Conclusion
The novelty of our study is the identification of genetic signatures
through classification rules for SNPs in angiogenesis and ADME genes
associated with sorafenib responses in HCC patients, through which it
may be possible to personalize prescription. The application of GRS
could allow a better stratification of patients. In addition, the
network analysis conducted in this study supports the association of
8/12 analyzed genes in topological key points involved in several common
biological pathways correlated to HCC and sorafenib. However, our study
has some limitations. The sample size was relatively small, and further
investigations in a larger sample size may be needed. The opportunity to
test the classification rules on predictive biomarkers of response to
sorafenib in an independent and larger validation set would give more
robustness to our findings. Therefore, our findings had an exploratory
aim and are intended as a “proof-of-concept” research to be further
validated in a larger dataset to allow sorafenib-tailored prescriptions
through predictive biomarkers of response and outcome in HCC patients.
The involvement of seed genes in multiple biological pathways related to
sorafenib and HCC, as well as the common interactions of ADH1A ,CYP26A1 , VEGF-A , and VEGF-C in signal transduction
pathways, should allow future studies on the simultaneous targeting of
different signaling pathways or common downstream proteins involved in
HCC control and sorafenib response with the aim of personalizing
treatment for this still uncurable disease.