Background:　Recently,　researchers　have　been　attracted　in　identifying　the　crucial　genes　related　to　cancer,　which　plays　important　role　in　cancer　diagnosis　and　treatment.　However,　in　performing　the　cancer　molecular　subtype　classification　task　from　cancer　gene　expression　data,　it　is　challenging　to　obtain　those　significant　genes　due　to　the　high　dimensionality　and　high　noise　of　data.　Moreover,　the　existing　methods　always　suffer　from　some　issues　such　as　premature　convergence.　Methods:　To　address　those　problems,　we　propose　a　new　ant　colony　optimization　(ACO)　algorithm　called　DACO　to　classify　the　cancer　gene　expression　datasets,　identifying　the　essential　genes　of　different　diseases.　In　DACO,　first,　we　propose　the　initial　pheromone　concentration　based　on　the　weight　ranking　vector　to　accelerate　the　convergence　speed;　then,　a　dynamic　pheromone　volatility　factor　is　designed　to　prevent　the　algorithm　from　getting　stuck　in　the　local　optimal　solution;　finally,　the　pheromone　update　rule　in　the　Ant　Colony　System　is　employed　to　update　the　pheromone　globally　and　locally.　To　demonstrate　the　performance　of　the　proposed　algorithm　in　classification,　different　existing　approaches　are　compared　with　the　proposed　algorithm　on　eight　high-dimensional　cancer　gene　expression　datasets.　Results:　The　experiment　results　show　that　the　proposed　algorithm　performs　better　than　other　effective　methods　in　terms　of　classification　accuracy　and　the　number　of　feature　sets.　It　can　be　used　to　address　the　classification　problem　effectively.　Moreover,　a　renal　cell　carcinoma　dataset　is　employed　to　reveal　the　biological　significance　of　the　proposed　algorithm　from　a　number　of　biological　analyses.　Conclusion:　The　results　demonstrate　that　CAPS　may　play　a　crucial　role　in　the　occurrence　and　development　of　renal　clear　cell　carcinoma.