FOXP4-AS1 promotes CD8+ T cell exhaustion and esophageal cancer immune escape through USP10-stabilized PD-L1.

Esophageal cancer (EC) is the 9th most frequently diagnosed malignancy globally with unfavorable prognosis. Immune escape is one of the principal factors leading to poor survival, however, the mechanism underlying immune escape remains largely uninvestigated. The xenograft mouse model and EC cell-CD8+ cytotoxic T lymphocytes (CTLs) co-culture system were established. Immunohistochemistry, qRT-PCR or western blot were employed to detect the levels of long non-coding RNA (lncRNA) FOXP4-AS1, PD-L1, USP10 and other molecules. The abundance of T cells, cytokine production and cell apoptosis were monitored by flow cytometry. The viability of CTLs was assessed by Trypan blue staining. The binding between FOXP4-AS1 and USP10 was validated by RNA pull-down assay, and the interaction between USP10 and PD-L1, as well as the ubiquitination of PD-L1, were detected by co-immunoprecipitation. The elevation of FOXP4-AS1 in EC was associated with decreased CTL abundance, and upregulated PD-L1 facilitated CTL apoptosis in EC. FOXP4-AS1 accelerated EC tumor growth by decreasing the abundance of tumor infiltrating CTLs in vivo. FOXP4-AS1 inhibited the viability of CTLs and facilitated the cytotoxicity and exhaustion of CTLs. In Kyse 450 cell-CTL co-culture system, FOXP4-AS1 suppressed the viability and abundance of CTLs, and inhibited EC cell apoptosis via PD-L1. Mechanistically, FOXP4-AS1 regulated the ubiquitination of PD-L1 through deubiquitinating enzyme USP10. FOXP4-AS1 promoted CTL exhaustion and EC immune escape through USP10-stabilized PD-L1. HIGHLIGHTS: PD-L1 facilitated CD8+ T cell apoptosis in EC. Upregulated FOXP4-AS1 promoted EC tumor growth by inhibiting the viability and facilitating the cytotoxicity and exhaustion of tumor infiltrating CD8+ T cells. FOXP4-AS1 suppressed the viability and abundance of CD8+ T cells through USP10-mediated deubiquitination of PD-L1.

High SNHG expression may predict a poor lung cancer prognosis based on a meta-analysis.

BACKGROUND: An increasing number of small nucleolar RNA host genes (SNHGs) have been revealed to be dysregulated in lung cancer tissues, and abnormal expression of SNHGs is significantly correlated with the prognosis of lung cancer. The purpose of this study was to conduct a meta-analysis to explore the correlation between the expression level of SNHGs and the prognosis of lung cancer. METHODS: A comprehensive search of six related databases was conducted to obtain relevant literature. Relevant information, such as overall survival (OS), progression-free survival (PFS), TNM stage, lymph node metastasis (LNM), and tumor size, was extracted. Hazard ratios (HRs) and 95% confidence intervals (CIs) were pooled to evaluate the relationship between SNHG expression and the survival outcome of lung cancers. Sensitivity and publication bias analyses were performed to explore the stability and reliability of the overall results. RESULTS: Forty publications involving 2205 lung cancer patients were included in this meta-analysis. The pooled HR and 95% CI values indicated a significant positive association between high SNHG expression and poor OS (HR: 1.890, 95% CI: 1.595-2.185), disease-free survival (DFS) (HR: 2.31, 95% CI: 1.57-3.39) and progression-free survival (PFS) (HR: 2.01, 95% CI: 0.66-6.07). The pooled odds ratio (OR) and 95% CI values indicated that increased SNHG expression may be correlated with advanced TNM stage (OR: 1.509, 95% CI: 1.267-1.799), increase risk of distant lymph node metastasis (OR: 1.540, 95% CI: 1.298-1.828), and large tumor size (OR: 1.509, 95% CI: 1.245-1.829). Sensitivity analysis and publication bias results showed that each result had strong reliability and robustness, and there was no significant publication bias or other bias. CONCLUSION: Most SNHGs are upregulated in lung cancer tissues, and high expression of SNHGs predicts poor survival outcomes in lung cancer. SNHGs may be potential prognostic markers and promising therapeutic targets.

Identification of a Prognostic Gene Signature Based on Lipid Metabolism-Related Genes in Esophageal Squamous Cell Carcinoma.

Background: Dysregulation of lipid metabolism is common in cancer. However, the molecular mechanism underlying lipid metabolism in esophageal squamous cell carcinoma (ESCC) and its effect on patient prognosis are not well understood. The objective of our study was to construct a lipid metabolism-related prognostic model to improve prognosis prediction in ESCC. Methods: We downloaded the mRNA expression profiles and corresponding survival data of patients with ESCC from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. We performed differential expression analysis to identify differentially expressed lipid metabolism-related genes (DELMGs). We used Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses to establish a risk model in the GEO cohort and used data of patients with ESCC from the TCGA cohort for validation. We also explored the relationship between the risk model and the immune microenvironment via infiltrated immune cells and immune checkpoints. Results: The result showed that 132 unique DELMGs distinguished patients with ESCC from the controls. We identified four genes (ACAA1, ACOT11, B4GALNT1, and DDHD1) as prognostic gene expression signatures to construct a risk model. Patients were classified into high- and low-risk groups as per the signature-based risk score. We used the receiver operating characteristic (ROC) curve and the Kaplan-Meier (KM) survival analysis to validate the predictive performance of the 4-gene signature in both the training and validation sets. Infiltrated immune cells and immune checkpoints indicated a difference in the immune status between the two risk groups. Conclusion: The results of our study indicated that a prognostic model based on the 4-gene signature related to lipid metabolism was useful for the prediction of prognosis in patients with ESCC.