Constructing a lactylation-related genes prognostic model to effectively predict the disease-free survival and treatment responsiveness in prostate cancer based on machine learning

Abstract

Abstract Background: More and more studies have revealed that protein lactylation is an important mechanism for lactate to fulfill its duties and participate in important biological processes, which can regulate gene expressions through histone lactation, thereby promoting tumor spread, metastasis and immunosuppression. However, protein lactylation has been poorly studied in prostate cancer. Methods: This study aimed to identify potential novel lactylation biomarkers of prostate cancer by biomarker analysis and to explore immune cell infiltration and treatment responsiveness. By downloading mRNA-Seq data of TCGA prostate cancer data for differential analysis, we obtained the differential genes related to Lactylation in prostate cancer. Five machine learning algorithms were used to screen for lactylation-related key genes for prostate cancer. The five overlapping key genes screened by five machine learning algorithms were used to construct a survival prognostic model by lasso cox regression analysis. Further, the relationships between the model and related pathways, tumor mutation and immune cell subpopulations and drug sensitivity were explored. Then, two risk groups were established according to the risk score calculated by the five lactylation-related genes (LRGs). Subsequently, a nomogram scoring system was established to predict disease-free survival (DFS) of patients by combining clinicopathological features and lactylation-related risk scores. Besides, the mRNA expression levels of five genes were verified in prostate cancer cell lines by qPCR. Results: We identified 5 key LRGs (ALDOA, DDX39A, H2AX, KIF2C, RACGAP1) and constructed the LRGs prognostic model. The AUC values for 1 -, 3 -, and 5-year DFS in the TCGA dataset were 0.762, 0.745, and 0.709. The risk score was a better predictor of disease-free survival than traditional clinicopathological features in prostate cancer. The prostate cancer patients in the high-risk group have a higher proportion of regulatory T cells and M2 macrophage, a higher tumor mutation burden, and a worse prognosis. The high-risk group had a lower IC50 for certain chemotherapeutic drugs, such as Docetaxel, Paclitaxel. Conclusions: The lactylation-related genes prognostic model can effectively predict the disease-free survival and therapeutic responses in prostate patients.