Advanced network pharmacology study reveals multi-pathway and multi-gene regulatory molecular mechanism of Bacopa monnieri in liver cancer based on data mining, molecular modeling, and microarray data analysis

Sadaqat, Muhammad; Qasim, Muhammad; Qamar, Muhammad Tahir ul; Masoud, Muhammad Shareef; Ashfaq, Usman Ali; Fatima, Nosheen; Fatima, Kinza; Allemailem, Khaled S.; Almatroudi, Ahmad

doi:10.1016/j.compbiomed.2023.107059

Cited by 16 publications

(8 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Kaplan-Meier survival curves were established and then log-rank of the survival rates were compared between the high and low expression groups using the log-rank test[19]. ABCB1, AURKA, CDK1, HDAC6, MET, and MMP3 were the 6 hub targets with p-value <0.01, which suggested that ABCB1, AURKA, CDK1, HDAC6, MET, and MMP3 might be involved in pancreatic survival, and further analysis of the RNA expression levels in normal and pancreatic cancer tissues was performed ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The fundamental idea of network pharmacology is to utilise big data information to analyse the treatment effects of drugs on diseases on the basis of “Drug-Target-Pathway”[18]. There is a variety of applications of current network pharmacology for diseases such as inflammation, cancer, cardiovascular disease, neurodegenerative disease, and so on[19]. For instance, Shreni Agrawal et al[20] used network pharmacology to study the therapeutic effects of kaempferol and catechins on pancreatic cancer.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Network pharmacology, single gene survival analysis and molecular docking to study the mechanism of Sotetsuflavone in the treatment of pancreatic cancer

Chu,

2024

Preprint

View full text Add to dashboard Cite

Pancreatic cancer is a highly lethal cancer with limited treatment options. The number of pancreatic cancer patients is increasing rapidly worldwide. Many natural products have been shown to have anticancer activity in a range of studies. Sotetsuflavone is derived from Cycas revoluta Thunb. and exhibits anticancer activity. The present study incorporates network pharmacology, single gene survival analysis, gene expression analysis and molecular docking to reveal the mechanism of Sotetsuflavone in the treatment of pancreatic cancer. Initially, it was evaluated through multiple databases for a comprehensive pharmacological evaluation of Sotetsuflavone. Then, the target information of Sotetsuflavone and pancreatic cancer was searched and screened using public databases. According to the results of matching the potential targets of Sotetsuflavone with the targets of pancreatic cancer, the protein-protein interaction (PPI) network was constructed by using the STRING database and imported into Cytoscape 3.9.0 for the network analysis to screen the hub targets, and then classify and co-expression analysis was carried out on the hub targets. Then, we conducted Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of potential and hub targets and discovered that these targets were involved in pancreatic cancer-related signalling pathways. In addition, the GEPIA 2 platform was used to be performed for single gene survival analysis and gene expression analysis, and AutoDock Vina was employed for molecular docking analysis. In total, we have acquired 31 hub targets for the treatment of pancreatic cancer by Sotetsuflavone, namely ABCB1, AURKA, CDK1, and so on. Kaplan-Meier survival analyses demonstrated that ABCB1, AURKA, CDK1, HDAC6, MET, and MMP3 are promising hub targets that can be used as biomarkers for pancreatic cancer diagnosis and prognosis. These hub targets are highly expressed in pancreatic cancer tissues compared to normal tissues. The molecular docking results showed a strong binding capacity of Sotetsuflavone to these hub targets. In summary, it is proposed that Sotetsuflavone is a new anticancer drug, which can regulate cancer-related signalling pathways such as pancreatic cancer by inhibiting the activities of ABCB1, AURKA, CDK1, HDAC6, MET, and MMP3, which are hub targets with up-regulated expression in pancreatic cancer tissues, in order to treat pancreatic cancer. However, it also requires a series of in vivo and in vitro studies to ensure its safety and efficacy.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network pharmacology, single gene survival analysis and molecular docking to study the mechanism of Sotetsuflavone in the treatment of pancreatic cancer

Chu,

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…2 D). JUN plays an essential role in tumour growth and progression by modulating several signalling pathways, including the MAPK signalling pathway [ 36 ]. p53 is a tumour suppressor gene, and its loss of function mutation leads to carcinogenesis [ 37 ].…”

Section: Resultsmentioning

confidence: 99%

Elucidating the anti-cancer potential of Cinnamomum tamala essential oil against non-small cell lung cancer: A multifaceted approach involving GC-MS profiling, network pharmacology, and molecular dynamics simulations

Mohanty,

Padhee,

Priyadarshini

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…These in silico approaches and the advancement of supercomputing capabilities have impressively improved the effectiveness of lead discovery in pharmaceutical research [17]. Artificial intelligence (AI) and machine learning techniques are frequently used for the identification of new lead compounds [18,19]. The identification and design of new lead compounds that bind to the therapeutic drug targets are greatly enhanced by artificial intelligence and ML approaches [20].…”

Section: Introductionmentioning

confidence: 99%

In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches

Ajmal,

Danial,

Zulfat

et al. 2024

Pharmaceuticals

View full text Add to dashboard Cite

Single-point mutations in the Kirsten rat sarcoma (KRAS) viral proto-oncogene are the most common cause of human cancer. In humans, oncogenic KRAS mutations are responsible for about 30% of lung, pancreatic, and colon cancers. One of the predominant mutant KRAS G12D variants is responsible for pancreatic cancer and is an attractive drug target. At the time of writing, no Food and Drug Administration (FDA) approved drugs are available for the KRAS G12D mutant. So, there is a need to develop an effective drug for KRAS G12D. The process of finding new drugs is expensive and time-consuming. On the other hand, in silico drug designing methodologies are cost-effective and less time-consuming. Herein, we employed machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF) for the identification of new inhibitors against the KRAS G12D mutant. A total of 82 hits were predicted as active against the KRAS G12D mutant. The active hits were docked into the active site of the KRAS G12D mutant. Furthermore, to evaluate the stability of the compounds with a good docking score, the top two complexes and the standard complex (MRTX-1133) were subjected to 200 ns MD simulation. The top two hits revealed high stability as compared to the standard compound. The binding energy of the top two hits was good as compared to the standard compound. Our identified hits have the potential to inhibit the KRAS G12D mutation and can help combat cancer. To the best of our knowledge, this is the first study in which machine-learning-based virtual screening, molecular docking, and molecular dynamics simulation were carried out for the identification of new promising inhibitors for the KRAS G12D mutant.

show abstract

Advanced network pharmacology study reveals multi-pathway and multi-gene regulatory molecular mechanism of Bacopa monnieri in liver cancer based on data mining, molecular modeling, and microarray data analysis

Cited by 16 publications

References 50 publications

Network pharmacology, single gene survival analysis and molecular docking to study the mechanism of Sotetsuflavone in the treatment of pancreatic cancer

Network pharmacology, single gene survival analysis and molecular docking to study the mechanism of Sotetsuflavone in the treatment of pancreatic cancer

Elucidating the anti-cancer potential of Cinnamomum tamala essential oil against non-small cell lung cancer: A multifaceted approach involving GC-MS profiling, network pharmacology, and molecular dynamics simulations

In Silico Prediction of New Inhibitors for Kirsten Rat Sarcoma G12D Cancer Drug Target Using Machine Learning-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulation Approaches

Contact Info

Product

Resources

About