HR and expression levels. a The relationships of the p values of 62 genes and the HR is shown. b The relationships of the p values of 62 genes and the expression levels. Red dots represent significantly different immune-related genes (p < 0.05) associated with prognosis

Fig. 2

Verification of the stability of the prognostic prediction model included 17 immune-related genes for the BC patient training set. a The predicted survival according to the ROC curves of the 17-gene risk model in the training set. b The distribution of samples in the Risk-H and Risk-L groups of the training set divided through the 17-gene risk model under different OS periods. c The level of Risk-L group/total sample size with the extension in OS in the training set. d The clustering results of the training set samples. e Difference in the RiskScore between the two groups, which were clustered by the expression of 17 genes in the training set samples

Fig. 3

Verification of the reliability of the prognostic prediction model included 17 immune-related genes for the BC patient test set. a The survival predicted by the ROC curves of the 17-gene risk model in the test set. b The distribution of samples in the Risk-H and Risk-L groups of the test set divided through the 17-gene risk model under different OS periods. c The level of Risk-L group/total sample size with the extension in OS in the test set. d The clustering results of the test set samples. e Difference in the RiskScore between the two groups, which were clustered by the expression of 17 genes in the test set samples

Fig. 4

Verification of the reliability of the prognostic prediction model included 17 immune-related genes for all the BC patients in both sets. a The survival predicted by the ROC curves of the 17-gene risk model. b The distribution of all the samples in the Risk-H and Risk-L groups divided through the 17-gene risk model under different OS periods. c The level of Risk-L group/total sample size with the extension in OS. d The clustering results of all the samples. e Difference in the RiskScore between the two groups, which were clustered by the expression of 17 genes

Fig. 5

The KM survival curve of the 17-gene-based risk model in predicting the OS of the Risk-H and Risk-L groups in the training set (a), test set (b) and all samples (c)

Fig. 6

The GO (a) and KEGG pathway (b) enrichment analyses of the 17 specific immune-related genes

Fig. 7

Correlation of the RiskScore with signaling pathways. The KEGG functional enrichment score of each sample was analyzed, and the correlation with the RiskScore was calculated based on the enrichment score of each pathway in each sample. The top 30 KEGG-related pathways are shown. The clustering analysis was performed according to the enrichment score in the training set

Fig. 8

The relationship between different clinical factors and the RiskScore of BC patients. Comparison of the RiskScore for the different factors of T (a), N (b), M (c), stage (d), age (e) and Her2 expression status (f). The horizontal axis represents the different clinical factors, and the vertical axis represents the RiskScore

Fig. 9

The nomogram model constructed by combining the clinical features (T, N, M, Stage, Age and Her2 expression) with the RiskScore of BC patients

Fig. 10

The forest plot constructed by combining the clinical features with the RiskScore of BC patients