Search Results (606)

Triple-negative breast cancer (TNBC) exhibits diverse histological and molecular characteristics. TNBC patients also have the poorest prognoses among those with various breast cancer subtypes, and no effective treatment strategy has been established for TNBC beyond non-specific chemotherapy. Recent studies have reported that the dysregulation of miRNAs is associated with tumor behavior, prognosis, and treatment responses in TNBC patients. Therefore, this study was conducted to identify miRNAs and key target proteins potentially associated with TNBC prognosis. Fresh-frozen tissue from relapsing and non-relapsing TNBC cases was examined for differentially expressed miRNAs using the Affymetrix GeneChip miRNA 4.0 array, while target genes and proteins were predicted using the miRwalk 2.0 database. The clinical significance of each differentially expressed miRNA was evaluated using the BreastMark database. Additional bioinformatics analyses were conducted to reveal associations with tumor-related signaling pathways; these analyses included protein–protein interaction network construction and Kyoto Encyclopedia of Genes and Genomes pathway annotation. Gene chip analysis identified three upregulated miRNAs (miR-500a, miR-501-3p, and miR-502-3p) and two downregulated miRNAs (miR-6798-5p and miR-7150) in patients with recurrence, and further bioinformatics analyses revealed that target proteins were significantly associated with cell cycle pathways. In addition, low expression of the miR-500a target protein UBE2Z was significantly associated with a poor prognosis. The expression levels of miR-500a and UBE2Z might be useful prognostic biomarkers in TNBC. Full article

The mucus layer covering the gastrointestinal tract forms a specialised interface where mucins, microbes, and extracellular vesicles create a dynamic, self-regulating ecosystem. Here, we introduce the concept of the muco-microbiotic layer as an integrated eco-physiological system that maintains mucosal homeostasis through coordinated structural, metabolic, and immune functions. The MuMi layer varies regionally in its biochemical composition, microbial inhabitants, and environmental parameters—from the acidic stomach to the anaerobic colon—thereby generating distinct niches for microbial colonisation and metabolite production. We summarise current evidence on how mucin glycans, mucus-associated microbiota, and vesicle-mediated signalling sustain barrier integrity, nutrient flux, and immune tolerance. Perturbations in any of these components lead to barrier failure, microbial encroachment, and inflammation, contributing to a broad spectrum of disorders, including gastritis, inflammatory bowel disease, colorectal cancer, and metabolic syndrome. Methodological advances such as organoid and mucus-on-chip models, spatial multi-omics, and vesiculomics are now enabling site-specific analyses of this complex system. Conceptually, defining the mucus, microbiota, and vesicular compartments as a single MuMi layer provides a new framework for understanding mucosal physiology and pathophysiology, emphasising the interdependence between structure and function. Integrating this perspective into experimental and clinical research may open new avenues for diagnostics and therapies targeting mucosal health. Full article

Microfluidic cell culture systems and organ-on-a-chip platforms provide powerful tools for modeling physiological processes, disease progression, and drug responses under controlled microenvironmental conditions. These technologies rely on diverse cell culture methodologies, including 2D and 3D culture formats, spheroids, scaffold-based systems, hydrogels, and organoid models, to recapitulate tissue-level functions and generate rich, multiparametric datasets through high-resolution imaging, integrated sensors, and biochemical assays. The heterogeneity and volume of these data introduce substantial challenges in pre-processing, feature extraction, multimodal integration, and biological interpretation. Artificial intelligence (AI), particularly machine learning and deep learning, offers solutions to these analytical bottlenecks by enabling automated phenotyping, predictive modeling, and real-time control of microfluidic environments. Recent advances also highlight the importance of technical frameworks such as dimensionality reduction, explainable feature selection, spectral pre-processing, lightweight on-chip inference models, and privacy-preserving approaches that support robust and deployable AI–microfluidic workflows. AI-enabled microfluidic and organ-on-a-chip systems now span a broad application spectrum, including cancer biology, drug screening, toxicity testing, microbial and environmental monitoring, pathogen detection, angiogenesis studies, nerve-on-a-chip models, and exosome-based diagnostics. These platforms also hold increasing potential for precision medicine, where AI can support individualized therapeutic prediction using patient-derived cells and organoids. As the field moves toward more interpretable and autonomous systems, explainable AI will be essential for ensuring transparency, regulatory acceptance, and biological insight. Recent AI-enabled applications in cancer modeling, drug screening, etc., highlight how deep learning can enable precise detection of phenotypic shifts, classify therapeutic responses with high accuracy, and support closed-loop regulation of microfluidic environments. These studies demonstrate that AI can transform microfluidic systems from static culture platforms into adaptive, data-driven experimental tools capable of enhancing assay reproducibility, accelerating drug discovery, and supporting personalized therapeutic decision-making. This narrative review synthesizes current progress, technical challenges, and future opportunities at the intersection of AI, microfluidic cell culture platforms, and advanced organ-on-a-chip systems, highlighting their emerging role in precision health and next-generation biomedical research. Full article

