Analysis of key targets for 5-hydroxymethyl-2-furfural-induced lung cancer based on network toxicology, network informatics, and in vitro experiments

Abstract

5-hydroxymethyl-2-furfural (5-HMF) is a by-product of Maillard reaction and widely exists in food and environment, which may lead to lung cancer. However, the relevant mechanism is unknown. This study aims to predict the key targets of 5-HMF-induced lung cancer through network toxicology, analyze the relationship between the key targets and lung cancer through network informatics, and further validate them through in vitro experiments. By using ChEMBL, STITCH, GeneCards, and OMIM databases, 51 toxic targets were identified. GO and KEGG enrichment analyses indicated a strong correlation between toxic targets and lung cancer. Through protein-protein interaction (PPI) analysis, MAPK3, MAPK1, and SRC were identified as key targets implicated in 5-HMF-induced lung cancer. The HPA database showed high expression of these three key targets in lung cancer tissues. Kaplan-Meier database demonstrated that the higher expression of these key targets in lung cancer patients was associated with a poorer prognosis. The TIMER database revealed that the high expression of these key targets had a significant impact on the level of immune cell infiltration in lung cancer, particularly impacting CD4+ T cells and macrophages. Finaly, in In vitro experiments demonstrated that prolonged exposure to 5-HMF induced malignant transformation of BEAS-2B cells and the upregulation of key targets. The findings suggest that 5-HMF is a contributing factor in the development of lung cancer, with MAPK3, MAPK1, and SRC potentially playing crucial roles in this process.

Keywords:

• 5-Hydroxymethyl-2-furfural
• lung cancer
• network toxicology
• network informatics
• in vitro experiments

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data will be made available on request.

Additional information

Funding

The project was supported by Smart Healthcare Industrial School, Nanjing Medical University; Jiangsu Students Platform for innovation and entrepreneurship training program (202310312135E, 202310312144K, 202310312074Y); National Natural Science Foundation of China (82202818).