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

Abstract

BackgroundDepression plays a significant role in mediating breast cancer recurrence and metastasis. However, a precise risk model is lacking to evaluate the potential impact of depression on breast cancer prognosis. In this study, we established a depression-related gene (DRG) signature that can predict overall survival (OS) and elucidate its correlation with pathological parameters and sensitivity to therapy in breast cancer.MethodsThe model training and validation assays were based on the analyses of 1,096 patients from The Cancer Genome Atlas (TCGA) database and 2,969 patients from GSE96058. A risk signature was established through univariate and multivariate Cox regression analyses.ResultsTen 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 integrating risk score, age, tumor-node-metastasis (TNM) stage, and molecular subtypes were established, and it showed good agreement between the predicted and observed OS.ConclusionThe 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.

Integrative dissection of 5-hydroxytryptamine receptors-related signature in the prognosis and immune microenvironment of breast cancer

“…However, few predictive models based on depression-associated genes (DRG) have been established. Wang ( 41 ) et al. established a signature consisting of 10-DRG that predicted OS, which performed well in predicting OS, especially for patients with TNBC.…”

“…However, few predictive models based on depression-associated genes (DRG) have been established. Wang (41) et al established a signature consisting of 10-DRG that predicted OS, which performed well in predicting OS, especially for patients with TNBC. However, this model involved many DRGs, making it difficult to determine a precise biomarker that predicts depression in breast cancer.…”

A signature constructed with mitophagy-related genes to predict the prognosis and therapy response for breast cancer

“…As a disease that is strongly associated with genetic abnormalities, the genome of BC is valuable for exploration. A large body of literature has reported good bioinformatics tools for BC, such as depression-related models, angiogenesis-related models, or lactate metabolism-related models [ 45 – 47 ]. However, no mitophagy-related signature of BC has been studied, which in our study showed excellent prognostic ability and was related to distinct immune cell infiltration patterns.…”

Crosstalk between mitochondrial and lysosomal co-regulators defines clinical outcomes of breast cancer by integrating multi-omics and machine learning

“…Additionally, an extensive review of the pertinent literature from the past ve years was conducted. Subsequently, 18 signature genes associated with diverse biological processes, including exosome (13,14), TP53 mutation (15), necroptosis(16), depression (17), pyroptosis(18), autophagy (7,19), immune (20)(21)(22), angiogenesis (23), cuproptosis(18, 20), tumor microenvironment (TME) (24), methylation (25), natural killer cell (26), lipid metabolism (27), were incorporated for comparative analysis. The mlMSG prognostic model exhibited superior C-index performance compared to nearly all models present in TCGA and GEO datasets (Fig.…”