Cancer stem cells (CSCs) remain challenging to isolate and characterize because of their plastic phenotype. To overcome this issue, we used a microfluidic lab-on-a-chip analysis approach based on ultra-high frequency dielectophoresis (UHF-DEP) to measure the dielectrophoretic signature of colorectal cancer cells. We demonstrated that CSCs exhibit a distinct and lower frequency signature than differentiated cancer cells. Extracellular vesicles (EVs) released by tumor cells are implicated in tumor progression and metastasis. As CSC-derived EVs carry a more aggressive cargo, we hypothesized that treating differentiated colorectal cancer cells with these vesicles might affect their phenotype which would be detected by our lab on a chip. Indeed, the dielectrophoretic signature of cells treated with those EVs was altered in comparison to untreated cells, even in cases where no detectable biological changes were observed. Compared to conventional approaches using biomarkers to characterize CSCs, this UHF-DEP lab on a chip is a label-free method providing rapid and relevant results. Such a method could be useful in the clinic for the early detection of CSCs in the tumor mass, as well as for monitoring CSC-derived EVs in the bloodstream in order to study responses to therapy and prevent relapses. Full article

Organoids are self-organizing three-dimensional (3D) cellular structures derived from stem cells. They can mimic the anatomical and functional properties of real organs. They have transformed in vitro disease modeling by closely replicating the structural and functional characteristics of human tissues. The complexity and variability of organoid-derived data pose significant challenges for analysis and clinical translation. Artificial Intelligence (AI) has emerged as a crucial enabler, offering scalable and high-throughput tools for interpreting imaging data, integrating multi-omics profiles, and guiding experimental workflows. This review aims to discuss how AI is reshaping organoid-based research by enhancing morphological image analysis, enabling dynamic modeling of organoid development, and facilitating the integration of genomics, transcriptomics, and proteomics for disease classification. Moreover, AI is increasingly used to support drug screening and personalize therapeutic strategies by analyzing patient-derived organoids. The integration of AI with organoid-on-chip systems further allows for real-time feedback and physiologically relevant modeling. Drawing on peer-reviewed literature from the past decade, Furthermore, CNNs have been used to analyze colonoscopy and histopathological images in colorectal cancer with over 95% diagnostic accuracy. We examine key tools, innovations, and case studies that illustrate this evolving interface. As this interdisciplinary field matures, the future of AI-integrated organoid platforms depends on establishing open data standards, advancing algorithms, and addressing ethical and regulatory considerations to unlock their clinical and translational potential. Full article

Optical fiber biosensors have evolved into powerful tools for non-invasive biomedical analysis. While foundational principles are well-established, recent years have marked a paradigm shift, driven by advancements in nanomaterials, fabrication techniques, and data processing. This review provides a focused overview of these emerging trends, critically analyzing the innovations that distinguish the current generation of optical fiber biosensors from their predecessors. We begin with a concise summary of fundamental sensing principles, including Surface Plasmon Resonance (SPR) and Fiber Bragg Gratings (FBGs), before delving into the latest breakthroughs. Key areas of focus include integrating novel 2D materials and nanostructures to dramatically enhance sensitivity and advancing synergy with Lab-on-a-Chip (LOC) platforms. A significant portion of this review is dedicated to the rapid expansion of clinical applications, particularly in early cancer detection, infectious disease diagnostics, and continuous glucose monitoring. We highlight the pivotal trend towards wearable and in vivo sensors and explore the transformative role of artificial intelligence (AI) and machine learning (ML) in processing complex sensor data to improve diagnostic accuracy. Finally, we address the persistent challenges—biocompatibility, long-term stability, and scalable manufacturing—that must be overcome for widespread clinical adoption and commercialization, offering a forward-looking perspective on the future of this dynamic field. Full article

