  Hepatocellular carcinoma (HCC) is the most common primary liver malignancy
and is a leading cause of cancer-related death worldwide. However, cure is not
possible with currently used therapies, and there is not so much approved
targeted therapy for HCC despite numerous attempts and clinical trials. So, it
is essential to identify additional therapeutic strategies to block the growth
of HCC tumors. As a cancer disease, it is associated with aberrant genomic and
transcriptional landscapes. We sought to use a systematic drug repositioning
bioinformatics approach to identify novel candidate drugs to treat HCC, which
considers not only aberrant genomic information, but also the changes of
transcriptional landscapes. First, we screen the collection of HCC feature
genes that frequently mutated in most samples of HCC based on human mutation
data. Then, the gene expression data of HCC in TCGA are combined to classify
the kernel genes of HCC. Finally, the therapeutic score (TS) of each drug is
calculated based on the kolmogorov-smirnov statistical method. Using this
strategy, we identified five drugs that associated with HCC, including three
drugs that could treat HCC and two drugs that might have side-effect on HCC. In
addition, we also make Connectivity Map (CMap) profiles similarity analysis and
KEGG enrichment analysis on drug targets. All these findings suggest that our
approach is effective for accurate discovering novel therapeutic options for
HCC and easily to be extended to other tumors.
