Integrated immune-related gene signature predicts clinical outcome for patients with Luminal B breast cancer

Abstract

Background: Luminal B breast cancer is routinely treated with chemotherapy and endocrine therapy. However, its sensitivity to treatment remains heterogeneous; therefore, identifying patients who may most benefit remains crucial. Immune-related genes are reportedly related to the prognosis of breast cancer. The purpose of this study was to evaluate the impact of an immune-related gene signature (IRGS) in predicting the prognosis of patients with Luminal B breast cancer.

Methods: We selected patients with Luminal B breast cancer from two large datasets: 488 from the Metabric dataset (training cohort) and 250 patients from The Cancer Genome Atlas (TCGA) dataset (validation cohort). Prognostic analysis was performed to test the predictive value of IRGS, and enrichment analysis and ESTIMATE were used for deeper function analysis.

Results: A prognostic IRGS model containing 12 immune-related genes was developed. After which, we separated patients with Luminal B breast cancer into low- and high-risk groups in terms of disease-free survival (DFS) (P<0.001). Multivariate analysis identified IRGS as an independent prognostic factor. Furthermore, functional analysis showed that the 12 genes were mainly enriched in pathways related to chemotherapy response, whose expression levels showed completely opposing trends in low- and high-risk groups.

Conclusions: The novel IRGS is a satisfactory and reliable biomarker to predict the clinical outcome of patients with Luminal B breast cancer which potentially facilitating individualised management. Further studies are needed to assess the clinical potential in predicting prognosis and the treatment options for Luminal B breast cancer patients.

Du CAI

Postdoc

I focus on leveraging explainable AI and large foundation models to advance medical imaging and digital pathology in colorectal cancer research.

Feng GAO

Professor

My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.