Ther Clin Risk Manag. 2025 Dec 2;21:1615-1636. doi: 10.2147/TCRM.S542338. eCollection 2025.

ABSTRACT

Lung cancer remains a leading cause of cancer-related mortality worldwide, largely due to challenges in its early detection and effective management. Despite advances in treatment modalities, the complex nature of lung cancer, characterized by its molecular heterogeneity and resistance mechanisms, underscores the need for innovative approaches. Cell-free RNA (cfRNA) has emerged as a promising biomarker with significant clinical applications in lung cancer diagnosis, monitoring, and precision medicine. We explore key themes including the utility of cfRNA in early detection, differentiation between benign and malignant lung nodules, molecular subtyping, and real-time therapeutic monitoring. Advances in liquid biopsy technologies, particularly non-invasive cfRNA analysis, provide dynamic means of tracking tumor evolution. cfRNA biomarkers such as miRNA, long non-coding RNAs, and circular RNAs offer unique insights into tumor biology, paving the way for personalized treatment strategies. Further, we discuss the application of cutting-edge technologies such as AI-driven analytics, next-generation sequencing, and multi-omics integration, which are enhancing the clinical utility of cfRNA in identifying treatment resistance and improving outcomes in immunotherapy, targeted therapy, and chemotherapy. The review addresses significant challenges facing cfRNA applications, including pre-analytical variability, technical limitations in detection methods, economic constraints, and the lack of standardization in clinical protocols. Through multidisciplinary collaborations and standardized methodologies, significant progress can be made toward integrating cfRNA into routine clinical practice. Emphasis is placed on future research directions, which include validating cfRNA biomarkers across diverse populations, streamlining workflows, and addressing scalability issues for real-world applications. This comprehensive exploration positions cfRNA at the forefront of innovations in lung cancer management, offering a pathway for improved diagnostic accuracy and individualized care.

PMID:41367889 | PMC:PMC12682701 | DOI:10.2147/TCRM.S542338

Nat Commun. 2025 Nov 26. doi: 10.1038/s41467-025-66644-5. Online ahead of print.

ABSTRACT

Single-cell technologies have transformed our understanding of cellular heterogeneity through multimodal data acquisition. However, robust cell alignment remains a major challenge for data integration and harmonization, including batch correction, label transfer, and multi-omics integration. Many existing methods constrain alignment based on rigid feature-wise distance metrics, limiting their ability to capture accurate cell correspondence across diverse cell populations and conditions. We introduce scGALA, a graph-based learning framework that redefines cell alignment by combining graph attention networks with a score-driven, task-independent optimization strategy. scGALA constructs enriched graphs of cell-cell relationships by integrating gene expression profiles with auxiliary information, such as spatial coordinates, and iteratively refines alignment via self-supervised graph link prediction, where a deep neural network is trained to identify and reinforce high-confidence correspondences across datasets. In extensive benchmarks, scGALA identifies over 25 percent more high-confidence alignments without compromising accuracy. By improving the core step of cell alignment, scGALA serves as a versatile enhancer for a wide range of single-cell data integration tasks.

PMID:41298467 | DOI:10.1038/s41467-025-66644-5