Development and Validation of a Risk Prediction Model for Breast Cancer Prognosis Based on Depression-Related Genes

Abstract

Background: Depression plays a significant role in mediating breast cancer recurrence and metastasis. However, a precise risk model is lacking to evaluate the potential effects of depression on breast cancer prognosis. In this study, we established a depression-related gene (DRG) signature that can predict overall survival (OS) and elucidated its correlation with pathological parameters and sensitivity to therapy in breast cancer. Methods: The model training and validation assays were based on the analyses of 1096 patients from The Cancer Genome Atlas (TCGA) database and 2969 patients from GSE96058. A risk signature was established through univariate and multivariate Cox regression analyses. Findings: Ten DRGs were determined to construct the risk signature. Multivariate analysis revealed that the signature was an independent prognostic factor for OS. Receiver operating characteristic (ROC) curves indicated good performance of the model in predicting 1-, 3-, and 5-year OS, particularly for patients with triple-negative breast cancer (TNBC). In the high-risk group, the proportion of immunosuppressive cells, including M0 macrophages, M2 macrophages, and neutrophils, was higher than that in the low-risk group. Furthermore, low-risk patients responded better to chemotherapy and endocrine therapy. Finally, a nomogram consisting of risk score, age, tumor-node-metastasis (TNM) stage, and molecular subtypes was established, and it showed good agreement between the predicted and observed OS. Interpretation: The 10-gene risk model not only highlights the significance of depression in breast cancer prognosis, but also provides a novel gene-testing tool to better prevent the potential adverse impact of depression on breast cancer prognosis. Funding Statement: This work was supported by the National Natural Science Foundation of China [82074165, 81873306, 81973526, 82004132, 82004373]; Science and Technology Planning Project of Guangdong Province [2021A0505030059, 2017B030314166, 2016A030306025]; Department of Education of Guangdong Province [2018KZDXM022, A1-2606-19-111-009, 2019KQNCX019]; The 2020 Guangdong Provincial Science and Technology Innovation Strategy Special Fund (Guangdong- Hong Kong-Macau Joint Lab) [2020B1212030006]; Guangdong traditional Chinese medicine bureau project [20201132, 20211114]; the Ph.D. Start-up Fund of Natural Science Foundation of Guangdong Province [2018A030310506]; Guangzhou science and technology project [202102010316，201904010407]; The Specific Research Fund for TCM Science and Technology of Guangdong provincial Hospital of Chinese Medicine [YN2018MJ07, YN2018QJ08]，and the Foundation for Young Scholars of Guangzhou University of Chinese Medicine [QNYC20190101]. Declaration of Interests: The authors have declared that no competing interest exists. Ethics Approval Statement: . Ethical approval was waived by institutional ethics committee because data are obtained from public databases and all the patients are de-identified.