With　the　development　of　biotechnology,　a　large　amount　of　multi-omics　data　have　been　collected　for　precision　medicine.　There　exists　multiple　graph-based　prior　biological　knowledge　about　omics　data,　such　as　gene-gene　interaction　networks.　Recently,　there　has　been　an　increasing　interest　in　introducing　graph　neural　networks　(GNNs)　into　multi-omics　learning.　However,　existing　methods　have　not　fully　exploited　these　graphical　priors　since　none　have　been　able　to　integrate　knowledge　from　multiple　sources　simultaneously.　To　solve　this　problem,　we　propose　a　multi-omics　data　analysis　framework　by　incorporating　multiple　prior　knowledge　into　graph　neural　network　(MPK-GNN).　To　the　best　of　our　knowledge,　this　is　the　first　attempt　to　introduce　multiple　prior　graphs　into　multi-omics　data　analysis.　Specifically,　the　proposed　method　contains　four　parts:　(1)　a　feature-level　learning　module　to　aggregate　information　from　prior　graphs;　(2)　a　projection　module　to　maximize　the　agreement　among　prior　networks　by　optimizing　a　contrastive　loss;　(3)　a　sample-level　module　to　learn　a　global　representation　from　input　multi-omics　features;　(4)　a　task-specific　module　to　flexibly　extend　MPK-GNN　for　various　downstream　multi-omics　analysis　tasks.　Finally,　we　verify　the　effectiveness　of　the　proposed　multi-omics　learning　algorithm　on　the　cancer　molecular　subtype　classification　task.　Experimental　results　show　that　MPK-GNN　outperforms　other　state-of-the-art　algorithms,　including　multi-view　learning　methods　and　multi-omics　integrative　approaches.　IEEE