Endothelial-to-mesenchymal transition (EndMT) is a cellular program implicated in fibrosis, vascular remodeling, and the tumor microenvironment across multiple organs. We synthesize mechanistic pathways including TGF-β/SMAD, non-canonical (MAPK, PI3K/AKT, Rho/ROCK), Notch, and Wnt/β-catenin cascades. Their crosstalk with hypoxia, inflammatory cues, and epigenetic mechanisms can drive loss of endothelial identity and acquisition of mesenchymal characteristics. We outline disease contexts in the heart, lungs, kidneys, liver, central nervous system, and cancer, highlighting context-dependent contributory roles of EndMT. Therapeutically, we review pathway-targeted agents, epigenetic inhibitors, microRNA-based strategies, antibodies/biologics, small molecules and natural compounds, and cell- and gene-based interventions. Finally, we outline a translational roadmap that pairs patient-derived iPSC/organoid and organ-on-a-chip platforms to stratify EndMT states and prioritize targets. We also explore combination regimens that integrate multi-pathway modulation with epigenetic and immune approaches, aiming to deliver clinically meaningful anti-fibrotic benefits while better preserving physiological signaling. Full article

Cancer drug screening is shifting from low-predictive, reductionist assays to human-relevant, data-integrated platforms. This review synthesizes preclinical strategies using a unified lens—Principle, Advantages, Limitations, and Clinical Application—to enable like-for-like comparison. We first appraise traditional two-dimensional (2D) monolayers and animal models, noting scalability and historical utility alongside constrained translational fidelity. We then evaluate advanced systems—patient-derived organoids (PDOs), patient-derived xenografts (PDXs), and organ-on-a-chip—that better recapitulate architecture, microenvironmental cues, and pharmacodynamics (PD), yet face trade-offs in throughput, timelines, costs, and standardization. Functional genomic screens (CRISPR/RNAi) and large-scale pharmacogenomics are summarized as engines for mechanism-based target discovery and resistance mapping, while AI-enabled modeling supports response prediction, biomarker development, and rational combinations. Finally, we discuss trial designs (basket/umbrella), drug repurposing lessons, and regulatory momentum for new approach methodologies. Across platforms, we emphasize cross-model validation, dataset harmonization, and clinically anchored endpoints as prerequisites for real-world impact. We conclude with pragmatic guidance for matching screening modality to study goals, sample constraints, and decision timelines to accelerate precision oncology. Full article

The high failure rate of anticancer drugs in clinical trials highlights the need for preclinical models that accurately reproduce the structural, biochemical, and mechanical complexity of human tumors. Conventional two-dimensional cultures and animal models often lack the physiological complexity required to predict clinical outcomes, driving the development of three-dimensional systems that better emulate the tumor microenvironment. Among these, microfluidic-based spheroid models have emerged as powerful tools for cancer research and drug screening. By integrating 3D spheroids with microfluidics, these platforms allow precise control of nutrient flow, oxygen gradients, shear stress, and interstitial pressure, while supporting co-culture with stromal, immune, and endothelial cells. Such systems enable the investigation of drug response, angiogenesis, metastasis, and immune interactions under dynamic and physiologically relevant conditions. This review summarizes recent advances in microfluidic spheroid models for cancer, covering both carcinomas and sarcomas, with an emphasis on device design, biomaterial integration, and translational validation. Key challenges remain, including technical complexity, scalability constraints, and the absence of standardized protocols. Overall, the merger of microfluidic technology with 3D spheroid culture provides a promising pathway toward predictive, ethical, and personalized preclinical testing, bridging the gap between in vitro modeling and clinical oncology. Full article

