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MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier (MSC) for Lung Cancer Screening
Published on: October 26, 2017
EV-Mediated Oncogenic Regulation in Lung Cancer and Clinical Translation: From Liquid Biopsy to Targeted Delivery
Jianjun Fang1,2,3, Jing Li1,2,3, Lun Yan1,2,3
1Medical Center of Hematology, Xinqiao Hospital, Army Medical University, Chongqing, 400037, People's Republic of China.
Abstract:
Lung cancer (LC) remains the leading cause of cancer-associated mortality globally. Delayed diagnosis, therapeutic resistance and high postoperative recurrence are major hurdles that compromise its clinical outcomes. Extracellular vesicles (EVs) are cell-secreted nanoscale lipid vesicles that mediate intercellular crosstalk via the transfer of bioactive cargo, such as nucleic acids and proteins. In the lung cancer microenvironment, EVs remodel core signaling axes including PI3K/AKT and NF-κB, consequently driving hallmark malignant phenotypes: angiogenesis, distant metastasis, immune evasion and multidrug resistance. Boasting robust physicochemical stability, complete molecular cargo signatures and minimally invasive sampling feasibility, EVs represent ideal tumor biomarkers for liquid biopsy. Additionally, their intrinsic low immunogenicity renders EVs versatile nanovehicles capable of amplifying anti-tumor effects elicited by conventional radiotherapy, chemotherapy and immunotherapy. However, substantial inherent heterogeneity, a lack of unified standards for EVs isolation and characterization, obstacles to scalable production and inadequate tumor-targeting efficiency collectively impede their clinical translation. This review systematically dissects the molecular mechanisms through which EVs orchestrate lung cancer malignant progression, comprehensively outlines state-of-the-art progress of EVs applications in liquid biopsy-based early screening, prognostic stratification, targeted drug delivery and cellular immunotherapy, thoroughly discusses prevailing translational bottlenecks, and envisions future directions of EVs in precision oncology for lung cancer. The work offers theoretical guidance for developing next-generation EV-centric diagnostic and therapeutic platforms.
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