Development of a prognostic model based on the ceRNA network in Triple-Negative Breast cancer.

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with a poor prognosis. Although circular RNAs (circRNAs) have been implicated in cancer progression, their roles in TNBC remain poorly understood. In this study, we aimed to develop a prognostic model for TNBC by constructing a competing endogenous RNA (ceRNA) network. This network integrates circRNAs, long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) to identify potential biomarkers and therapeutic targets for improving clinical outcomes.

Methods: Differentially expressed circRNAs, lncRNAs, and mRNAs were identified from GEO datasets (144 samples: 94 TNBC and 50 normal tissues). A ceRNA network was constructed, and key genes were validated using The Cancer Genome Atlas (TCGA) dataset (115 TNBC and 113 para-cancer tissues). Multivariate Cox regression analysis was performed to develop a prognostic model, and Gene Set Enrichment Analysis (GSEA) was performed to identify associated pathways.

Results: Nine genes (, , , , , , , , ) were identified as key factors in the prognostic model, which demonstrated an area under the curve (AUC) of 0.90. Patients classified as high-risk patients exhibited significantly shorter overall survival (median OS: 8.12 years . 9.51 years,  < 0.01). The mitogen-activated protein kinase (MAPK) signaling pathway was identified as a key regulatory pathway, with circRNAs (hsa_circ_0005455, hsa_circ_000632, hsa_circ_0001666, and hsa_circ_0000069) regulating , , and expression.

Conclusion: This study developed a novel prognostic model based on a ceRNA network analysis, highlighting the critical role of circRNAs and the MAPK signaling pathway in TNBC progression. These findings offer valuable insights into potential biomarkers for TNBC prognosis and reveal promising therapeutic targets for improving patient outcomes.