Evidence map›Paper›PMID 42223693›Full record

ArticleFunctional & integrative genomics2026

Exploring the potential targets and mechanisms of artemisinin in the treatment of diabetic kidney disease using network pharmacology and molecular docking.

Hongning Li, Xiangzeng Cai, Shuqin Yu

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Article in Functional & integrative genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Hongning LiDepartment of Endocrinology, Lishui District People's Hospital, No. 85 Chongwen Road Nanjing, Jiangsu, 211200, China.
Xiangzeng CaiDepartment of Endocrinology, Lishui District People's Hospital, No. 85 Chongwen Road Nanjing, Jiangsu, 211200, China.
Shuqin YuDepartment of Endocrinology, Lishui District People's Hospital, No. 85 Chongwen Road Nanjing, Jiangsu, 211200, China. yushuqinzx20@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetic kidney disease (DKD) is a secondary glomerular disease caused by diabetes, and its incidence is increasing annually. Artemisinin is an organic compound with multiple pharmacological effects. This study aims to investigate the potential therapeutic effects of artemisinin on DKD and its molecular mechanisms. Common targets were identified using network pharmacology, transcriptome data from the Gene Expression Omnibus (GEO) database, and three machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF). Target expression profiles were examined through single-cell sequencing data analysis. Molecular docking and cellular assays, including western blot, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL), enzyme-linked immunosorbent assay (ELISA), and reactive oxygen species (ROS) detection, validated the effects of artemisinin and Fms related receptor tyrosine kinase 1(FLT1) on human renal glomerular endothelial cells (HRGECs) under a high-glucose (HG) injury model. A total of 64 common targets between artemisinin and DKD were identified, and machine learning identified FLT1 as a key target. Single-cell analysis revealed high FLT1 expression in DKD endothelial cells. More importantly, artemisinin exhibited stable binding to FLT1. HG reduced FLT1 expression and cell viability in HRGECs. Besides, HG-induced decrease in cell viability, increase in proportion of TUNEL-positive cells, elevation in the levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) and lipid ROS production were reversed by FLT1 overexpression or artemisinin treatment, and these protective effects of artemisinin were significantly attenuated upon FLT1 knockdown. Artemisinin alleviates HG-induced HRGEC injury by restoring FLT1 expression, providing a potential therapeutic target for DKD treatment.

Indexed as

ArtemisininsDiabetic NephropathiesApoptosisGlucoseHumansMolecular Docking SimulationNetwork PharmacologyReactive Oxygen SpeciesartemisininArtemisininsGlucoseReactive Oxygen SpeciesArtemisininDiabetic kidney diseaseFms related receptor tyrosine kinase 1Machine learningMolecular docking

Identifiers

PMID42223693

What Socratic holds

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Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.