Traditional animal-based preclinical models, including xenografts and genetically engineered mice, have been used for assessing pharmacodynamics, toxicity, efficacy, and safety for decades. Despite their limited ability to mimic human tumor heterogeneity, immune interactions, and microenvironmental complexity, over 90% of oncology candidates that succeed in animal studies fail in clinical trials. The New Approach Methodologies (NAMs), which include patient-derived organoids, organ-on-chip platforms, and AI-driven computational models, provide human-relevant solutions that can improve predictive validity, mechanistic insight, and ethics. Through these technologies, it will be possible to replicate tumor biology specific to patients, to support co-clinical trial designs, and to facilitate biomarker discovery while reducing animal testing. Several recent regulatory reforms, including the Food and Drug Administration (FDA) Modernization Act 2.0 and the European Medicines Agency’s NAM qualification framework, have established clear pathways for the integration of validated NAMs into preclinical drug evaluation. Critically evaluating the scientific rationale, comparative performance, and regulatory context of key NAM platforms in oncology, this review highlights opportunities for synergistic integration, technical refinement, and global harmonization in order to accelerate the development of clinically effective cancer therapeutics based on preclinical findings. Full article

Hematological malignancies such as acute myeloid leukemia (AML), chronic myeloid leukemia (CML), lymphomas, and multiple myeloma remain difficult to model ex vivo because conventional two-dimensional (2D) cultures and murine systems fail to reproduce the spatial, metabolic, vascular, and immune complexity of human bone marrow and lymphoid niches. Recent advances in three-dimensional (3D) platforms—including spheroids, engineered organoid-like marrow models, and microfluidic niche-on-a-chip systems—now allow for a more physiological replication of stromal, endothelial, and immune interactions that drive resistance and relapse. In this review, we introduce explicit definitions distinguishing spheroids, organoid-like constructs, true hematopoietic organoids, and microfluidic devices to establish a unified framework for hematologic 3D modeling. We synthesize applications across AML, CML, lymphoma, and myeloma, highlighting mechanistic insights, strengths, and limitations unique to each disease. Finally, we outline a translational roadmap that integrates bioprinting, perfusable vasculature, immune reconstitution, and AI-driven analytics toward next-generation patient-specific platforms. These innovations position 3D marrow-mimetic systems as essential tools for precision oncology in blood cancers. Full article

The master transcription factors that control cell adaptation under hypoxia are known as hypoxia-inducible factors or HIFs. HIF-2α is the second isoform, which has been studied less extensively, and its expression is limited to particular cell types and is associated with increased malignancy in tumors. Herein, we investigate the interaction of HIF-2α with Ataxin-10, an intracellular protein involved in cell survival and differentiation, as well as the mechanism and the effects of this interaction in cervical cancer (HeLa) and glioma (U-87MG) cells. The interaction was investigated by LC-MS/MS proteomic analysis, immunoprecipitation, and immunoblotting. HIF-2 transcriptional activity was measured by luciferase assays and quantitative RT-PCR of target genes specific to HIF-2. The mechanism of interaction was investigated using immunofluorescence microscopy analysis, subcellular fractionation, siRNA-mediated silencing, quantitative RT-PCR, in vitro binding assays, and chromatin immunoprecipitation (ChIP). Ataxin interacts specifically with HIF2α and binds to the HIF-2α carboxyterminal activation domain. The interaction of HIF-2α with Ataxin-10 increases HIF-2-transcriptional activity under hypoxia through the enhancement of HIF-2α binding to chromatin in Hypoxia Response Elements of HIF-2 specific target genes SERPINE1, CITED-2, and SOD-2. These new data highlight a novel HIF-2 fine-tuning mechanism and may offer new, effective therapeutic approaches for treating cancerous tumors. Full article

