Title

Network toxicology and single-cell analysis reveal molecular mechanisms of DEHP-induced colorectal cancer

Authors

Yingyi Wei¹, Weixue Li², Wenhua Huang³^,4, * & Zhai Cai³^,4, †

Affiliation

¹School of Basic Medical Sciences, Dali University, School of Fundamental Sciences, Dali University, Dali, Yunnan Province, China; ²Department of Nursing, West China Hospital, West China Hospital of Sichuan University, Chengdu, Sichuan, China; ³Department of Basic Medicine, Southern Medical University, Southern Medical University Second Clinical Medical College, Guangzhou, Guangdong, China; ⁴Department of General Surgery, West China Hospital, Zhujiang Hospital, Southern Medical University, 253 Xingang East Road, Haizhu District, Guangzhou 510282, Guangdong, China; *Corresponding author

Email

Yingyi Wei - E-mail: yingyi_wei@163.com

Weixue Li - E-mail: lwx12200026@163.com

Wenhua Huang - E-mail: huangwenhua2009@139.com

Zhai Cai - E-mail: czhaidr@126.com

Article Type

Research Article

Date

Received June 1, 2026; Revised June 30, 2026; Accepted June 30, 2026, Published June 30, 2026

Abstract

Di(2-ethylhexyl) phthalate (DEHP) is a common environmental pollutant associated with colorectal cancer (CRC), but its molecular mechanisms remain unclear. Hence, we integrated toxicological target screening, weighted gene co-expression network analysis (WGCNA), and 113 machine learning algorithms to construct a diagnostic model for DEHP-related CRC. Single-cell transcriptomics and molecular docking were further applied to analyze the cellular distribution of key genes and the interactions between DEHP and target proteins. Five core genes (MMP7, CDK4, MMP1, CNR1, and TNFRSF1A) were identified and linked to inflammation, cell cycle regulation, and PI3K-Akt signaling. Thus, we report the molecular basis of DEHP-induced CRC progression providing a novel strategy for identifying environmental carcinogenic biomarkers and therapeutic targets.

Keywords

Network toxicology; di(2-ethylhexyl) phthalate (DEHP); colorectal cancer (CRC); machine learning (ML); molecular docking.

Citation

Wei et al. Bioinformation 22(6): 3430-3436 (2026)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.