Unraveling the molecular mechanism of l-menthol against cervical cancer based on network pharmacology, molecular docking and in vitro analysis

Sidhu, Harsimran; Gautam, Lalit Kumar; Capalash, Neena

doi:10.1007/s11030-022-10429-1

Cited by 10 publications

(5 citation statements)

References 89 publications

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“…On the contrary, based on the traditional Sedlis criteria 38 as the risk classification system, no significant difference in OS was observed within the cohort 39 . Promising results incorporating ML algorithms with gene expression, molecular subtyping, and pharmacogenomics in CC patients were also reported recently 40–42 …”

Section: Discussionmentioning

confidence: 91%

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A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

Yu¹,

Shou

et al. 2022

Cancer Medicine

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Background Prediction models with high accuracy rates for nonmetastatic cervical cancer (CC) patients are limited. This study aimed to construct and compare predictive models on the basis of machine learning (ML) algorithms for predicting the 5‐year survival status of CC patients through using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute. Methods The data registered from 2004 to 2016 were extracted and randomly divided into training and validation cohorts (8:2). The least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant factors. Then, four predictive models were constructed, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The predictive models were evaluated and compared using Receiver‐operating characteristics with areas under the curves (AUCs) and decision curve analysis (DCA), respectively. Results A total of 13,802 patients were involved and classified into training ( N = 11,041) and validation ( N = 2761) cohorts. By using the LASSO regression method, seven factors were identified. In the training cohort, the XGBoost model showed the best performance (AUC = 0.8400) compared to the other three models (all p < 0.05 by Delong's test). In the validation cohort, the XGBoost model also demonstrated a superior prediction ability (AUC = 0.8365) than LR and SVM models (both p < 0.05 by Delong's test), although the difference was not statistically significant between the XGBoost and the RF models ( p = 0.4251 by Delong's test). Based on the DCA results, the XGBoost model was also superior, and feature importance analysis indicated that the tumor stage was the most important variable among the seven factors. Conclusions The XGBoost model proved to be an effective algorithm with better prediction abilities. This model is proposed to support better decision‐making for nonmetastatic CC patients in the future.

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Section: Discussionmentioning

confidence: 91%

“… 39 Promising results incorporating ML algorithms with gene expression, molecular subtyping, and pharmacogenomics in CC patients were also reported recently. 40 , 41 , 42 …”

Section: Discussionmentioning

confidence: 99%

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

Yu¹,

Shou

et al. 2022

Cancer Medicine

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“…Compared to de novo drug development, it results in quick drug access at a lower cost and shorter time 55 . One of the most recent approaches to investigate the activity of a drug is network pharmacology to select key targets of the drug followed by molecular docking for simulating structure‐based binding, through computational tools 56,57 . Therefore, it is relevant to find the potential targets of BRAF inhibitors and their likely binding energies by exploiting computational tools.…”

Section: Discussionmentioning

confidence: 99%

Network pharmacology and molecular docking analysis on potential molecular targets and mechanism of action of BRAF inhibitors for application in wound healing

Karhana

Dabral

Garg

et al. 2023

J of Cellular Biochemistry

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Topical application of BRAF inhibitors has been shown to accelerate wound healing in murine models, which can be extrapolated into clinical applications. The aim of the study was to identify suitable pharmacological targets of BRAF inhibitors and elucidate their mechanisms of action for therapeutic applicability in wound healing, by employing bioinformatics tools including network pharmacology and molecular docking. The potential targets for BRAF inhibitors were obtained from SwissTargetPrediction, DrugBank, CTD, Therapeutic Target Database, and Binding Database. Targets of wound healing were obtained using online databases DisGeNET and OMIM (Online Mendelian Inheritance in Man). Common targets were found by using the online GeneVenn tool. Common targets were then imported to STRING to construct interaction networks. Topological parameters were assessed using Cytoscape and core targets were identified. FunRich was employed to uncover the signaling pathways, cellular components, molecular functions, and biological processes in which the core targets participate. Finally, molecular docking was performed using MOE software. Key targets for the therapeutic application of BRAF inhibitors for wound healing are peroxisome proliferator‐activated receptor γ, matrix metalloproteinase 9, AKT serine/threonine kinase 1, mammalian target of rapamycin, and Ki‐ras2 Kirsten rat sarcoma viral oncogene homolog. The most potent BRAF inhibitors that can be exploited for their paradoxical activity for wound healing applications are Encorafenib and Dabrafenib. By using network pharmacology and molecular docking, it can be predicted that the paradoxical activity of BRAF inhibitors can be used for their potential application in wound healing.

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“…Similarly, menthol has a possible cytotoxic action. Its activity against different tumor cell lineages has been investigated [ 1 , 4 , 5 ], including some related to cervical cancer [ 6 ] or against ovarian cancer cells [ 7 ] and rat glioma cells. Menthol also inhibits murine leukemia cells [ 8 ], but shows low toxicity in colorectal cancer cells [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative Cytotoxicity of Menthol and Eucalyptol: An In Vitro Study on Human Gingival Fibroblasts

Puig-Herreros,

Sanz,

García-Bernal

et al. 2024

Pharmaceutics

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The aim of this study was to assess the influence of eucalyptol and menthol on the cell viability, migration, and reactive oxygen species production of human gingival fibroblasts (GFs) in vitro. Three different concentrations of eucalyptol and menthol were prepared following ISO 10993-5 guidelines (1, 5, and 10 mM). GFs were isolated from extracted teeth from healthy donors. The following parameters were assessed: cell viability via MTT, Annexin-V-FITC and 7-AAD staining, and IC50 assays; cell migration via horizontal scratch wound assay; and cell oxidative stress via reactive oxygen species assay. Data were analyzed using one-way ANOVA and Tukey’s post hoc test. Statistical significance was established at p < 0.05. Eucalyptol and Menthol exhibited high cytotoxicity on gingival fibroblasts, as evidenced by cytotoxicity assays. Eucalyptol showed lower levels of cytotoxicity than menthol, compared to the control group. The cytotoxicity of the tested substances increased in a concentration-dependent manner. The same occurred in a time-dependent manner, although even 10 min of exposure to the tested substances showed a high cytotoxicity to the GFs. Commercially available products for oral application with these substances in their composition should be tested for cytotoxicity before their use.

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Unraveling the molecular mechanism of l-menthol against cervical cancer based on network pharmacology, molecular docking and in vitro analysis

Cited by 10 publications

References 89 publications

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms

Network pharmacology and molecular docking analysis on potential molecular targets and mechanism of action of BRAF inhibitors for application in wound healing

Comparative Cytotoxicity of Menthol and Eucalyptol: An In Vitro Study on Human Gingival Fibroblasts

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