Traditional　survival　analysis　models,　such　as　the　COX　proportional　hazards　model,　face　challenges　in　processing　multimodal　data,　identifying　nonlinear　relationships,　and　recognizing　complex　data　patterns.　The　rise　of　deep　learning,　particularly　Transformers　and　variational　autoencoders　(VAEs),　has　showcased　its　potential　in　analyzing　cancer　multi-omics　data　comprehensively.　However,　many　individual　cancer　datasets　suffer　from　limited　sample　sizes,　preventing　some　deep　learning　models　from　extracting　in-depth　data　representations　and　resulting　in　subpar　performance.　To　address　this　issue,　we　advocate　the　adoption　of　pre-training　and　fine-tuning　techniques,　which　effectively　mitigate　performance　deficits　due　to　sparse　cancer　data　samples.　We　introduce　TransVCOX,　a　deep　survival　analysis　model　integrating　a　Transformer　encoder　with　VAE.　This　model　leverages　pre-training　and　fine-tuning　approaches　to　predict　patients＇　survival　risk　using　cancer　multi-omics　data.　Rigorous　tests　on　eight　unique　cancer　datasets　from　TCGA　revealed:　(1)　TransVCOX　outperforms　other　deep　learning　and　conventional　COX　models.　(2)　Pre-training　significantly　reduces　model　overfitting　and　enhances　performance.　(3)　VAE　encoding,　compared　to　positional　encoding,　offers　a　richer　decision-making　foundation.　(4)　The　performance　boost　doesn＇t　linearly　correlate　with　the　addition　of　Transformer　blocks.　These　findings　underline　TransVCOX＇s　promising　capability　for　predicting　cancer　patients＇　survival　risks　using　multi-omics　data.　The　implementation　can　be　accessed　at　https://github.com/wenwenmin/TransVCOX.　©　2023　IEEE.