Xuan Zheng

The lactylation modification has been implicated in several cancer types; however, the role of lactylation modification-related genes in esophageal carcinoma (EC) remains underexplored. Utilizing a set of 16 lactylation modification-related genes, cohorts of patients with EC were stratified into two distinct clusters, characterized by significant disparities in both survival outcomes and the immune microenvironment. An extensive bioinformatics analysis unveiled 382 differentially expressed genes (DEGs) between these two clusters. A subsequent univariate Cox regression analysis identified 24 DEGs specifically associated with lactylation, forming the basis of a constructed lactylation-related score. The resultant lactylation-related score exhibited notable predictive efficacy for survival and other clinicopathological traits, which was validated through calibration curves, Kaplan–Meier survival curves and the Wilcoxon test. Moreover, the lactylation-related score displayed a close correlation with immune cell infiltration in EC. Notable differential expressions of immune checkpoints and regulators were observed between groups stratified by low and high lactylation scores, with the latter exhibiting a more favorable response to anti-PD-1/PD-L1 therapy. Furthermore, the expression profile of U2 snRNP associated SURP domain containing (U2SURP), a constituent of the lactylation-related score, underwent both ex vivo and in vitro validation. The expression of U2SURP was significantly associated with lactylation levels, histological grade and tumor stage. Notably, knockdown of U2SURP expression inhibited the lactylation levels, immune genes IL-1A and IL-1B, proliferation, migration and invasion of EC cells. In conclusion, the lactylation-related score developed in the present study showed promise in predicting the prognosis and immunotherapeutic responses among patients with EC. Moreover, the identification of U2SUPR as a novel oncogene in EC suggests its potential as a prospective therapeutic target for EC treatment.

Esophageal squamous cell cancer (ESCC) is an aggressive disease associated with a poor prognosis. Long non-coding RNAs (lncRNAs) and oxidative stress play crucial roles in tumor progression. We aimed to identify an oxidative stress-related lncRNA signature that could predict the prognosis in ESCC. In the GSE53625 dataset, we identified 332 differentially expressed lncRNAs (DElncRNAs) between ESCC and control samples, out of which 174 were oxidative stress-related DElncRNAs. Subsequently, seven oxidative stress-related DElncRNAs (CCR5AS, LINC01749, PCDH9-AS1, TMEM220-AS1, KCNMA1-AS1, SNHG1, LINC01672) were selected based on univariate and LASSO Cox to build a prognostic risk model, and their expression was detected by RT-qPCR. The model exhibited an excellent ability for the prediction of overall survival (OS) and other clinicopathological traits using Kaplan–Meier (K-M) survival curves, receiver operating characteristic (ROC) curves, and the Wilcoxon test. Additionally, analysis of infiltrated immune cells and immune checkpoints indicated differences in immune status between the two risk groups. Finally, the in vitro experiments showed that PCDH9-AS1 overexpression inhibited proliferation ability and promoted apoptosis and oxidative stress levels in ESCC cells. In conclusion, our study demonstrated that a novel oxidative stress-related DElncRNA prognostic model performed favorably in predicting ESCC patient prognosis and benefits personalized clinical applications.