Front Cell Dev Biol. 2026 Sep 14;14:1878092. doi: 10.3389/fcell.2026.1878092. eCollection 2026.

ABSTRACT

Liquid biopsy has emerged as a powerful non-invasive tool in precision oncology, providing real-time insights into tumor evolution, host immune responses, and dynamic changes in the tumor immune microenvironment. By enabling minimally invasive sampling, it can overcome several limitations of conventional tissue biopsy. This review summarizes the major biological sources and components of liquid biopsy, including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and circulating immune cells, and discusses their value as dynamic indicators of interactions during cancer immunotherapy. Particular attention is given to immune-related biomarkers associated with immune checkpoints, immunosuppressive mechanisms, and immune escape, including circulating immune cell populations, and inflammatory cytokine profiles. We further examine their potential applications in predicting treatment response, monitoring immune-related adverse events, assessing minimal residual disease, and detecting acquired resistance. In addition, recent technological advances that are accelerating the clinical translation of liquid biopsy are highlighted, including multi-omics integration, microfluidic platforms. These approaches have improved the sensitivity, accuracy, and multidimensional characterization of tumor- and immune-derived biomarkers. Nevertheless, biological heterogeneity, limited assay standardization, and the lack of large-scale prospective validation studies continue to restrict widespread clinical implementation. Overall, immune-related biomarkers detected through liquid biopsy offer considerable potential for the longitudinal monitoring of the tumor immune microenvironment and may improve non-invasive cancer diagnosis, therapeutic monitoring, and personalized immunotherapy in the era of precision oncology.

PMID:42807637 | PMC:PMC13617286 | DOI:10.3389/fcell.2026.1878092

Nat Commun. 2026 Aug 29;17(1):10311. doi: 10.1038/s41467-026-77187-8.

ABSTRACT

Type 2 diabetes (T2D) subgroups defined by clinical variables differ in disease progression and treatment response. To uncover potential molecular drivers of this heterogeneity, we performed a multi-omics analysis of 826 drug-naïve T2D patients from two phase 3 trials of the insulin sensitizer chiglitazar. Here we show that severe insulin-resistant diabetes (SIRD) is characterized by distinct miRNA profiles (e.g., miR-122-5p) correlated with liver injury, and metabolic shifts in amino acids and primary bile acids. Mild obesity-related diabetes (MOD) showed the lowest level of phenylacetylglutamine, a metabolite known to promote cardiovascular disease. Severe insulin-deficient diabetes (SIDD) exhibited high pancreas-specific miR-7-5p, while mild age-related diabetes (MARD) presented the mildest abnormalities. Finally, integrating these multi-omics signatures into machine learning models enhanced prediction of insulin sensitizer efficacy over clinical data alone. Our findings define the distinct molecular signatures of T2D subgroups, facilitating the prediction of heterogeneous treatment responses and supporting personalized clinical management.

PMID:42805981 | PMC:PMC13620142 | DOI:10.1038/s41467-026-77187-8