Brain metastasis (BM) is common in patients with non-small cell lung cancer (NSCLC) and is associated with a poor prognosis. Ceramide synthase 1 (CERS1) participates in malignancy development, but its potential role in NSCLC BM remains unclear. This study aimed to explore the physiological effects and molecular mechanism of CERS1 in NSCLC BM. CERS1 expression was evaluated in NSCLC tissues and cell lines, and its physiological roles were subsequently explored in vivo and in vitro. Mass spectrometry and co-immunoprecipitation were performed to explore CERS1-interacting proteins. The associated signaling pathways of CERS1 in NSCLC BM were further investigated using bioinformatics analysis and molecular biotechnology. We demonstrated that CERS1 was significantly downregulated in NSCLC cell lines and BM tissues, and its upregulation was associated with better prognoses. In vitro, CERS1 overexpression inhibited cell migration, invasion, and the ability to penetrate the blood-brain barrier. Moreover, CERS1 interacted with ubiquitin-specific protease 14 (USP14) and inhibited BM progression by downregulating the PI3K/AKT/mTOR signaling pathway. Further, CERS1 expression substantially suppressed BM tumor formation in vivo. This study demonstrated that CERS1 plays a suppressor role in NSCLC BM by interacting with USP14 and downregulating the PI3K/AKT/mTOR signaling pathway, thereby serving as a novel therapeutic target for NSCLC BM.

Lung adenocarcinoma (LUAD) is a major challenge in oncology due to its complex molecular structure and generally poor prognosis. The aim of this study was to find diagnostic markers and therapeutic targets for LUAD by integrating differential gene expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning methods. Differentially expressed genes (DEGs) were identified through the analysis of gene expression data from the Gene Expression Omnibus (GEO) database. Ten of the gene co-expression modules constructed by WGCNA were identified, with the red module having the most significant correlation with clinical features. In addition, a machine learning model constructed based on Stepglm[backward] with the random forest algorithm achieved the highest C-index (0.999) and screened eight core genes, among which ST14 was noted for its excellent predictive ability. Single-cell RNA sequencing of the LUAD samples further analyzed the expression patterns of these genes in 29 cellular subtypes, revealing their significant association with immune cell infiltration. Of particular note, the association of ST14 with clinical prognosis, drug responsiveness, and immune infiltration was validated, while enrichment analysis further clarified its role in key biological pathways. Ultimately, the expression of the core genes was validated experimentally. This study provides new insights into the pathogenesis of LUAD, clarifies potential diagnostic markers and therapeutic targets, and provides an important basis for future clinical interventions.