Development and validation of a ferroptosis-related gene signature for prognostic prediction and therapeutic target identification in invasive lobular carcinoma.

Background: Invasive lobular carcinoma (ILC) accounts for 15% of breast cancers and presents challenges such as chemotherapy resistance and poorer survival outcomes compared to other subtypes. While often managed similarly to invasive ductal carcinoma (IDC), ILC requires tailored approaches due to its distinct biology. Ferroptosis, an iron-dependent form of cell death, shows potential in overcoming therapeutic resistance but remains unexplored in ILC. This study aimed to identify ferroptosis-related molecular subtypes, develop a robust gene signature using machine learning, construct an integrated prognostic model, and uncover potential therapeutic targets for ILC. Methods: This study integrated data from a total of 490 patients with ILC across four datasets from The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and Gene Expression Omnibus (GEO). The TCGA cohort was utilized for model development, while the remaining cohorts served for independent external validation. ILC samples were classified into two subtypes based on the expression levels of ferroptosis-related genes via consensus clustering. The associations between the subtypes and the tumor microenvironment (TME), biological function, and mutation were assessed. A ferroptosis-related gene signature (FRGS) was developed using the integration of machine learning. A prediction model was subsequently constructed by combining the FRGS with clinical features. Sensitivity analysis and molecular docking were used to identify potentially effective targets and drugs. Results: We identified two ferroptosis-related subtypes and found that Cluster 2 had increased immune cell infiltration. By integrating machine learning, we identified 10 hub biomarkers of ILC and developed a FRGS. The FRGS was proven to be an independent risk factor for overall survival. Combining the FRGS with clinical features, a stable and superior ILC prognostic model was constructed. Sensitivity analysis and molecular docking revealed that Conclusions: By integrating multi-omics analysis, machine learning and molecular docking, we established a robust prognostic model for ILC, revealed two distinct ferroptosis-related molecular subtypes, and identified potential therapeutic targets and candidate drugs. These findings may help advance the development of personalized medicine and targeted therapies for ILC.