Mannose Inhibits the Pentose Phosphate Pathway in Colorectal Cancer and Enhances Sensitivity to 5-Fluorouracil Therapy

Hadeethi, Sadaf Al; El-Baba, Chirine; Araji, Khaled; Hayar, Berthe; Cheikh, Israa; El-Khoury, Riyad; Usta, Jinan; Darwiche, Nadine

doi:10.3390/cancers15082268

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Among the 12 qualified drug references, 10 are consistent with the literature descriptions. The literature pertaining to the drug 5-FU, which has the highest frequency, is as follows: in the research by Al Hadeethi et al, it was found that the combined use of 5-fluorouracil (5-FU) and mannose effectively downregulates the pentose phosphate pathway (PPP) to inhibit CRC. This result corresponds to HANSynergy predicting the synergistic effect of 5-FU and mannose in the HCT116 cell line.…”

Section: Resultsmentioning

confidence: 99%

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

Cheng,

Wang,

Liu

et al. 2024

J. Chem. Inf. Model.

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Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug−drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein− protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein−protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.

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Section: Resultsmentioning

confidence: 99%

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

Cheng,

Wang,

Liu

et al. 2024

J. Chem. Inf. Model.

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“…Thus, alteration of the PPP contributes directly to cell proliferation, survival and senescence [ 60 ]. Many studies have shown that inhibiting the PPP can inhibit the development of tumors [ 61 , 62 ], but there are currently almost no reports on the regulation of cell death and migration by D-ribouranose 5-phosphate. Interestingly, in this study, the lung cancer cell group treated with Pinellia ternata showed a decrease in D-ribofuranose 5-phosphate expression.…”

Section: Discussionmentioning

confidence: 99%

Integrated network pharmacology and metabolomics to reveal the mechanism of Pinellia ternata inhibiting non-small cell lung cancer cells

Feng,

Hu,

Peng

et al. 2024

BMC Complement Med Ther

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Lung cancer is a malignant tumor with highly heterogeneous characteristics. A classic Chinese medicine, Pinellia ternata (PT), was shown to exert therapeutic effects on lung cancer cells. However, its chemical and pharmacological profiles are not yet understood. In the present study, we aimed to reveal the mechanism of PT in treating lung cancer cells through metabolomics and network pharmacology. Metabolomic analysis of two strains of lung cancer cells treated with Pinellia ternata extracts (PTE) was used to identify differentially abundant metabolites, and the metabolic pathways associated with the DEGs were identified by MetaboAnalyst. Then, network pharmacology was applied to identify potential targets against PTE-induced lung cancer cells. The integrated network of metabolomics and network pharmacology was constructed based on Cytoscape. PTE obviously inhibited the proliferation, migration and invasion of A549 and NCI-H460 cells. The results of the cellular metabolomics analysis showed that 30 metabolites were differentially expressed in the lung cancer cells of the experimental and control groups. Through pathway enrichment analysis, 5 metabolites were found to be involved in purine metabolism, riboflavin metabolism and the pentose phosphate pathway, including D-ribose 5-phosphate, xanthosine, 5-amino-4-imidazolecarboxyamide, flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD). Combined with network pharmacology, 11 bioactive compounds were found in PT, and networks of bioactive compound–target gene–metabolic enzyme–metabolite interactions were constructed. In conclusion, this study revealed the complicated mechanisms of PT against lung cancer. Our work provides a novel paradigm for identifying the potential mechanisms underlying the pharmacological effects of natural compounds.

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