Abstract
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.