Machine learning-based prognostic model of lactylation-related genes for predicting prognosis and immune infiltration in patients with lung adenocarcinoma

作者：Mingjun Gao, Mengmeng Wang, Siding Zhou, Jiaqi Hou, Wenbo He, Yusheng Shu, Xiaolin Wang · 发表于：Cancer Cell International · 年份：2024 · DOI：10.1186/s12935-024-03592-y · 被引用次数：31 · 研究领域：Epigenetics and DNA Methylation、Ferroptosis and cancer prognosis、Immune cells in cancer

BACKGROUND: Histone lactylation is a novel epigenetic modification that is involved in a variety of critical biological regulations. However, the role of lactylation-related genes in lung adenocarcinoma has yet to be investigated. METHODS: RNA-seq data and clinical information of LUAD were downloaded from TCGA and GEO datasets. Unsupervised consistent cluster analysis was performed to identify differentially expressed genes (DEGs) between the two clusters, and risk prediction models were constructed by Cox regression analysis and LASSO analysis. Kaplan-Meier (KM) survival analysis, ROC curves and nomograms were used to validate the accuracy of the models. We also explored the differences in risk scores in terms of immune cell infiltration, immune cell function, TMB, TIDE, and anticancer drug sensitivity. In addition, single-cell clustering and trajectory analysis were performed to further understand the significance of lactylation-related genes. We further analyzed lactate content and glucose uptake in lung adenocarcinoma cells and tissues. Changes in LUAD cell function after knockdown of lactate dehydrogenase (LDHA) by CCK-8, colony formation and transwell assays. Finally, we analyzed the expression of KRT81 in LUAD tissues and cell lines using qRT-PCR, WB, and IHC. Changes in KRT81 function in LUAD cells were detected by CCK-8, colony formation, wound healing, transwell, and flow cytometry. A nude mouse xenograft model and a KrasLSL-G12D in situ lung adenocarcinoma mouse mo...