ArticleMolecular diversity2025
Integrating network pharmacology, molecular docking, and bioinformatics to explore the mechanism of sparganii rhizoma in the treatment of laryngeal cancer.
Article in Molecular diversity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Pulmonary fibrosis: Emerging targets and novel therapeutics.The Journal of international medical research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sparganii Rhizoma (SR) has demonstrated promising anticancer effects across various malignancies; however, its mechanisms in laryngeal cancer (LC) remain poorly understood. This study employs network pharmacology and molecular docking to investigate the molecular mechanisms underlying SR's therapeutic effects on LC, providing novel insights for its potential use in treatment. Active compounds and targets of SR were identified through the TCMSP and Pharmmapper databases, while LC-related targets were sourced from GEO, GeneCards, OMIM, and PharmGkb databases. A Venn diagram generated from these datasets highlighted 58 overlapping targets. The STRING database and Cytoscape 3.9.1 software facilitated the construction of a protein-protein interaction network for these targets, and R language analysis revealed 15 core targets. GO and KEGG enrichment analyses, conducted with the ''clusterProfiler'' package, identified relevant biological processes, cellular components, and molecular functions associated with LC treatment. KEGG analysis suggested SR primarily regulates pathways such as TNF, IL-17, and P53. Molecular docking confirmed SR's ability to bind effectively to the 15 core targets. Molecular dynamics simulations further validated stable protein-ligand interactions for MAPK1, GSK3B, and MAPK14. Core target validation across transcriptional, translational, and immune infiltration levels was performed using GEPIA, HPA, cBioPortal, and TIMER databases. In conclusion, network pharmacology, molecular docking, and dynamics simulations provided insights into SR's mechanism in LC treatment, forming a theoretical basis for further investigation of its therapeutic potential.
Indexed as
Identifiers
40009149What OpenQuestion holds
Registered trials
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.