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

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. Author summary: Selecting therapeutic drugs that can be used for cancer treatment from existing drugs is a way to quickly obtain effective drugs, whereas the safety, dose and marginal effects of these drugs have been verified, and their clinical application will be faster than that of novel drug discovery. There have been a large number of successful cases of drug repositioning in the past few years. Tumors have complex regulatory pathways; thus, with the help of the HCC regulatory pathway, the single gene expression changes in the pathway, and the specific correlation between HCC and liver tissue, we screened for the predicted drugs, and constructed Connectivity Map and Toxicogenomics Database combined with KEGG pathway to carry out these drugs. Also, we verified the therapeutic effect of selected drugs in vitro to confirm the effectiveness of these drugs. Our results show that our drug screening method is effective, and can be applied to other malignant tumor drug screening. [ABSTRACT FROM AUTHOR]

Copyright of PLoS Computational Biology is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)