Biological multi-omics approaches to next-generation biomarkers in immune-related disorders and malignancies: An...

Cancer and autoimmune diseases are major global health challenges characterized by molecular and clinical heterogeneity. Traditional single-analyte biomarkers often lack the sensitivity and specificity required for early detection and personalized therapy, highlighting the need for robust next-generation biomarkers (NGBs). This review provides a concise overview of NGBs in cancer and autoimmune diseases, emphasizing multi-omics integration and artificial intelligence (AI)-driven approaches shaping precision diagnostics and therapeutics. A literature search of PubMed, Scopus, and Web of Science over the last 15 years focused on genomics, transcriptomics, spatial transcriptomics, proteomics, metabolomics, and Microbiomics, particularly studies combining multi-omics datasets with AI/machine learning. Multi-omics and AI reveal dynamic molecular signatures-circulating tumor DNA, microRNAs, long non-coding RNAs, proteins, metabolites, and immune profiles. Single-cell and spatial analyses uncover cellular heterogeneity and tissue context, while proteomics, metabolomics, and microbiomics provide functional insights, enhancing disease detection, patient stratification, and therapy monitoring. Challenges include assay standardization, inter-patient variability, and regulatory hurdles. Multi-omics and AI-powered NGBs promise to transform precision diagnostics and personalized therapy, requiring continued research, standardization, and global collaboration.