Millions　of　new　cases　of　cancer　are　diagnosed　worldwide　each　year,　making　it　a　serious　public　health　concern.　Developments　in　customized　therapy　and　early　detection　have　significantly　enhanced　treatment　for　and　results　from　cancer.　Therefore,　it　is　important　to　investigate　new　molecular　biomarkers.　In　this　study,　we　created　an　efficient　transient　receptor　potential　melastatin　(TRPM)　family　members-related　TRPM-Score　for　17　solid　tumors.　CCNE1,　produced　from　TRPM-Score,　was　found　to　be　an　exceptional　biomarker　through　several　sophisticated　machine　learning　and　deep　learning　computational　techniques.　TRPM-Score　and　CCNE1　immunotherapeutic　prediction,　immunological　characteristics,　and　predictive　value　were　thoroughly　assessed.　In　most　cancer　types,　CCNE1　was　a　substantially　dangerous　marker.　Additional　in　vitro　tests　validated　CCNE1＇s　immunomodulatory　properties,　demonstrating　that　silencing　impeded　macrophage　movement　and　decreased　PD-L1　expression.　Additionally,　CCNE1　may　accurately　predict　responses　to　cancer　immunotherapy.　These　findings　indicate　that　the　TRPM　family-particularly　CCNE1,　which　is　associated　with　TRPM-is　a　significant　player　in　the　pan-cancer　domain　and　can　be　utilized　as　a　therapeutic　target　and　prognostic　biomarkers,　especially　in　immuno-oncology.　The　thorough　characterization　of　the　TRPM　family　and　the　discovery　of　CCNE1　as　a　crucial　downstream　effector　mark　important　developments　in　our　comprehension　of　pan-cancer　biology.