Gene　expression　profiles　of　14　common　tumors　and　their　counterpart　normal　tissues　were　analyzed　with　machine　learning　methods　to　address　the　problem　of　selection　of　tumor-specific　genes　and　analysis　of　their　differential　expressions　in　tumor　tissues.　First,　a　variation　of　the　Relief　algorithm,　＂RFE_Relief　algorithm＂　was　proposed　to　learn　the　relations　between　genes　and　tissue　types.　Then,　a　support　vector　machine　was　employed　to　find　the　gene　subset　with　the　best　classification　performance　for　distinguishing　cancerous　tissues　and　their　counterparts.　After　tissue-specific　genes　were　removed,　cross　validation　experiments　were　employed　to　demonstrate　the　common　deregulated　expressions　of　the　selected　gene　in　tumor　tissues.　The　results　indicate　the　existence　of　a　specific　expression　fingerprint　of　these　genes　that　is　shared　in　different　tumor　tissues,　and　the　hallmarks　of　the　expression　patterns　of　these　genes　in　cancerous　tissues　are　summarized　at　the　end　of　this　paper.