Overexpression of UDP-Glucose 4-Epimerase Is Associated with Differentiation Grade of Gastric Cancer

Souza, Maria de Fátima Deodato de; Filho, Antônio Felix da Silva; Albuquerque, Amanda Vasconcelos de; Quirino, Michael Williams Leal; Albuquerque, Mário S. de Souza; Cordeiro, Marina Ferraz; Martins, Mário Rino; Pitta, Ivan da Rocha; Lucena-Araújo, Antonio R.; Pitta, Maíra Galdino da Rocha; Rêgo, Moacyr Jesus Barreto de Melo

doi:10.1155/2019/6325326

Cited by 4 publications

(3 citation statements)

References 16 publications

(15 reference statements)

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“…UDP-galactose-4-epimerase (GALE), a key enzyme of galactose metabolism,4,5 was overexpressed in some kinds of cancers, such as papillary thyroid carcinoma and glioblastoma [Sun et al, 2019]. Souza observed that GALE expression was associated with clinical-pathological parameters and the outcome of gastric adenocarcinoma patients [ de Souza et al, 2019]. These evidence suggested that GALE may be a diagnostic biomarker and a potential therapeutic target.…”

Section: Explanation Of Transformer Attention Scoresmentioning

confidence: 99%

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

Gao

Fang

et al. 2022

Briefings in Bioinformatics

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Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challenging for the wet-lab experiments due to the time-consuming process of the enormous search space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic functions. Notwithstanding the success of current models, understanding the mechanism of drug synergy from a chemical–gene–tissue interaction perspective lacks study, hindering current algorithms from drug mechanism study. Here, we proposed a deep neural network model termed DTSyn (Dual Transformer encoder model for drug pair Synergy prediction) based on a multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer encoder to capture chemical substructure–gene and gene–gene associations and a coarse-granularity transformer encoder to extract chemical–chemical and chemical–cell line interactions. DTSyn achieved the highest receiver operating characteristic area under the curve of 0.73, 0.78. 0.82 and 0.81 on four different cross-validation tasks, outperforming all competing methods. Further, DTSyn achieved the best True Positive Rate (TPR) over five independent data sets. The ablation study showed that both transformer encoder blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, representing the potential mechanisms of drug action. By leveraging the attention mechanism and pretrained gene embeddings, DTSyn shows improved interpretability ability. Thus, we envision our model as a valuable tool to prioritize synergistic drug pairs with chemical and cell line gene expression profile.

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Section: Explanation Of Transformer Attention Scoresmentioning

confidence: 99%

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

Gao

Fang

et al. 2022

Briefings in Bioinformatics

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“…To evaluate TSTA3 and FUCA2 expression we followed the methods described by De Souza et al [36]. Briefly, biopsy slices were deparaffinized with xylol and rehydrated in graded ethanol.…”

Section: Immunohistochemistrymentioning

confidence: 99%

“…If more than 10% of tumor cells were stained in different degrees of intensity, expression was considered positive. According to De Souza et al [36], staining below 10% was denoted as negative. Cancer samples that presented a counterpart of normal tissue were studied and mucosa was described.…”

Section: Immunohistochemistrymentioning

confidence: 99%

FUCA2 and TSTA3 expression in gastric cancer: candidate biomarkers of malignant transformation

Quirino

Albuquerque

Souza

et al. 2022

Folia Histochem Cytobiol.

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This article is available in open access under Creative Common Attribution-Non-Commercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0) license, allowing to download articles and share them with others as long as they credit the authors and the publisher, but without permission to change them in any way or use them commercially. www.journals.viamedica.pl/folia_histochemica_cytobiologica ©Polish Society for Histochemistry and Cytochemistry Folia Histochem Cytobiol.

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DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

Gao²,

Fang³

et al. 2022

Preprint

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Drug combination therapies are superior to monotherapy for cancer treatment in many ways when addressing tumor heterogeneity issue. For wet-lab experiment, screening out novel synergistic drug pairs is challenging due to the enormous searching space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic function. Notwithstanding the success of current models, the power of generalization to other datasets as wells as understanding of mechanism for chemical-chemical interaction or chemical-sample interaction are lack of study, hindering current algorithms from real application. In this paper, we proposed a deep neural model termed DTSyn (Dual Transformer model for drug pair Synergy prediction) based on multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer for capturing chemical substructure-gene and gene-gene associations and a coarse-granularity transformer for extracting and chemical-chemical and chemical-cell line interactions. DTSyn achieves highest Receiver operating characteristic area under curve (ROC AUC) of 0.73, 0.78. 0.82 and 0.81 on four different cross validation tasks, outperforming all competing methods. Further, DTSyn achieved best True Positive Rate (TPR) over five independent datasets. The ablation study showed that both transformer blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, which may represent the mechanisms of drug action. Thus, we envision our model a valuable tool to prioritize synergistic drug pairs by utilizing chemicals and transcriptome data.

show abstract

Overexpression of UDP-Glucose 4-Epimerase Is Associated with Differentiation Grade of Gastric Cancer

Cited by 4 publications

References 16 publications

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

FUCA2 and TSTA3 expression in gastric cancer: candidate biomarkers of malignant transformation

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

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