Colon　cancer　is　the　third　most　common　cancer　and　one　of　the　leading　causes　of　cancer-related　death　in　the　world.　Therefore,　identification　of　biomarkers　with　potential　in　recognizing　the　biological　characteristics　is　a　key　problem　for　early　diagnosis　of　colon　cancer　patients.　In　this　study,　we　used　a　random　forest　approach　to　discover　biomarkers　based　on　a　set　of　oligonucleotide　microarray　data　of　colon　cancer.　Real-time　PCR　was　used　to　validate　the　related　expression　levels　of　biomarkers　selected　by　our　approach.　Furthermore,　ROC　curves　were　used　to　analyze　the　sensitivity　and　specificity　of　each　biomarker　in　both　training　and　test　sample　sets.　Finally,　we　analyzed　the　clinical　significance　of　each　biomarker　based　on　their　differential　expression.　A　single　classifier　consisting　of　4　genes　(IL8,　WDR77,　MYL9　and　VIP)　was　selected　by　random　forests　with　an　average　sensitivity　and　specificity　of　83.75　and　76.15%.　The　differential　expression　levels　of　each　biomarker　was　validated　by　real-time　PCR　in　48　test　colon　cancer　samples　compared　to　the　matched　normal　tissues.　Patients　with　high　expression　of　IL8　and　WDR77,　and　low　expression　of　MYL9　and　VIP　had　a　significantly　reduced　median　survival　rate　compared　to　colon　cancer　patients.　The　results　indicate　that　our　approach　can　be　employed　for　biomarker　identification　based　on　microarray　data.　These　4　genes　identified　by　our　approach　have　the　potential　to　act　as　clinical　biomarkers　for　the　early　diagnosis　of　colon　cancer.