Network pharmacology and machine learning reveal kit as a target of roburic acid in anaplastic thyroid carcinoma: Preliminary in vitro validation
- 1Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Jiangsu, Nanjing, China
Abstract
This study aims to systematically explore the potential molecular mechanism of Roburic Acid (RBA) in the treatment of Anaplastic Thyroid Carcinoma (ATC) through the approaches of network pharmacology, machine learning and in vitro experiments. Public databases were used to retrieve the potential targets of RBA and disease-related targets of ATC. Intersection analysis was performed to identify the common targets, followed by the construction of a Protein-Protein Interaction (PPI) network for these common targets. Enrichment analyses, including GO functional and KEGG pathway analyses, were carried out for the targets. Core targets were further prioritized using SVM, LASSO and random forest machine learning algorithms. Validation of the binding affinity and stability of RBA with the core target was performed using molecular docking and dynamics simulations. Finally, in vitro experiments—including cell viability, scratch assay, apoptosis detection, and Western blot—were conducted using the ATC cell line KHM-5M to validate the therapeutic effect and underlying mechanism of RBA. A total of 401 RBA-associated targets and 2151 ATC-related targets were identified; their intersection yielded 173 shared targets. GO enrichment analysis indicated that these targets were mainly involved in responses to peptide hormones and nuclear receptor activity, among other biological functions. KEGG pathway analysis showed significant enrichment in cancer-related pathways, including the PI3K-Akt and MAPK signaling pathways. Using SVM, LASSO regression, and random forest models, KIT was consistently identified as the single core target. Molecular docking and molecular dynamics simulations demonstrated strong binding between RBA and c-KIT, characterized by low binding energy and the formation of a stable protein-ligand complex. In vitro, RBA significantly reduced KHM-5M cell viability and migration; the inhibitory effect on migration became pronounced at 24 h and was comparable to that of the positive control DOX. Furthermore, RBA promoted apoptosis and dose-dependently suppressed c-KIT phosphorylation. This study integrated network pharmacology, machine learning, and molecular simulations to identify KIT as a high-confidence core target of RBA in ATC. Preliminary in vitro validation demonstrated that RBA suppresses ATC cell viability and migration, induces apoptosis, and inhibits c-KIT phosphorylation in a dose-dependent manner.
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BibTeX
@article{tan2026network,
title = {{Network pharmacology and machine learning reveal kit as a target of roburic acid in anaplastic thyroid carcinoma: Preliminary in vitro validation}},
author = {Foxing Tan and Haolin Du and Longcheng Deng and Yaping Shen and Yan Yang and Kun Mei and Zilu Chen and Yan Huang},
journal = {PLOS One},
year = {2026},
month = oct,
doi = {10.1371/journal.pone.0359087},
url = {https://doi.org/10.1371/journal.pone.0359087},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Network pharmacology and machine learning reveal kit as a target of roburic acid in anaplastic thyroid carcinoma: Preliminary in vitro validation
AU - Foxing Tan
AU - Haolin Du
AU - Longcheng Deng
AU - Yaping Shen
AU - Yan Yang
AU - Kun Mei
AU - Zilu Chen
AU - Yan Huang
PY - 2026
DA - 2026/10/15
JO - PLOS One
DO - 10.1371/journal.pone.0359087
UR - https://doi.org/10.1371/journal.pone.0359087
AB - This study aims to systematically explore the potential molecular mechanism of Roburic Acid (RBA) in the treatment of Anaplastic Thyroid Carcinoma (ATC) through the approaches of network pharmacology, machine learning and in vitro experiments. Public databases were used to retrieve the potential targets of RBA and disease-related targets of ATC. Intersection analysis was performed to identify the common targets, followed by the construction of a Protein-Protein Interaction (PPI) network for these common targets. Enrichment analyses, including GO functional and KEGG pathway analyses, were carried out for the targets. Core targets were further prioritized using SVM, LASSO and random forest machine learning algorithms. Validation of the binding affinity and stability of RBA with the core target was performed using molecular docking and dynamics simulations. Finally, in vitro experiments—including cell viability, scratch assay, apoptosis detection, and Western blot—were conducted using the ATC cell line KHM-5M to validate the therapeutic effect and underlying mechanism of RBA. A total of 401 RBA-associated targets and 2151 ATC-related targets were identified; their intersection yielded 173 shared targets. GO enrichment analysis indicated that these targets were mainly involved in responses to peptide hormones and nuclear receptor activity, among other biological functions. KEGG pathway analysis showed significant enrichment in cancer-related pathways, including the PI3K-Akt and MAPK signaling pathways. Using SVM, LASSO regression, and random forest models, KIT was consistently identified as the single core target. Molecular docking and molecular dynamics simulations demonstrated strong binding between RBA and c-KIT, characterized by low binding energy and the formation of a stable protein-ligand complex. In vitro, RBA significantly reduced KHM-5M cell viability and migration; the inhibitory effect on migration became pronounced at 24 h and was comparable to that of the positive control DOX. Furthermore, RBA promoted apoptosis and dose-dependently suppressed c-KIT phosphorylation. This study integrated network pharmacology, machine learning, and molecular simulations to identify KIT as a high-confidence core target of RBA in ATC. Preliminary in vitro validation demonstrated that RBA suppresses ATC cell viability and migration, induces apoptosis, and inhibits c-KIT phosphorylation in a dose-dependent manner.
SN - 1932-6203
ER -