Role of machine learning in molecular pathology for breast cancer: A review on gene expression profiling and RNA sequencing application

• Published in: Critical reviews in oncology/hematology Vol. 213; p. 104780
• Main Authors: Rezaei, Sahar, Hamedani, Zeinab, Ahmadi, Kousar, Ghannadikhosh, Parna, Motamedi, Alireza, Athari, Maedeh, Yousefi, Hengameh, Rajabi, Amir Hossein, Abbasi, Alireza, Arabi, Hossein
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.09.2025
• Subjects: Artificial Intelligence; Biomarkers, Tumor - genetics; Breast Cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Breast Neoplasms - pathology; Female; Gene Expression Profiling; Gene Expression Profiling - methods; Gene Expression Regulation, Neoplastic; Humans; Machine Learning; Molecular Pathology; Pathology, Molecular - methods; Prognosis; RNA Sequencing; Sequence Analysis, RNA - methods; Transcriptome; RNA Sequencing; Gene Expression Profiling; Breast Cancer; Artificial Intelligence; Molecular Pathology
• ISSN: 1040-8428; 1879-0461; 1879-0461
• DOI: 10.1016/j.critrevonc.2025.104780

Breast cancer is the most prevalent cancer among women, with growing incidence and mortality rates. Regardless of remarkable progress in cancer research, breast cancer remains a major concern due to its complex nature. These factors underscore the necessity of innovative research and diagnostic tools. Attention to gene signatures and biotechnology methods have shown significant performance in the diagnosis and management of breast cancer. Currently, artificial intelligence (AI) is known as a revolutionary tool to analyze data, identify biomarkers, and enrich diagnostic and prognostic accuracy. Therefore, the integration of breast cancer datasets with artificial intelligence can play a crucial role in the control of breast cancer. This review explores advanced machine learning techniques to analyze transcriptomic data while focusing on breast cancer subtype classification and its potential impact and limitations. A comprehensive literature search was performed in PubMed, Scopus, WoS, Embase, and IEEE Xplore. Duplicates were removed, two reviewers screened articles, and two additional reviewers resolved conflicts. Data extraction included details on molecular methods, AI techniques, clinical targets, study populations, and data analysis methods which were used to categorize relevant studies into RNA sequencing and gene expression profiling groups. In the initial stage, 7287 articles were identified, and 54 were retained following further screening, 24 in RNA sequencing and 30 in gene expression profiling. A review of these studies showed how artificial intelligence is advancing breast cancer research by using RNA sequencing and gene expression profiling. AI algorithms, including Random Forest, CNNs, SVMs, and LASSO, were the most applied techniques that showed significant potential to identify biomarkers, prognostic survival, and optimize drug responses to manage breast cancer. The methods of artificial intelligence hold very great potential for change in the field of breast cancer. This promising progress can be seen in every aspect including diagnosis, prognosis, and treatment. However, it is important to note that we are still in the early stages of progress, and larger-scale studies and interdisciplinary collaborations in this field are needed. [Display omitted] •AI-integrated molecular pathology improves breast cancer management.•Random Forest, CNNs, SVMs, and LASSO were the most applied techniques.•AI has significant potential to identify breast cancer biomarkers.•AI models predict metastases, assess survival, and optimize drug response.