Predicting therapeutic drugs for hepatocellular carcinoma based on tissue-specific pathways

Abstract

Hepatocellular carcinoma (HCC) is a significant health problem worldwide with poor prognosis. Drug repositioning represents a profitable strategy to accelerate drug discovery in the treatment of HCC. In this study, we developed a new approach for predicting therapeutic drugs for HCC based on tissue-specific pathways and identified three newly predicted drugs that are likely to be therapeutic drugs for the treatment of HCC. We validated these predicted drugs by analyzing their overlapping drug indications reported in PubMed literature. By using the cancer cell line data in the database, we constructed a Connectivity Map (CMap) profile similarity analysis and KEGG enrichment analysis on their related genes. By experimental validation, we found securinine and ajmaline significantly inhibited cell viability of HCC cells and induced apoptosis. Among them, securinine has lower toxicity to normal liver cell line, which is worthy of further research. Our results suggested that the proposed approach was effective and accurate for discovering novel therapeutic options for HCC. This method also could be used to indicate unmarked drug-disease associations in the Comparative Toxicogenomics Database. Meanwhile, our method could also be applied to predict the potential drugs for other types of tumors by changing the database.

Identification of cyclin protein using gradient boost decision tree algorithm

“…A reliable and accurate dataset is necessary to establish a prediction model [29] , [30] , [31] , [32] , [33] , [34] , [35] . Therefore, the dataset was obtained from Mohabatkar et al [14] .…”

Innovative Drug and Disease Prediction with Dimensionality Reduction and Intelligence Based Random Walk Methods

“…The variations in the processes in a single gene, Yu et al [6] have provided a new technique for defining both therapeutic and unpleasant HCC medicines based on tissue-specific pathways taking both hepatological tissue and functional pathways. Three drugs and three aggravating drugs for HCC have finally gathered by filtering the Comparative Toxicogenomics Database (CTD) criterion.…”

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

“…It is estimated that it takes an average of 10–15 years to bring a drug to the market, which costs approximately US $2,558 million ( Zhong et al, 2018 ). Therefore, predicting whether a protein can potentially be used as a drug target has significant value in disease treatment and reducing the time and cost of drug development, which greatly accelerates the drug development process for the protein ( Wang et al, 2020b ; Yu et al, 2021 ).…”