FOXA1/UBE2T Inhibits CD8+T Cell Activity by Inducing Mediates Glycolysis in Lung Adenocarcinoma.

BACKGROUND: Immune escape is a key factor influencing survival rate of lung adenocarcinoma (LUAD) patients, but molecular mechanism of ubiquitin binding enzyme E2T (UBE2T) affecting immune escape of LUAD remains unclear. The objective was to probe role of UBE2T in LUAD. METHODS: Bioinformatics means were adopted for analyzing UBE2T and forkhead box A1 (FOXA1) expression in LUAD tissues, the gene binding sites, the pathway UBE2T regulates, and the correlation between UBE2T and glycolysis genes. Dual luciferase and chromatin immunoprecipitation (ChIP) assays were conducted for validating the binding relationship between the two genes. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blot were employed to evaluate UBE2T, FOXA1, and programmed death ligand 1 (PD-L1) levels in cancer cells. MTT assay was conducted for detecting cell viability. Cytotoxicity assay detected CD8+T cell toxicity. Cytokine expression was assayed by enzyme linked immunosorbent assay (ELISA). Extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) were assayed by extracellular flow analyzer. Glycolytic gene expression was analyzed by qRT-PCR, and glycolysis-related indicators were detected by ELISA. Immunohistochemistry (IHC) detected CD8+T cell infiltration in tumor tissues. RESULTS: FOXA1 and UBE2T were up-regulated in LUAD, and a binding site existed between UBE2T and FOXA1. Overexpressing UBE2T could increase PD-L1 expression and inhibit toxicity of CD8+T cells to LUAD cells. Overexpressing UBE2T repressed CD8+T cell activity in LUAD by activating the glycolysis pathway, and the addition of glycolysis inhibitor 2-deoxy-d-glucose (2-DG) reversed the above results. Mechanistically, FOXA1 promoted the immune escape of LUAD by up-regulating UBE2T and thus mediating glycolysis. In vivo experiments revealed that UBE2T knockdown hindered tumor growth, inhibited PD-L1 expression, and facilitated CD8+T cell infiltration. CONCLUSION: FOXA1 up-regulated the expression of UBE2T, which activated glycolysis, and thus inhibited activity of CD8+T cells, causing immune escape of LUAD.

Aumolertinib: effective treatment for asymptomatic pulmonary giant cell carcinoma with EGFR L858R mutation - a case report.

Aumolertinib, as a novel third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), has been widely employed as a first-line treatment for advanced non-small cell lung cancer (NSCLC) patients with EGFR mutation. However, reports regarding the benefit of using aumolertinib as a monotherapy in pulmonary giant cell carcinoma are relatively scarce. In this report, we present a pulmonary giant cell carcinoma case harboring the EGFR Leu858Arg (L858R) mutation, with the patient at stage cT2bN3M1c IVB. Through the use of autolearning as a single agent, we effectively controlled the progression of pulmonary giant cell carcinoma, achieving a 6-month progression-free survival during the treatment course. Notably, the patient's tumor not only ceased its growth but also continued to shrink, highlighting a significant therapeutic effect. This case reveals the effectiveness of aumolertinib as a monotherapy in controlling disease progression. The finding underscores the therapeutic advantage of aumolertinib in this particular subgroup of patients, offering a novel treatment option for pulmonary giant cell carcinoma.

Construction of a prognostic model for lung adenocarcinoma based on membrane-tension-related genes.

Background: Lung adenocarcinoma (LUAD), which is the most common type of non-small cell lung cancer (NSCLC), is one of the most aggressive and fatal tumors. Therefore, the identification of key biomarkers affecting prognosis is important to improving the prognosis of patients with LUAD. Cell membranes have long been understood; however, few studies have focused on the role of membrane tension in LUAD. The present study aimed to construct a prognostic model associated with membrane-tension-related genes (MRGs) and explore its prognostic value in LUAD patients. Methods: RNA sequencing data and the corresponding clinical characteristics data of LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Five membrane-tension prognosis-related genes (5-MRG) were screened by univariate and multifactorial COX regression and least absolute shrinkage and selection operator (LASSO) regression analyses. The data were then divided into testing, training, and all groups to build a prognostic model, and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), copy number variations (CNV), tumor mutation burden (TMB), and tumor microenvironment (TME) analyses were performed to explore the potential mechanisms of MRGs. Finally, single-cell data from the GSE200972 dataset in the Gene Expression Omnibus (GEO) database were obtained to determine the distribution of prognostic MRGs. Results: Construction and validation of the prognostic risk models were conducted using 5-MRG in the trial, test, and all data sets. Patients in the low-risk group had a better prognosis than those in the high-risk group, and the Kaplan-Meier survival curve and receiver operating characteristic curve (ROC) confirmed that the model had a better predictive value for LUAD patients. GO and KEGG analyses of differential genes in the high- and low-risk groups were significantly enriched in immune-related pathways. Immune checkpoint (ICP) differential genes differed significantly in the high- and low-risk groups. By analyzing the single-cell sequencing data, the cells were divided into nine subpopulations and cell subpopulation localization through 5-MRG. Conclusions: The results of this study suggest that a prognostic model based on prognosis-associated MRGs can be used to predict the prognosis of LUAD patients. Therefore, prognosis-related MRGs could be potential prognostic biomarkers and therapeutic targets.

