  Motivation: Hepatocellular carcinoma (HCC) is a significant health problem
worldwide and annual number of cases are nearly more than 700,000.
However,there are few safe and effective thera-peutic options for HCC
patients.Here, we propose a new approach for predicting therapeutic and
aggravating drugs for HCCbased ontissue-specificpathways, which considersnot
onlyliver tis-sueand functional informationof pathways, but also the changes of
single gene in pathways. Results: Firstly, we map genes related to HCC to the
liver-specific protein interaction network and get anextended tissue-specific
gene set of HCC. Then, based on the extended gene set, 12 en-riched
KEGGfunctional pathways are extracted.Using Kolmogorov-Smirnov statistic, we
calculate the therapeutic scores of drugs based on the 12 tissue-specific
pathways. Finally, after filtering by Comparative Toxicogenomics Database (CTD)
benchmark,we get 3 therapeutic drugs and 3 ag-gravating drugs for HCC.
Furthermore, we validate the6potentially related drugs of HCC by
analyz-ingtheiroverlaps with drug indications reported in PubMed literatures,
and also making CMapprofile similarity analysis and KEGG enrichment analysis
based on their targets. All analysis results sug-gest thatour approach is
effective and accurate for discovering novel therapeutic options for HCC and it
can be easily extended to other diseases.More importantly, our method can
clearly distin-guish therapeutic and aggravating drugsforHCC, which also can be
used to indicateunmarked drug-disease associations in CTD as positive or
negative.
