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An externally validated lactylation-associated prognostic signature for overall survival prediction in lung adenocarcinoma identifies TUBA1C as a candidate gene

   
@article{TCR120231,
	author = {Zhi Wang and Nuo Yan and Taohui Ding and Wenxun Xiong and Weiqiang Feng and Yunzhe Wang and Yiping Wei},
	title = {An externally validated lactylation-associated prognostic signature for overall survival prediction in lung adenocarcinoma identifies TUBA1C as a candidate gene},
	journal = {Translational Cancer Research},
	volume = {15},
	number = {7},
	year = {2026},
	keywords = {},
	abstract = {Background: Lung adenocarcinoma (LUAD) is characterized by marked prognostic heterogeneity. Although lactylation has been implicated in tumor progression and immune regulation, the clinical relevance of lactylation-related transcriptional programs in LUAD remains insufficiently defined. This study aimed to develop and externally validate a lactylation-related gene signature for overall survival prediction in LUAD and to prioritize candidate genes for further biological investigation.Methods: We conducted a retrospective prediction model development and external validation study by integrating single-cell and bulk transcriptomic data. The Cancer Genome Atlas (TCGA)-LUAD was used as the model development cohort, whereas GSE31210 and GSE72094 were used as independent external validation cohorts, including 503, 226, and 398 patients, respectively. Overall survival was defined as the primary outcome. An optimized lactylation-related gene signature (LRGS) was constructed using machine learning strategies, and its predictive performance was evaluated using the concordance index, Kaplan-Meier survival analysis, and time-dependent receiver operating characteristic (ROC) analysis. In addition, pathway enrichment, tumor microenvironment, genomic alteration, and intercellular communication analyses were performed. Immunohistochemistry and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were further used to validate TUBA1C expression in LUAD tissues and cell lines.Results: The optimal model [StepCox (forward) + random survival forest (RSF)] achieved C-index values of 0.935, 0.668, and 0.637 in the TCGA-LUAD, GSE31210, and GSE72094 cohorts, respectively. The final LRGS consisted of 15 genes and stratified patients into high- and low-risk groups with significantly different overall survival across all cohorts (all P},
	issn = {2219-6803},	url = {https://tcr.amegroups.org/article/view/120231}
}