Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Hengjun Zhang; Shuai Ma; Yusong Wang; Xiuyun Chen; Yumeng Li; Mozhi Wang; Yingying Xu

doi:10.1016/j.isci.2024.109133

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

iScience. 2024 Feb 5;27(3):109133. doi: 10.1016/j.isci.2024.109133. eCollection 2024 Mar 15.

Authors

Hengjun Zhang¹, Shuai Ma², Yusong Wang¹, Xiuyun Chen¹, Yumeng Li¹, Mozhi Wang¹, Yingying Xu¹

Affiliations

¹ Department of Breast Surgery, the First Hospital of China Medical University, Shenyang, Liaoning, P.R. China.
² Department of Thyroid and Breast Surgery, People's Hospital of China Medical University (Liaoning Provincial People's Hospital), Shenyang, China.

Abstract

Despite adjuvant chemotherapy and endocrine therapy in luminal breast cancer (LBC), relapses are common. Addressing this, we aim to develop a prognostic model to refine adjuvant therapy strategies, particularly for patients at high recurrence risk. Notably, obesity profoundly affects the tumor microenvironment (TME) of LBC. However, it is unclear whether obesity-related biological features can effectively screen high-risk patients. Utilizing weighted gene coexpression network analysis (WGCNA) on RNA sequencing (RNAseq) data, we identified seven obese LBC genes (OLGs) closely associated with patient prognosis. Subsequently, we developed a luminal obesity-gene clinical prognostic index (LOG-CPI), combining a 7-gene signature, TNM staging, and age. Its predictive efficacy was confirmed across validation datasets and a clinical cohort (5-year accuracy = 0.828, 0.760, 0.751, and 0.792, respectively). LOG-CPI emerges as a promising predictor for clinical prognosis and treatment response, helping distinguish molecular and immunological features in LBC patients and guiding clinical practice by identifying varying prognoses.

Keywords: Biological sciences; Cancer systems biology; Health sciences; Internal medicine; Medical specialty; Medicine; Natural sciences; Oncology; Systems biology.