###### Abstract  
Identifying menopause-related breast cancer biomarkers is crucial for enhancing diagnosis, prognosis, and personalized treatment at that stage of the patient's life. In this paper, we present a comprehensive framework for extracting mulotimics biomarkers specifically related to breast cancer incidence before and after menopause. Our approach integrates DNA methylation, gene expression, and copy number alteration data using a systematic pipeline encompassing data preprocessing and handling class imbalance, dimensionality reduction, and classification. The framework starts with MutSigCV for data preprocessing and ensuring data quality. The Synthetic Minority Over-sampling Technique (SMOTE) up-sampling technique is applied to address the class imbalance representation. Then, Principal Component Analysis (PCA) transforms the DNA methylation, gene expression, and copy number alteration data into a latent space. The purpose is to discard irrelevant variations and extract relevant information. Finally, a classification model is built based on the transformed mulotimics data into a unified representation. The framework contributes to understanding the complex interplay between menopause and breast cancer, thereby revealing more precise diagnostic and therapeutic strategies in the future. The explainable artificial intelligence model Shapley based on the XGBoost regressor showed the power of the selected gene expressions for predicting the menopause status, and the potential biomarkers included RUNX1, PTEN, MAP3K1, and CDH1. The literature confirmed the findings. m 20241713. 11.  
and long-term health. These menopausal symptoms may arise through different scenarios: (1) natural menopause coinciding with a breast cancer diagnosis; (2) the reappearance of symptoms after discontinuing hormone replacement therapy; and (3) menopause induced by cancer treatments such as chemotherapy, ovarian ablation/suppression, and adjuvant endocrine therapy [6]. Several studies have analyzed breast cancer menopausal status [7; 8]. In a study involving 167 invasive cancers from postmenopausal women undergoing exclusive endocrine therapy, the expression of ER\(\alpha\) and ER\(\beta\)1 was examined. Specifically, 143 cases received adjuvant Tamoxifen following surgery. The analysis, conducted through immunohistochemistry and reverse transcription RT-PCR, revealed a strong correlation between ER\(\alpha\) protein expression and the corresponding RNA detected using RT-PCR (chi-squared test, \(p<0.001\)). However, ER\(\beta\)1 protein and mRNA exhibited inconsistency in their association [7]. In another postmenopausalence breast cancer study, [PERSON] et al. reported that the epigenome of breast tumors is affected by a complex interaction between the body mass index (BMI) and the menopausal status [8]. In this study, we identify different multiomics activities between premenopause and postmenopausal breast cancer patients. The advancement of high-throughput multiomics technologies, encompassing genomics, transcriptomics, proteomics, and metabolomics, has ushered in a new era of cancer research. These innovative tools enable comprehensive profiling of the molecular landscape of breast cancer, thereby facilitating the discovery of molecular signatures that are intricately linked to disease dynamics, prognosis, and treatment outcomes. Consequently, there is a growing recognition of the potential of multiomics data in uncovering biomarkers that hold promise for enhancing the diagnosis and management of breast cancer [9], particularly within the context of menopausal status [10]. The convergence of multiomics data and advanced computational methodologies presents an opportunity for a holistic exploration of the complex interplay between molecular alterations, menopause, and breast cancer. Through the integration of data across diverse omics domains, researchers can unravel novel insights into the molecular mechanisms underlying the intricate relationship between menopause and breast cancer.