Development of an Immune-Related Prognostic Signature in Breast Cancer

Abstract

Background: Although increased early detection, diagnosis and treatment have improved the outcome of breast cancer patients, prognosis estimation still poses challenges due to the disease heterogeneity. Accumulating data indicated an evident correlation between tumor immune microenvironment and clinical outcomes. Objective: To construct an immune-related signature that can estimate disease prognosis and patient survival in breast cancer. Methods: Gene expression profiles and clinical data of breast cancer patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, which were further divided into a training set (n = 499), a testing set (n = 234) and a Meta-validation set (n = 519). In the training set, immune-related genes were recognized using combination of gene expression data and ESTIMATE algorithm-derived immune scores. An immune-related prognostic signature was generated with LASSO Cox regression analysis. The prognostic value of the signature was validated in the testing set and the Meta-validation set. Results: A total of 991 immune-related genes were identified. Twelve genes with nonzero coefficients in LASSO analysis were used to construct an immune-related prognostic signature. The 12-gene signature significantly stratified patients into high and low immune risk groups in terms of overall survival independent of clinical and pathologic factors. The signature also significantly stratified overall survival in clinical defined groups, including stage I/II disease. Several biological processes, such as immune response, were enriched among genes in the immune-related signature. The percentage of M 2 macrophage infiltration was significantly different between low and high immune risk groups. Timedependent ROC curves indicated good performance of our signature in predicting the 1-, 3-and 5-year overall survival for patients from the full TCGA cohort. Furthermore, the composite signature derived by integrating immune-related signature with clinical factors, provided a more accurate estimation of survival relative to molecular signature alone.

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

“…By utilizing the breast cancer cohorts in the TCGA database and ESTIMATE algorithm-derived scores, we rst analyzed the relationship between stromal/immune scores and different stages or subtypes of breast cancer and found that stromal/immune scores were highly correlated with subtypes of breast cancer, especially in the basal-like subtype, and the luminal B and HER2-enriched subtypes also showed relevance with immune scores. This nding demonstrated that TME variation correlated with subtypes of breast cancer and possessed immunological characteristics, which is consistent with the ndings of a previous study [11]. However, the high immune-related scores did not predict a better prognosis in breast cancer subtypes.…”

Peer Review #1 of "Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes (v0.1)"

“…Our ROC analysis showed that the AUC was 0.842 for 3-year OS and 0.808 for 5-year OS, higher than the AUC values reported in previous models (Lin et al, 2020;Xie et al, 2019), thus indicating that our model had better predictive ability.…”

Identification and Validation of a Five-gene Signature Associated with Overall Survival in Breast Cancer Patients

“…A 6-KIFs-based risk score (KIF10, KIF15, KIF18A, KIF18B, KIF20A, KIF4A) reported by Li et al [42] was proved to be associated with the prognosis in patients with breast cancer. Immune related index in breast cancer were also found through LASSO by Xie et al [43] and Zheng et al [44]. These researchers indicate that gene signatures could serve as risk factors for cancer management and played a vital role in predicting cancer prognosis.…”