Bulk and single cell RNA sequencing data reveal lactylation-related gene signatures for prognosis and immunity in cervical cancer.

Authors

Jing Song, Yutian Zhao, Chunlin Dong, Xiaowei Qi, Qu Guo, Yongju Zhuang, Chunqing Yu, Ruofan Dong

Published in

Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas. Volume 59. Pages e15368. Epub Aug 21, 2026.

Abstract

Previous studies indicate that lactylation, a post-translational modification, may play an important role in the progression of cervical cancer (CC). However, the comprehensive roles of lactylation in influencing the tumor microenvironment, immune landscape, and prognosis of CC have yet to be fully elucidated. The bulk RNA-seq and single-cell RNA-seq datasets of CC patients were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. A total of 630 genes associated with lactylation activity were identified using AUCell algorithm, differential expression, and correlation analyses. Subsequently, a prognostic risk model comprising 17 genes was constructed through univariate Cox and LASSO analyses, which accurately predicted the prognosis of CC patients and was validated in an independent dataset. The nomogram, including risk scores and N staging, outperformed other clinical parameters. The high-risk group was positively related to the glycolysis pathway. Furthermore, bioinformatics analyses further indicated that the high-risk group was associated with a poorer prognosis, pro-tumorigenic pathways, and immunosuppression, and was sensitive to ulixertinib, dasatinib, nutlin-3a, and trametinib. Mendelian randomization analysis suggested that ITGA5 may be causally associated with an increased risk of CC, with caution in causal inference. Additionally, the expressions of several risk genes were validated using real-time qPCR on tissue samples from six CC patients. In conclusion, the lactylation-related gene risk model could accurately and independently predict the prognosis of CC patients, providing insights into therapeutic strategies for CC patients. These bioinformatics-based findings are exploratory and warrant further validation in preclinical and clinical settings.

PMID:
42659456
Bibliographic data and abstract were imported from PubMed on 28 Aug 2026.

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