{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T17:10:07Z","timestamp":1788801007787,"version":"build-2803163510"},"reference-count":118,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,6]],"date-time":"2026-09-06T00:00:00Z","timestamp":1788652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Curr. Issues Mol. Biol.","CIMB"],"abstract":"<jats:p>Cancer remains a major global health challenge, requiring innovative diagnostic and therapeutic strategies. Cell culture models are essential tools in cancer research. While traditional two-dimensional (2D) cultures are widely used because of their accessibility and simplicity, they inadequately represent the tumor microenvironment. This review provides a molecular perspective on the biological differences between 2D and 3D cancer models, emphasizing extracellular matrix signaling, mechanotransduction, metabolic adaptation, tumor heterogeneity, and therapeutic resistance rather than a general comparison of culture systems. It discusses the limitations of 2D models and highlights the advantages of 3D systems, including spheroids and organoids, for more accurately recapitulating the tumor microenvironment and improving the predictive value of anticancer drug testing. This narrative review synthesizes current evidence on the roles of 2D and 3D cell culture methods in oncology. The literature was searched in PubMed, Google Scholar, and Web of Science from 2015 to 2026 using keywords including \u201c2D cell culture,\u201d \u201c3D cell culture,\u201d \u201cspheroids,\u201d \u201corganoids,\u201d and \u201ccancer.\u201d Studies were eligible for inclusion if they addressed cancer research using 2D or 3D cell culture models and provided relevant experimental, preclinical, or translational evidence, particularly regarding tumor microenvironment, extracellular matrix signaling, cellular interactions, therapeutic response, or molecular mechanisms. Studies unrelated to cancer or 2D\/3D culture models, duplicate records, and publications lacking relevant primary data were excluded. Study selection was performed by screening titles and abstracts followed by full-text assessment according to the predefined eligibility criteria. Seminal studies were also included when considered relevant to the conceptual framework of the review. Findings were synthesized narratively without quantitative analysis. 2D cultures limit cell\u2013cell and cell\u2013extracellular matrix interactions, reducing the applicability of findings to in vivo conditions. In contrast, 3D cultures better reproduce oxygen and nutrient gradients and cellular interactions, providing a more realistic representation of the tumor microenvironment. Recent advances in 3D systems have improved predictive accuracy for drug screening and personalized medicine. Both 2D and 3D culture methods possess unique strengths and limitations. Used in complementary ways, they can improve the translation of in vitro findings to in vivo and clinical applications, accelerating progress in cancer research and therapeutic development.<\/jats:p>","DOI":"10.3390\/cimb48090914","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T16:30:02Z","timestamp":1788798602000},"page":"914","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Current Perspectives on 2D and 3D Cell Culture Models in Cancer Research: Molecular Determinants of Tumor Biology and Therapeutic Response"],"prefix":"10.3390","volume":"48","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3521-3383","authenticated-orcid":false,"given":"Selma","family":"Y\u0131ld\u0131r\u0131m","sequence":"first","affiliation":[{"name":"Department of Biotechnology, Kafkas University, 36000 Kars, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9053-7730","authenticated-orcid":false,"given":"Juan","family":"G\u00f3mez-Salgado","sequence":"additional","affiliation":[{"name":"Department of Medicine and Public Health, Faculty of Labour Sciences, University of Huelva, 21007 Huelva, Spain"},{"name":"Safety and Health Postgraduate Programme, Universidad Esp\u00edritu Santo, Guayaquil 092301, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8012-8652","authenticated-orcid":false,"given":"Luis","family":"El Khoury-Moreno","sequence":"additional","affiliation":[{"name":"Department of Stomatology, Faculty of Odontology, University of Seville, 41009 Seville, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9467-3761","authenticated-orcid":false,"given":"\u00d6zkan","family":"\u00d6zden","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, Kafkas University, 36000 Kars, T\u00fcrkiye"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,6]]},"reference":[{"key":"ref_1","first-page":"10","article-title":"Cancer statistics, 2025","volume":"75","author":"Siegel","year":"2025","journal-title":"CA Cancer J. 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