搜索结果：Oncogenesis

Understanding how glioblastoma (GBM) emerges from initially healthy glial tissue requires models that integrate bioelectrical, metabolic, and multicellular dynamics. This work introduces an ASAL-inspired agent-based framework that simulates bioelectric state transitions in glial cells as a function of mitochondrial efficiency (Meff), ion-channel conductances, gap-junction coupling, and ROS dynamics. Using a 64x64 multicellular grid over 60,000 simulation steps, we show that reducing Meff below a critical threshold (~0.6) drives sustained depolarization, ATP collapse, and elevated ROS, reproducing key electrophysiological signatures associated with GBM. We further apply evolutionary optimization (genetic algorithms and MAP-Elites) to explore resilience, parameter sensitivity, and the emergence of tumor-like attractors. Early evolutionary runs converge toward depolarized, ROS-dominated regimes characterized by weakened electrical coupling and altered ionic transport. These results highlight mitochondrial dysfunction and disrupted bioelectric signaling as sufficient drivers of malignant-like transitions and provide a computational basis for probing the bioelectrical origins of oncogen

Identifying driver genes is crucial for understanding oncogenesis and developing targeted cancer therapies. Driver discovery methods using protein or pathway networks rely on traditional network science measures, focusing on nodes, edges, or community metrics. These methods can overlook the high-dimensional interactions that cancer genes have within cancer networks. This study presents a novel method using Persistent Homology to analyze the role of driver genes in higher-order structures within Cancer Consensus Networks derived from main cellular pathways. We integrate mutation data from six cancer types and three biological functions: DNA Repair, Chromatin Organization, and Programmed Cell Death. We systematically evaluated the impact of gene removal on topological voids ($β_2$ structures) within the Cancer Consensus Networks. Our results reveal that only known driver genes and cancer-associated genes influence these structures, while passenger genes do not. Although centrality measures alone proved insufficient to fully characterize impact genes, combining higher-order topological analysis with traditional network metrics can improve the precision of distinguishing between driver

Tumour heterogeneity is increasingly recognized as a major obstacle to therapeutic success across neuro-oncology. Gliomas are characterised by distinct combinations of genetic and epigenetic alterations, resulting in complex interactions across multiple molecular pathways. Predicting disease evolution and prescribing individually optimal treatment requires statistical models complex enough to capture the intricate (epi)genetic structure underpinning oncogenesis. Here, we formalize this task as the inference of distinct patterns of connectivity within hierarchical latent representations of genetic networks. Evaluating multi-institutional clinical, genetic, and outcome data from 4023 glioma patients over 14 years, across 12 countries, we employ Bayesian generative stochastic block modelling to reveal a hierarchical network structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH- wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma, IDH- mutant. Our findings illuminate the complex dependence between features across the genetic landscape of brain tumours, and show that generative network models reveal distinct signatures of survival with b

The cell cycle is tightly regulated by cyclins and their catalytic moieties, the cyclin-dependent kinases (CDKs). Cyclin D1, in association with CDK4/6, acts as a mitogenic sensor and integrates extracellular mitogenic signals and cell cycle progression. When deregulated (overexpressed, accumulated, inappropriately located), cyclin D1 becomes an oncogene and is recognized as a driver of solid tumors and hemopathies. Recent studies on the oncogenic roles of cyclin D1 reported non-canonical functions dependent on the partners of cyclin D1 and its location within tumor cells or tissues. Support for these new functions was provided by various mouse models of oncogenesis. Finally, proteomic and transcriptomic data identified complex cyclin D1 networks. This review focuses on these aspects of cyclin D1 pathophysiology, which may be crucial for targeted therapy.

Tumor cells with two nuclei (binucleated cells, BiNC) or more nuclei (multinucleated cells, MuNC) indicate an increased amount of cellular genetic material which is thought to facilitate oncogenesis, tumor progression and treatment resistance. In canine cutaneous mast cell tumors (ccMCT), binucleation and multinucleation are parameters used in cytologic and histologic grading schemes (respectively) which correlate with poor patient outcome. For this study, we created the first open source data-set with 19,983 annotations of BiNC and 1,416 annotations of MuNC in 32 histological whole slide images of ccMCT. Labels were created by a pathologist and an algorithmic-aided labeling approach with expert review of each generated candidate. A state-of-the-art deep learning-based model yielded an $F_1$ score of 0.675 for BiNC and 0.623 for MuNC on 11 test whole slide images. In regions of interest ($2.37 mm^2$) extracted from these test images, 6 pathologists had an object detection performance between 0.270 - 0.526 for BiNC and 0.316 - 0.622 for MuNC, while our model archived an $F_1$ score of 0.667 for BiNC and 0.685 for MuNC. This open dataset can facilitate development of automated image

The glutamate metabotropic receptor 1 (GRM1) drives oncogenesis when aberrantly activated in melanoma and several other cancers. Metabolomics reveals that patient-derived xenografts with GRM1-positive melanoma tumors exhibit elevated plasma glutamate levels associated with metastatic melanoma in vivo. Stable isotope tracing and GCMS analysis determined that cells expressing GRM1 fuel a substantial fraction of glutamate from glycolytic carbon. Stimulation of GRM1 by glutamate leads to activation of mitogenic signaling pathways, which in turn increases the production of glutamate, fueling autocrine feedback. Implementing a rational drug-targeting strategy, we critically evaluate metabolic bottlenecks in vitro and in vivo. Combined inhibition of glutamate secretion and biosynthesis is an effective rational drug targeting strategy suppressing tumor growth and restricting tumor bioavailability of glutamate.

Massive stars in their final stages of collapse radiate most of their binding energy in the form of MeV neutrinos. The recoil atoms that they produce in elastic scattering off nuclei in organic tissue create radiation damage which is highly effective in the production of irreparable DNA harm, leading to cellular mutation, neoplasia and oncogenesis. Using a conventional model of the galaxy and of the collapse mechanism, the periodicity of nearby stellar collapses and the radiation dose are calculated. The possible contribution of this process to the paleontological record of mass extinctions is examined.

Background: Radiotherapy outcomes are usually predicted using the Linear Quadratic model. However, this model does not integrate complex features of tumor growth, in particular cell cycle regulation. Methods: In this paper, we propose a multiscale model of cancer growth based on the genetic and molecular features of the evolution of colorectal cancer. The model includes key genes, cellular kinetics, tissue dynamics, macroscopic tumor evolution and radiosensitivity dependence on the cell cycle phase. We investigate the role of gene-dependent cell cycle regulation in the response of tumors to therapeutic irradiation protocols. Results: Simulation results emphasize the importance of tumor tissue features and the need to consider regulating factors such as hypoxia, as well as tumor geometry and tissue dynamics, in predicting and improving radiotherapeutic efficacy. Conclusion: This model provides insight into the coupling of complex biological processes, which leads to a better understanding of oncogenesis. This will hopefully lead to improved irradiation therapy.

A groundbreaking superconducting X-ray spectrometer has begun operation at BESSY II, giving Europe its first TES-based system and boosting photon detection efficiency by up to 1,000 times。 The advance enables scientists to explore atomically thin materials, nanostructures, and ultra-dilute samples with remarkable speed and sensitivity

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