N6-methyladenosine (m6A) is an abundant eukaryotic mRNA modification involved in regulating the formation and metastasis of nonsmall cell lung cancer (NSCLC). We collected clinical NSCLC tissue and paracarcinoma tissue. Then methyltransferase-like 14 (METTL14), pleomorphic adenoma gene like-2 (PLAGL2), and β-catenin expressions were assessed using quantitative real-time PCR and western blot. PLAGL2, and β-catenin (nuclear) expressions were increased in NSCLC tissues. Cell proliferation, migration, invasion, and death were examined. PLAGL2 could activate β-catenin signaling to affect cell proliferation and migration abilities. RNA immunoprecipitation assay was operated to identify m6A modification levels of PLAGL2 after knockdown and overexpression of METTL14. PLAGL2 was regulated by METTL14-mediated m6A modification. Knockdown of METTL14 repressed cell proliferation, migration, and invasion, and promoted cell death. Interestingly, these effects were reversed when PLAGL2 was overexpressed. Finally, tumor formation in nude mice was performed to verify the role of the METTL14/PLAGL2/β-catenin signaling axis. Tumor formation in nude mice demonstrated METTL14/PLAGL2/β-catenin axis promoted NSCLC development in vivo. In brief, METTL14 promoted NSCLC development by increasing m6A methylation of PLAGL2 to activate β-catenin signaling. Our research provided essential clues for in-depth comprehension of the mechanism of NSCLC occurrence and development and also provided the basis for NSCLC treatment.

Inhibiting cholesterol metabolism has shown great potential in non-small cell lung cancer (NSCLC). However, the regulatory mechanism of the lipid metabolism key factor Sect. 14-like lipid binding 2 (SEC14L2) in NSCLC remains unclear. This study investigates the effects of differentially expressed genes related to cholesterol metabolism on the development of NSCLC. Cox regression and survival analysis were performed to screen cholesterol metabolism-related genes and predict survival prognosis in NSCLC patients. The proliferation and migration of NSCLC cells were assessed by CCK-8, EdU, colony formation and wound-healing assay. Cholesterol depletion and rescue trials were used to evaluate the effect of SEC14L2 on cholesterol transport in NSCLC cells. IF and Co-IP were used to analyze the targeting relationship between SEC14L2 and scavenger receptor class B member 1 (SCARB1). SEC14L2 was a key gene related to prognosis in NSCLC patients and was highly expressed in A549 and Calu-1 cells. Subsequent studies demonstrated that knockdown of SEC14L2 significantly reduced the proliferation and migration of NSCLC cells, resulting in inhibited tumor growth. Furthermore, both in vitro and in vivo experiments indicated that SEC14L2 regulated cholesterol uptake. Silencing SEC14L2 partially counteracted the promotion of cholesterol content by MβCD-chol in A549 and Calu-1 cells. We then verified that there was a protein interaction between SEC14L2 and SCARB1. SEC14L2 promoted cholesterol uptake in NSCLC cells by up-regulating SCARB1 expression, thereby promoting NSCLC development.

Lung cancer is the most common cause of cancer deaths worldwide, and lung adenocarcinoma (LUAD) is the most common histological subtype. However, the prognostic and predictive outcomes differ because of the heterogeneity of programmed cell death. The purpose of this work is to investigate and develop a cuproptosis-associated lncRNA-based LUAD prediction marker. We firstly performed bioinformatic analysis of the Cuprotosis database and The Cancer Genome Atlas (TCGA) database to obtain 19 cuprotosis-related gene datasets and transcriptional data for LUAD. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis were utilized to construct cuproptosis-associated lncRNA modes. LUAD patients were thus classified into high-risk and low-risk categories based on prognostic risk values, with a median of It acted as a boundary. Risk models were evaluated and validated using Kaplan-Meier analysis, principal component analysis (PCA), gene set enrichment analysis (GSEA) and nomograms. Utilizing the TCGA-LUAD dataset, we identified seven predicted cuproptosis-associated lncRNAs in tumor microenvironment to create the risk model. 95.54% (214/224) of high-risk category tumor samples included cuproptosis-associated gene alterations, compared to 85.65% (203/237) of low-risk category tumor samples, with TP53 accounting for the bulk of occurrences. According to these findings, risk value was superior to other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival (OS). The predictive validity of the cuproptosis-associated lncRNA-based risk model for LUAD is high, and this may have implications for how lung cancer patients are treated individually.