Construct lactylation-related gene signature to effectively predict the disease-free survival and treatment responsiveness in prostate cancer based on the 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 signature genes for prostate cancer. The five overlapping signature genes screened by five machine learning algorithms were used to construct a survival prognostic model by lasso cox regression analysis. Further analyses were performed for functional enrichment, immune infiltration and tumor mutation analysis. Then, we predicted chemosensitivity differences from prostate cancer gene expression for some chemotherapeutics drugs. Besides, the transcript levels of five genes were verified in prostate cancer cell lines by qPCR. Subsequently, a nomogram scoring system was established to predict disease-free survival of patients by combining clinicopathological features and lactylation-related risk scores. Results The lactylation-related gene signature, which is based on five lactylation-related genes, showed a good efficacy in predicting the disease-free survival of prostate cancer and has a good potential for clinical application. The proportion of regulatory T cells and M2 macrophages is higher in the high-risk group of prostate cancer patients, so the worse prognosis of the high-risk group may be related to immunosuppression. The prostate cancer patients in the high-risk group were more sensitive to 7 chemotherapeutic drugs related to DNA synthesis and repair. Conclusions This study established a lactylation-related gene signature, which accurately predicted disease-free survival in prostate cancer patients. The lactylation-related gene signature can help clinicians identify prostate cancer patients with shorter disease-free survival, and also provide a reference for personalized drug treatment.