Identification of lysine lactylation (kla)-related lncRNA signatures using XGBoost to predict prognosis and immune microenvironment in breast cancer patients

Abstract

Breast cancer (BC) stands as a predominant global malignancy, significantly contributing to female mortality. Recently uncovered, histone lysine lactylation (kla) has assumed a crucial role in cancer progression. However, the correlation with lncRNAs remains ambiguous. Scrutinizing lncRNAs associated with Kla not only improves clinical breast cancer management but also establishes a groundwork for antitumor drug development. We procured breast tissue samples, encompassing both normal and cancerous specimens, from The Cancer Genome Atlas (TCGA) database. Utilizing Cox regression and XGBoost methods, we developed a prognostic model using identified kla-related lncRNAs. The model's predictive efficacy underwent validation across training, testing, and the overall cohort. Functional analysis concerning kla-related lncRNAs ensued. We identified and screened 8 kla-related lncRNAs to formulate the risk model. Pathway analysis disclosed the connection between immune-related pathways and the risk model of kla-related lncRNAs. Significantly, the risk scores exhibited a correlation with both immune cell infiltration and immune function, indicating a clear association. Noteworthy is the observation that patients with elevated risk scores demonstrated an increased tumor mutation burden (TMB) and decreased tumor immune dysfunction and exclusion (TIDE) scores, suggesting heightened responses to immune checkpoint blockade. Our study uncovers a potential link between Kla-related lncRNAs and BC, providing innovative therapeutic guidelines for BC management.