{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T04:49:49Z","timestamp":1791521389359,"version":"4.3.4"},"reference-count":99,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background: Small-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 (EGFR) alterations, histologic transformation from EGFR-mutant non-small cell lung cancer (NSCLC) to SCLC, and the influence of distinct transcriptional subtypes on clinical behavior. Methods: This 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. Results: The 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. Conclusion: AI-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.<\/jats:p>","DOI":"10.3390\/cancers18193159","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:33:12Z","timestamp":1790699592000},"page":"3159","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Driven Multi-Omics Approaches for Deciphering Small-Cell Lung Cancer Heterogeneity Using Nucleic-Acid-Based Features"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9278-6204","authenticated-orcid":false,"given":"Saman","family":"Zeeshan","sequence":"first","affiliation":[{"name":"Department of Biomedical and Health Informatics, School of Medicine, The University of Missouri-Kansas City, Kansas City, MO 64110, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6583-526X","authenticated-orcid":false,"given":"Caila","family":"McVey","sequence":"additional","affiliation":[{"name":"Department of Biomedical and Health Informatics, School of Medicine, The University of Missouri-Kansas City, Kansas City, MO 64110, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shafaq","family":"Majeed","sequence":"additional","affiliation":[{"name":"Department of Bio Sciences, COMSATS University, Islamabad 45550, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7065-1699","authenticated-orcid":false,"given":"Zeeshan","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA"},{"name":"Department of Medicine, Robert Wood Johnson Medical School, Rutgers Health, New Brunswick, NJ 08903, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5887-1685","authenticated-orcid":false,"given":"Mark","family":"Nichols","sequence":"additional","affiliation":[{"name":"Department of Biomedical and Health Informatics, School of Medicine, The University of Missouri-Kansas City, Kansas City, MO 64110, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1002\/cncr.29098","article-title":"Small cell lung cancer: Where do we go from here?","volume":"121","author":"Byers","year":"2015","journal-title":"Cancer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1093\/annonc\/mdj114","article-title":"EGFR mutation in gefitinib-responsive small-cell lung cancer","volume":"17","author":"Okamoto","year":"2006","journal-title":"Ann. 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