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Coordinated changes in midkine expression and midkine-associated multiomic profile in glioma microenvironment
Abstract Midkine (MDK), a multifunctional growth factor, has been implicated in promoting tumor progression, yet its role in glioblastoma (GBM) remains insufficiently characterized. To investigate MDK’s function in glioma, we integrated four RNA-Seq datasets into a harmonized cohort of 1,017 adult gliomas, including 256 GBM samples. We complemented this with freshly collected human GBM tissues and matched primary cell cultures to evaluate MDK expression and secretion patterns, further contextualized using single-cell RNA-Seq. Finally, we tested the impact of GBM-derived MDK on macrophage secretome composition to validate our in silico observations. We found that MDK expression increases with tumor grade in IDH wildtype gliomas, accompanied by a shift in isoform proportions favoring the canonical MDK transcript. High MDK expression was associated with poor prognosis specifically in GBM, where the MDK high subgroup comprised 75% of cases. MDK high GBMs exhibited a distinctive multiomic signature, including elevated chemokine and cytokine expression. Functionally, GBM-derived MDK induced macrophages to secrete multiple cytokines and chemokines, suggesting its role in reshaping the tumor microenvironment. Our findings reveal MDK’s previously underappreciated role in GBM aggressiveness and immune modulation, underscoring its potential as a biomarker and actionable therapeutic target for most GBM patients.
Tumor cell-adipocyte gap junctions activate lipolysis and contribute to breast tumorigenesis
Abstract A pro-tumorigenic role for adipocytes has been identified in breast cancer, and reliance on fatty acid catabolism found in aggressive tumors. The molecular mechanisms by which tumor cells coopt neighboring adipocytes, however, remain incompletely understood. Here, we describe a direct interaction linking tumorigenesis to adjacent adipocytes. We examine breast tumors and their normal adjacent tissue from several patient cohorts, patient-derived xenografts, and mouse models, and find that lipolysis and lipolytic signaling are activated in neighboring adipose tissue. We find that functional gap junctions form between breast cancer cells and adipocytes. As a result, cAMP is transferred from breast cancer cells to adipocytes and activates lipolysis in a gap junction-dependent manner. We find that connexin 31 ( GJB3 ) promotes receptor triple negative breast cancer growth and activation of lipolysis in vivo. Thus, direct tumor cell-adipocyte interaction contributes to tumorigenesis and may serve as a new therapeutic target in breast cancer.
Computational optimization of 3D printed bone scaffolds using orthogonal array-driven FEA and neural network modeling
Abstract Today, orthopedic surgeons have been continuously focusing on bone tissue engineering for regenerating damaged bone through the use of biomimetic scaffolds and innovative materials. Hence, this study presents a comprehensive investigation into the optimization of PLA + 3D printed lattice scaffolds for bone tissue engineering applications, emphasizing the role of geometric configuration and processing parameters on mechanical performance. Three distinct lattice geometries such as Lidinoid, Diamond, and Gyroid were developed with varying wall thicknesses (1.0 mm, 1.5 mm, and 2.0 mm) and subjected to compressive loads of 3 kN, 6 kN, and 9 kN. A Taguchi L27 Orthogonal Array was employed to evaluate key mechanical responses, including displacement and strain. Among these configurations, the Gyroid lattice exhibited superior mechanical integrity, demonstrating the least displacement (0.36 mm) and strain (1.2 × 10⁻²) at 3 kN with 2.0 mm thickness, whereas the Lidinoid structure showed the highest deformability. A Back-propagation Artificial Neural Network (BPANN) model was developed to predict scaffold behavior with remarkable accuracy (R² = 0.9991 for displacement, R² = 0.9954 for strain), further Finite Element Analysis (FEA) was conducted to validate both experimental and predicted results. The novelty of this work lies in its integrative, multi-modal approach that synergizes experimental design, machine learning-based predictive modeling, and simulation. The focus of this study is to define a robust framework for optimizing scaffold architecture, with significant implications for enhancing mechanical strength and biological performance in bone healing applications.
Network pharmacology and experimental validation to elucidate the pharmacological mechanisms of Guben Xiezhuo decoction against renal fibrosis
Decoding mechanosensitive genes in cardiac fibroblasts via 3D hydrogel models of fibrosis
Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics
Development and validation of a nomogram-based predictive model for recurrence risk of uterine leiomyoma after myomectomy
Near dipole-dipole interaction induced control of high-precision three-dimensional atom localization via probe absorption in a four-level phase-coherent atomic medium
Tumor treating fields suppress tumor cell growth and induce immunogenic cell death biomarkers in biliary tract cancer cell lines
Abstract Tumor Treating Fields (TTFields) are low-intensity, intermediate-frequency alternating electric fields that exert antimitotic effects on cancer cells. This study is the first to evaluate the in vitro efficacy of TTFields on biliary tract cancer (BTC) cell lines HCCC-9810 and RBE, investigating their sensitivity to TTFields across varying frequencies and electric field intensities (100–200 kHz, 1.3–2.1 V/cm). The results demonstrated that a frequency of 150 kHz induced the most pronounced cytotoxic effects, significantly impairing clonogenicity and migratory capacity while inducing multipolar spindles and microtubule disorganization. Furthermore, TTFields treatment triggered a marked upregulation of immunogenic cell death (ICD) biomarkers, including enhanced surface exposure of calreticulin (CRT) and increased extracellular release of high mobility group box 1 (HMGB1) and Adenosine triphosphate (ATP). These findings suggest that TTFields hold great promise as a therapeutic strategy for BTC. They not only suppress tumor cell proliferation but also promote the release of ICD-associated biomarkers. Nevertheless, this study is limited to preliminary in vitro experiments. To provide insights for the ongoing clinical trial (NCT06611345), further in vivo studies and mechanistic explorations are essential to investigate the potential of combining TTFields with immune checkpoint inhibitor (ICI) therapy in the treatment of BTC.