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ArticleNaunyn-Schmiedeberg's archives of pharmacology2025

Bioinformatics combined with network pharmacology and experimental validation to identify key biomarkers of hepatocellular carcinoma and corresponding compounds in Radix Astragali and Pueraria Mirifica.

Mohan Li, Bang Liu, Minghua Xian, Shumei Wang, Peiyi Liu

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Article in Naunyn-Schmiedeberg's archives of pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Frontiers in pharmacology · 2026
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Mohan LiGuangdong Pharmaceutical University, Guangzhou, 510006, China.
Bang LiuGuangdong Pharmaceutical University, Guangzhou, 510006, China.
Minghua XianGuangdong Pharmaceutical University, Guangzhou, 510006, China.
Shumei WangGuangdong Pharmaceutical University, Guangzhou, 510006, China. gdpuwsm@126.com.
Peiyi LiuHuangpu People's Hospital of Zhongshan, Zhongshan, 528429, China.

Funding

Shumei Wang 81773884;81473413
6 · The paper itself

Abstract

The occurrence and death rates of primary hepatocellular carcinoma (HCC) are increasing, and there remains a shortage of effective oral medications with minimal side effects. We aim to identify potential biomarkers and compounds from Radix Astragali (RA) and Pueraria Mirifica (PM) to treat liver cancer and improve prognosis. Differentially expressed genes (DEGs) associated with HCC were identified by bioinformatics analysis of three datasets, GSE112791, GSE101685, and GSE45114. Using public databases to predict the bioactive components and possible targets of RA and PM. Target crossover from Gene Expression Omnibus (GEO) and public databases were used to identify potential biomarkers for HCC. Subsequently, validation and prognostic value analyses were performed using the Gene Expression Profile Interaction Analysis (GEPIA) platform. The Cytoscape software created a network of "compound targets" to pinpoint compounds linked to the biomarkers. Molecular docking techniques were utilized to validate the connection between these compounds and the identified biomarkers. Ultimately, the HepG2 liver cancer cell line was chosen to assess the inhibitory effect of Hederagenin (HDG) and to confirm the expression of ADH1B through Western blot analysis. In this study, four key biomarkers (NR1I2, ADH1B, NQO1, GHR) were identified. Molecular docking showed that these four core targets could form stable conformations with the corresponding compounds. As the drug concentration decreases, the inhibitory effect on HepG2 diminishes, and the survival rate of HepG2 cells significantly declines following the administration of 100 µmol/L HDG. Compared to the control, the expression of ADH1B protein is significantly increased in HepG2 cells treated with 100 µmol/L HDG. The study identified four key biomarkers (ADH1B, GHR, NQO1, NR1I2) that have prognostic ability for HCC. This study provides biomarkers and potential targeted monomeric medicines for treating HCC.

Indexed as

Antineoplastic Agents, PhytogenicBiomarkers, TumorCarcinoma, HepatocellularDrugs, Chinese HerbalLiver NeoplasmsPuerariaAstragalus propinquusComputational BiologyGene Expression Regulation, NeoplasticHep G2 CellsHumansMolecular Docking SimulationNetwork PharmacologyAntineoplastic Agents, PhytogenicBiomarkers, TumorDrugs, Chinese HerbalHuang QiBioinformaticsBiomarkersHederageninHepatocellular carcinomaMolecular dockingNetwork pharmacology

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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 OpenQuestion graph.