ReviewCancers2026
AI-Driven Multi-Omics Approaches for Deciphering Small-Cell Lung Cancer Heterogeneity Using Nucleic-Acid-Based Features.
Review in Cancers, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
Funding
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Abstract
backgroundSmall-cell lung cancer (SCLC) is a rare but highly aggressive malignancy defined by rapid progression, early metastasis, and profound therapeutic resistance. Its extensive heterogeneity across genomic, transcriptomic, epigenetic, proteomic, and microenvironmental levels continues to undermine precision medicine. Emerging evidence highlights the complex role of epidermal growth factor receptor (
methodsThis review synthesizes current literature encompassing nucleic-acid-based determinants of resistance, SCLC molecular architecture, transformation biology, and subtype evolution, integrating findings from genomic, transcriptomic, epigenetic, proteomic, imaging, and clinical studies. We additionally evaluated artificial intelligence (AI) and machine learning (ML) methodologies with potential to unify multi-omics, radiomics, and clinical variables to model subtype plasticity, clonal evolution, and therapy-induced rewiring.
resultsThe evidence demonstrates that traditional single-layer analyses are insufficient to capture the multiscale architecture of SCLC biology or the nonlinear interactions driving resistance, whereas AI-enabled multi-omics approaches offer advantages in enhancing subtype classification, supporting early detection, and improving prediction of therapeutic response. Key barriers such as tissue scarcity, limited biomarker availability, and extreme intratumoral heterogeneity remain significant challenges, though increasingly addressable through integrative computational frameworks.
conclusionAI-driven systems-level integration reframes SCLC as a dynamically evolving, multilayered disease process, where nucleic-acid alterations are central to subtype evolution and treatment resistance, providing a conceptual model to guide biomarker discovery and therapeutic development. By leveraging AI-enabled multi-omics and radiomics, this framework has the potential to advance personalized clinical management and overcome longstanding diagnostic and therapeutic challenges in SCLC.
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