ArticleComputational intelligence and neuroscience2022
Network Pharmacology-Based Strategy to Investigate the Mechanisms of Lenvatinib in the Treatment of Hepatocellular Carcinoma.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.
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
2 citing papers in PubMed, 3 citations in OpenAlex.
- The role of Aurora kinase A in hepatocellular carcinoma: Unveiling the intriguing functions of a key but still underexplored factor in liver cancer.Cell proliferation · 2024Review
- Retracted: Network Pharmacology-Based Strategy to Investigate the Mechanisms of Lenvatinib in the Treatment of Hepatocellular Carcinoma.Computational intelligence and neuroscience · 2023Article
Corrections and comments
- Retraction · 2023-11-29Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is a complex and refractory malignant tumor, ranking the third cause of cancer-related deaths worldwide. Lenvatinib is currently employed to treat advanced, unresectable HCC as a first-line drug. The purpose of this study was to explore the pharmacological mechanisms of lenvatinib acting on HCC through the analysis of differential expressed genes based on network pharmacology. The target genes of lenvatinib were collected from PubChem, SwissTargetPrediction, PharmMapper, and BATMAN-TCM online public databases. In addition, related gene targets for HCC were obtained using NCBI Gene Expression Omnibus (NCBI-GEO) database. Afterward, the protein-protein interaction (PPI) network was established to visualize and understand the interaction relationships of overlapping gene targets from both lenvatinib and HCC. Furthermore, according to the data obtained, Gene Ontology (GO) analysis indicated that these intersectant genes were mainly enriched in response to xenobiotic stimulus, gland development, ion channel complex, membrane raft, and steroid binding. Besides, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that the therapeutic effects of lenvatinib on HCC probably involved bile secretion, MAPK signaling pathway, cGMP-PKG signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway. Moreover, a total of six key differential genes, namely, ALB, CCND1, ESR1, AR, CCNA2, and AURKA, were identified as most significant targets associated with lenvatinib treating HCC and further verified by molecular docking, which demonstrated that lenvatinib had a strong binding efficiency with these six key gene-encoded proteins. Taken together, this study systematically provided new insights for researchers to determine the intervention mechanisms of lenvatinib in HCC therapy.
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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.