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Updated: May 13, 2026
Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors
Published on: February 27, 2015
Construction of lactylation (LA) risk signature in prostate cancer based on 4D fast DIA L-lactated quantitative
Fan Zou1, Yongchen Jin1, Ziteng Zhang1
1Department of Urology, Beijing Tongren Hospital, Capital Medical University, Beijing, 100176, China.
Background:
Lactylation (LA) plays a crucial role in regulating protein stability, angiogenesis, and immune modulation. Global lactylation of proteins in prostate cancer cells is a key event in tumor progression. This study aimed to explore the characteristics of LA in patients with prostate cancer (PRAD) and construct a LA-related risk model to predict prognosis.
Methods:
LA-related genes in prostate cancer were screened through quantitative lactylation proteomics of human tissues from Beijing Tongren Hospital, Capital Medical University. Based on the TCGA and GEO databases, patients were divided into two LA-related gene clusters. Principal component analysis (PCA) was used to identify the heterogeneity of the grouping, and differentially expressed genes (DEGs) between the clusters were identified. A LA risk model was constructed using Lasso-Cox regression analysis, and its efficacy was verified in the TCGA, GSE116918, and GSE70769 cohorts through K-M curves, receiver operating characteristic (ROC) curves, and nomograms. The most representative gene, KCNMA1, was selected for in vitro and animal experiments to verify its association with prostate cancer.
Results:
Based on quantitative lactylation proteomics, two LA clusters were identified in prostate cancer and were significantly associated with prognosis. A total of 122 DEGs were screened to construct a gene risk model. The K-M curves verified the differences between the high - and low - risk groups of the model in the test group and the training cohort (test group: P = 0.025; training group: P < 0.001). The ROC curve verified that the prognostic model had good accuracy. The nomogram integrating staging and LA risk factors showed high accuracy and reliability in predicting the prognosis of prostate cancer. The expression of KCNMA1 in PCa was significantly lower than that in NATs, and its expression level decreased with the increase in grading. In cell experiments, overexpression of KCNMA1 promoted the infiltration of M1 macrophages by inhibiting the RAS/RAF/MEK/ERK signaling pathway, thereby inhibiting the proliferation, migration, and invasion of prostate cancer cells. Animal experiments demonstrated that overexpression of KCNMA1 inhibited the growth rate of tumors.
Conclusion:
The LA risk model constructed in this study can effectively predict the prognosis of prostate cancer and is expected to become a new type of test scoring criterion. KCNMA1 is expected to become a novel target for prostate cancer.
