Wide　heterogeneity　exists　in　cancer　patients＇　survival,　ranging　from　a　few　months　to　several　decades.　To　accurately　predict　clinical　outcomes,　it　is　vital　to　build　an　accurate　predictive　model　that　relates　the　patients＇　molecular　profiles　with　the　patients＇　survival.　With　complex　relationships　between　survival　and　high-dimensional　molecular　predictors,　it　is　challenging　to　conduct　nonparametric　modeling　and　irrelevant　predictors　removing　simultaneously.　In　this　article,　we　build　a　kernel　Cox　proportional　hazards　semi-parametric　model　and　propose　a　novel　regularized　garrotized　kernel　machine　(RegGKM)　method　to　fit　the　model.　We　use　the　kernel　machine　method　to　describe　the　complex　relationship　between　survival　and　predictors,　while　automatically　removing　irrelevant　parametric　and　nonparametric　predictors　through　a　LASSO　penalty.　An　efficient　high-dimensional　algorithm　is　developed　for　the　proposed　method.　Comparison　with　other　competing　methods　in　simulation　shows　that　the　proposed　method　always　has　better　predictive　accuracy.　We　apply　this　method　to　analyze　a　multiple　myeloma　dataset　and　predict　the　patients＇　death　burden　based　on　their　gene　expressions.　Our　results　can　help　classify　patients　into　groups　with　different　death　risks,　facilitating　treatment　for　better　clinical　outcomes.