While immune escape and physical invasion are two key pathways in tumor development, traditional methods for analyzing their exosomal markers are often complex and face identification bias. Microfluidic technology offers significant advantages for non-invasive liquid biopsy, particularly in the analysis of tumor progression markers carried by exosomes. Here, we developed an integrated microfluidic system for the sensitive, accurate, totally on-chip exosome isolation and automatic quantification of tumor progression markers PD-L1 and MMP9. This platform leverages microfluidic design principles for efficient sample mixing and monodisperses microbeads for precise analysis, allowing for complete processing within 40 min. The system’s high efficiency and precision are further enhanced by a lightweight YOLOv5-based positional migration strategy that automates fluorescence quantification. Validation using four different cell lines demonstrated distinct exosomal protein signatures with a low detection limit of 12.58 particles/μL. This innovative microfluidic chip provides a sensitive and easy-to-handle tool for exosomal marker analysis, holding great potential for cancer identification and personalized therapy guidance in the era of point-of-care testing (POCT). Full article

In this work, we used a label-free biosensor that provides optical readouts to perform continuous detection of human interleukin 8 (IL-8), which is especially overexpressed in certain cancers and, thus, could be an effective biomarker for cancer prognosis estimation and therapy evaluation. For this purpose, we engineered a compact, portable, and easy-to-assemble biosensing module device. It combines a fluidic chip for reagent flow, a biosensing chip for signal transduction, and an optical readout head based on fiber optics in a single module. The biosensing chip is based on independent arrays of resonant nanopillar transducer (RNP) networks. We integrated the biosensing chip with the RNPs facing down in a simple and rapidly fabricated polydimethyl siloxane (PDMS) microfluidic chip, with inlet and outlet channels for the sample flowing through the RNPs. The RNPs were vertically oriented from the backside through an optical fiber mounted on a holder head fabricated ad hoc on polytetrafluoroethylene (PTFE). The optical fiber was connected to a visible spectrometer for optical response analysis and consecutive biomolecule detection. We obtained a sensogram showing anti-IL-8 immobilization and the specific recognition of IL-8. This unique portable and easy-to-handle module can be used for biomolecule detection within minutes and is particularly suitable for in-line sensing of physiological and biomimetic organ-on-a-chip systems. Cancer biomarkers’ continuous monitoring arises as an efficient and non-invasive alternative to classical tools (imaging, immunohistology) for determining clinical prognostic factors and therapeutic responses to anticancer drugs. In addition, the multiplexed layout of the optical transducers and the simplicity of the monolithic sensing module yield potential high-throughput screening of a combination of different biomarkers, which, together with other medical exams (such as imaging and/or patient history), could become a cutting-edge technology for further and more accurate diagnosis and prediction of cancer and similar diseases. Full article

Background: Cervical cancer remains a major global health burden, and treatment failure due to radioresistance and secondary drug resistance severely limits clinical outcomes. Enhancer of zeste homolog 2 (EZH2) is a key epigenetic regulator implicated in tumor progression. This study aimed to determine whether EZH2-mediated PTEN silencing drives afatinib resistance via AKT activation in radiation-resistant cervical cancer cells. Methods: A radioresistant cervical cancer cell line (HeLaR) was established following cumulative irradiation (70 Gy). Cell viability, clonogenic survival, methylation-specific PCR (MSP), chromatin immunoprecipitation (ChIP), and Western blot analyses were conducted. EZH2 (Dznep; tazemetostat), PI3K, and AKT inhibitors were tested in combination with afatinib. A xenograft mouse model was used for in vivo validation. Results: HeLaR cells exhibited upregulation of EZH2 and H3K27me3, downregulation of PTEN, and sustained AKT activation. EZH2 inhibition restored PTEN expression, attenuated AKT phosphorylation, and re-sensitized cells to afatinib. MSP and ChIP confirmed EZH2-mediated PTEN promoter silencing. PI3K inhibition reproduced these effects, whereas ERK inhibition had minimal impact. In xenograft models, combined treatment with Dznep and afatinib significantly suppressed tumor growth compared to single agents. Conclusions: EZH2-driven PTEN suppression promotes AKT-dependent afatinib resistance in radiation-resistant cervical cancer. Targeting the EZH2–PTEN–AKT axis may provide a potential therapeutic approach to mitigate combined radioresistance and chemoresistance in recurrent cervical cancer, although further preclinical and clinical validation is required. Full article