Construction of a prognostic model for lung squamous cell carcinoma based on immune-related genes.

Lung squamous cell carcinoma (LUSC) lacks appropriate prognostic and diagnostic strategies. Available studies suggest the effectiveness of immunotherapy for LUSC, but effective molecular markers are still insufficient. We obtained mRNA expression and clinical information of LUSC samples from The Cancer Genome Atlas (TCGA) database. Enrichment levels of immune-related genes were revealed by single sample gene set enrichment analysis. Then, differentially expressed genes (DEGs) related to immunity were obtained by differential analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. In addition, Cox regression analysis combined with LASSO method was utilized to identify immune-related prognostic genes, and an immune-related prognostic model was constructed. Kaplan-Meier and receiver operating characteristic (ROC) curves were drawn to verify the accuracy of the model. Finally, a nomogram and calibration curve were drawn to predict LUSC patients' survival. Samples were assigned into high-, medium- and low-immune groups. Compared with low- and medium-immune groups, high-immune group enriched more immune cells, with higher immune infiltration degree, and higher expression of immune checkpoints and human leukocyte antigen. DEGs were enriched in biological processes and signaling pathways related to immunity. Eleven genes (ONECUT3, MAGED4, SULT2A1, HPR, S100A5, IRS4, DPP6, FGF8, TEX38, PLAAT1 and CLEC3A) were obtained to construct an immune-related prognostic model. Riskscore served as an independent prognostic factor. Besides, the nomogram prediction model could predict disease progression in LUSC patients. The constructed risk assessment model for LUSC immune-related genes could assess LUSC patients' prognoses with great efficacy, providing guidance for the clinical treatment of LUSC.

Systematic analysis of apoptosis-related genes in the prognosis of lung squamous cell carcinoma: a combined single-cell RNA sequencing study.

Background: Lung squamous cell carcinoma (LUSC) has a poor prognosis and lacks appropriate diagnostic and treatment strategies. Apoptosis dysregulation is associated with tumor occurrence and drug resistance, but the prognostic value of apoptosis-related genes (ARGs) in LUSC remains unclear. Methods: Using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analysis based on differentially expressed ARGs, we constructed an ARG-related prognostic model for LUSC survival rates. We conducted correlation analysis of prognostic ARGs by incorporating the dataset of normal lung tissue from the Genotype-Tissue Expression (GTEx) database. We then constructed a risk model, and the predictive ability of the model was evaluated using receiver operating characteristic (ROC) curve analysis. Non-small cell lung cancer (NSCLC) single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, these data were subjected to single-cell analysis. Cell subgroups were determined and annotated by dimensionality reduction clustering, and the cell subgroups in disease development were identified via pseudotemporal analysis with the Monocle 2 algorithm. Results: We identified four significantly prognostic ARGs and constructed a stable prognostic risk model. Kaplan-Meier curve analysis showed that the high-risk group had a poorer prognosis (P<0.05). Furthermore, the ROC analysis of 3-, 5- and 7-year survival rates confirmed that the model had good predictive value for patients with LUSC. Single-cell RNA sequencing showed the prognostic ARGS were enriched in epithelial cells, smooth muscle cells, and T cells. Pseudotime analysis was used to infer the differentiation process and time sequence of cells. Conclusions: This study identified ARGs that are associated with prognosis in LUSC, and a risk model based on these prognostic genes was constructed that could accurately predict the prognosis of LUSC. Single-cell sequencing analysis provided new insights into the cellular-level development of tumors. These findings provide more guidance for the diagnosis and treatment of patients with LUSC.