Abstract
Multi-omics approaches are revolutionizing cancer research and treatment by integrating single-modality omics methods, such as the transcriptome, genome, epigenome, epi-transcriptome, proteome, metabolome, and developing omics (single-cell omics). These technologies enable a deeper understanding of cancer and provide personalized treatment strategies. However, challenges such as standardization and appropriate methods for funneling complex information into clinical consequences remain. The tumor microenvironment (TME) is a complex system containing cancer cells, immune cells, stromal cells, and secreted molecules. To overcome these challenges, researchers can establish standardized protocols for data collection, analysis, and interpretation. Collaborations and data sharing among research groups and institutions can create a comprehensive and standardized multi-omics database, facilitating cross-validation and comparison of results. Multi-omics profiling enables in-depth characterization of diversified tumor types and better reveal their function in cancer immune escape. Datasets play a fundamental role in multi-omics approaches, with artificial intelligence and machine learning (AI) rapidly advancing in multi-omics for cancer.