Inhibition of NNMT enhances drug sensitivity in lung cancer cells through mediation of autophagy

Jian Wang; Ming Zhang; Xin You; Yang Xu; Congcong Zhang; Ying Li; Chunhui Yang; Qi Wang

doi:10.3389/fphar.2024.1415310

Inhibition of NNMT enhances drug sensitivity in lung cancer cells through mediation of autophagy

Front Pharmacol. 2024 Jul 5:15:1415310. doi: 10.3389/fphar.2024.1415310. eCollection 2024.

Authors

Jian Wang^{1

2

3}, Ming Zhang^{1

2}, Xin You^{1

2}, Yang Xu^{1

2}, Congcong Zhang^{1

2}, Ying Li^{1

2}, Chunhui Yang^{1

2

4}, Qi Wang^{1

2}

Affiliations

¹ Translational Research Center for Lung Cancer, The Second Hospital, Dalian Medical University, Dalian, Liaoning, China.
² Department of Respiratory Medicine, The Second Hospital, Dalian Medical University, Dalian, Liaoning, China.
³ Department of Respiratory Medicine, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
⁴ Department of Clinical Laboratory, The Second Hospital, Dalian Medical University, Dalian, Liaoning, China.

Abstract

Introduction: This study aimed to investigate the role of Nicotinamide N-methyltransferase (NNMT) in the drug sensitivity of non-small cell lung cancer (NSCLC) cells, with a focus on its impact on autophagy and resistance to the chemotherapeutic agent osimertinib. The study hypothesized that NNMT knockdown would enhance drug sensitivity by modifying autophagic processes, providing a potential new therapeutic target for overcoming chemoresistance in lung cancer.

Methods: Proteomic analysis was utilized to identify changes in protein expression following NNMT knockdown in H1975 and H1975 osimertinib resistance (H1975OR) lung cancer cell lines. Gene expression patterns and their correlation with NNMT expression in lung cancer patients were analyzed using The Cancer Genome Atlas (TCGA) dataset. Additionally, a predictive model for lung cancer survival was developed via lasso regression analysis based on NNMT-associated gene expression. Drug sensitivity was assessed using the IC50 values and apoptosis ratio, and autophagy was evaluated through Western blot and flow cytometric analysis.

Results: Significant variations in the expression of 1,182 proteins were observed following NNMT knockdown, with a significant association with autophagy-related genes. Analysis of gene expression patterns unveiled a significant correlation between NNMT expression and specific changes in gene expression in lung cancer. The predictive model successfully forecasted lung cancer patient survival outcomes, highlighting the potential of NNMT-associated genes in predicting patient survival. Knockdown of NNMT reversed osimertinib resistance in H1975 cells, as evidenced by altered IC50 values and apoptosis ratio, and changes were observed in autophagy markers.

Discussion: Knockdown of NNMT in lung cancer cells enhances drug sensitivity by modulating autophagy, providing a promising therapeutic target to overcome chemoresistance in NSCLC. The study underscores the importance of NNMT in lung cancer pathology and underscores its potential as a predictive marker for clinical outcomes. Additionally, the developed predictive model further supports the clinical relevance of NNMT-associated gene expression in improving the prognosis of lung cancer patients.

Keywords: NNMT; autophagy; drug sensitivity; machine learning; osimertinib.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was funded in part by the Natural Science Foundation of China (82027805, 81972916, 82002211,82204439). Liao Ning Revitalization Talents Program (XLYC2002013), The Science and Technology Innovation Foundation of Dalian (2019J11CY019), Special Funds of the Central Guide Local Science and Technology for Development (2020JH6/1050063), “1+X” Program for Clinical Competency Enhancement-Interdisciplinary Innovation Project of the Second Hospital of Dalian Medical University (2022JCXKYB08), National Natural Science Foundation of China (82003318). The Educational Department of Liaoning Province (LJKMZ20221289).