Knockdown of lncRNA NUTM2A‑AS1 inhibits lung adenocarcinoma cell viability by regulating the miR‑590‑5p/METTL3 axis

Abstract

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related mortality worldwide. Long non-coding RNA (lncRNA) NUT family member 2A antisense RNA 1 (NUTM2A-AS1) is dysregulated in LUAD; however, its role in this disease remains unclear. The present study aimed to identify the underlying molecular mechanism of the effect of lncRNA NUTM2A-AS1 in LUAD by exploring whether lncRNA NUTM2A-AS1 could affect LUAD cell proliferation and apoptosis through the microRNA (miR)-590-5p/methyltransferase 3, N6-adenosine-methyltransferase complex catalytic subunit (METTL3) axis. miR-590-5p was predicted and verified as the direct target of NUTM2A-AS1 using bioinformatics analysis and a dual luciferase reporter assay. The expression levels of NUTM2A-AS1 and miR-590-5p in lung cancer cells, and the effects of NUTM2A-AS1 on cell viability and apoptosis were determined using MTT assays and flow cytometry, respectively. Reverse transcription-quantitative PCR analysis revealed that the expression levels of NUTM2A-AS1 were significantly upregulated, while those of miR-590-5p were significantly downregulated, in lung cancer cells compared with the control epithelial cells. NUTM2A-AS1 knockdown inhibited NCI-H23 cell viability and induced apoptosis by upregulating miR-590-5p expression. Moreover, the function and regulatory mechanism of miR-590-5p in LUAD were also investigated. It was determined that miR-590-5p could interact with METTL3, and further analysis of the expression levels of METTL3 in lung cancer cells demonstrated that METTL3 was significantly upregulated in NCI-H23 and A549 cells compared with the control cells. In addition, miR-590-5p inhibited NCI-H23 cell viability and induced apoptosis by downregulating METTL3 expression. In conclusion, the findings of the present study suggested that NUTM2A-AS1 knockdown may inhibit LUAD progression by regulating the miR-590-5p/METTL3 axis. These results may provide insight into the mechanisms underlying the tumorigenesis of LUAD and offer a new treatment strategy for the disease.

CTAT-LR-fusion: accurate fusion transcript identification from long and short read isoform sequencing at bulk or single cell resolution

“…Approximately 60% of the NUTM2A-AS1::RP11-203L2.4 containing tumor cells were solely identified by long read evidence, another 20% by short reads only, and the remaining 20% by both short and long reads ( Figure 5b ). Interestingly, fusion gene partner NUTM2A-AS1 has recently been identified as an oncogene with roles in multiple cancer types (Wang et al 2020; Wang et al 2021; Long et al 2023). The long fusion reads appear to be largely full-length and yield evidence for eight different fusion splicing isoforms, mostly involving skipping of alternative exons and one isoform involving an alternative terminal exon ( Figure 5c ).…”

Using integrated analysis from multicentre studies to identify RNA methylation-related lncRNA risk stratification systems for glioma

“…In a hypoxic tumour microenvironment, it eventually protects gastric cancer cells from ferroptosis [ 56 ]. Knockdown of lncRNA NUTM2A-AS1 suppresses lung adenocarcinoma cell viability and induces apoptosis through the miR-590-5p/METTL3 axis [ 57 ]. Overall, numerous experimental findings indicate that lncRNAs regulated by RNA methylation play a critical role in bringing about, developing, and metastasizing cancerous tumours.…”

An m6A/m5C/m1A/m7G-Related Long Non-coding RNA Signature to Predict Prognosis and Immune Features of Glioma

“…Conversely, lncRNA can also affect the function of genes related to RNA methylation modification. For example, knockdown of Nutm2a-as1 can regulate Mettl3 to inhibit lung cancer progression ( Wang J. et al, 2021 ). Song et al used a systems approach to integrate gene expression and patient survival data with protein interaction networks in discrete windows of tumor proliferative biology and demonstrated that, in combination, these networks may form the basis of highly accurate prognostic classification models, with potential clinical utility for guiding therapeutic options for patients ( Song et al, 2015 ).…”