Purpose
Persistent inflammation and epithelial-mesenchymal transition are essential pathophysiological processes in chronic obstructive pulmonary disease (COPD) and involve airway remodeling. m6A methylation modification was discovered to play an important role in various diseases. Nevertheless, the regulatory role of m6A methylation has not yet been investigated in cigarette smoking-induced COPD. The study aims to explore the regulatory role of m6A methylation in cigarette smoking-induced COPD.
Patients and Methods
In this study, two Gene Expression Omnibus (GEO) datasets were first utilized to analyze the expression profiles of m6A RNA methylation regulators in COPD. We then established a cell model of COPD by exposing human bronchial epithelial cells (HBECs) to cigarette smoke extract (CSE) in vitro and detected the expression of m6A writer Mettl3 and EMT phenotype markers. RNA interference, cycloleucine, RT-qPCR, western blot, MeRIP-sequencing, and cell migration assay were performed to investigate the potential effect of Mettl3 on the EMT process in CSE-induced HBECs.
Results
Our results showed that Mettl3 expression was significantly elevated in cigarette smoking-induced COPD patients and in a cellular model of COPD. Furthermore, Mettl3 silence and cycloleucine treatment inhibited the EMT process of HBECs caused by CSE. Mechanically, Mettl3 silence weakens the m6A methylation of SOCS3 mRNA to enhance the protein expression of SOCS3, inhibiting CSE-induced SOCS3/STAT3/SNAI1 signaling and EMT processes in HBECs.
Conclusion
Our study inferred that Mettl3-mediated m6A RNA methylation modification modulates CSE-induced EMT by targeting SOCS3 mRNA and ultimately serves as a crucial regulator in the emergence of COPD. This conclusion reinforces the regulatory role of m6A methylation in COPD.
(1) Background: Gene-expression data usually contain missing values (MVs). Numerous methods focused on how to estimate MVs have been proposed in the past few years. Recent studies show that those imputation algorithms made little difference in classification. Thus, some scholars believe that how to select the informative genes for downstream classification is more important than how to impute MVs. However, most feature-selection (FS) algorithms need beforehand imputation, and the impact of beforehand MV imputation on downstream FS performance is seldom considered. (2) Method: A modified chi-square test-based FS is introduced for gene-expression data. To deal with the challenge of a small sample size of gene-expression data, a heuristic method called recursive element aggregation is proposed in this study. Our approach can directly handle incomplete data without any imputation methods or missing-data assumptions. The most informative genes can be selected through a threshold. After that, the best-first search strategy is utilized to find optimal feature subsets for classification. (3) Results: We compare our method with several FS algorithms. Evaluation is performed on twelve original incomplete cancer gene-expression datasets. We demonstrate that MV imputation on an incomplete dataset impacts subsequent FS in terms of classification tasks. Through directly conducting FS on incomplete data, our method can avoid potential disturbances on subsequent FS procedures caused by MV imputation. An experiment on small, round blue cell tumor (SRBCT) dataset showed that our method found additional genes besides many common genes with the two compared existing methods.
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