Search Results (13)

Breast cancer is the most prevalent solid tumor diagnosed in women worldwide, remaining a leading cause of cancer-related mortality. Among its subtypes, triple-negative breast cancer (TNBC) is characterized by high aggressiveness and heterogeneity, accounting for approximately 90% of breast cancer-related deaths. Receptor tyrosine kinases (RTKs), such as epidermal growth factor receptor (EGFR) and the insulin-like growth factor I receptor (IGF-IR), are critical cell growth and survival regulators, with their dysregulation closely related to therapy resistance in breast cancer. Studies on RTK targeting have shown promise, and recently attention has shifted toward developing more physiologically relevant preclinical models. Unlike traditional two-dimensional (2D) cell cultures, 3D models such as spheroids better mimic the complex nature of the tumor microenvironment (TME), offering a more accurate representation of tumor behavior and progression. This study utilized both 2D and 3D culture models to assess the effects of EGFR and IGF-IR inhibition, individually and in combination, in two TNBC cell lines with distinct metastatic potential. The results demonstrate that both receptors play critical roles in regulating key cellular functions, including migration, expression of epithelial-to-mesenchymal transition (EMT) markers and matrix metalloproteinases (MMPs). The use of 3D spheroid models enabled the evaluation of additional functional properties, such as spheroid growth and dissemination, revealing treatment-dependent responses to combined receptor inhibition. Overall, this dual-model approach underscores the importance of incorporating 3D culture systems in preclinical cancer research and provides new insights into the regulatory roles of EGFR and IGF-IR in TNBC progression. Full article

Basic and translational cancer biology research requires model systems that recapitulate the features of human tumors. While two-dimensional (2D) cell cultures have been foundational and allowed critical advances, they lack the organizational complexity, cellular interactions, and extracellular matrix present in vivo. Mouse models have thus remained the gold standard for studying cancer. In addition to high cost and low throughput, mouse models can also suffer from reduced tumor heterogeneity and species-specific differences. Three-dimensional (3D) culture models have emerged as a key intermediary between 2D cell lines and mouse models, with lower cost and greater flexibility than mouse models and a more accurate representation of the tumor microenvironment than 2D cell lines. In neuroblastoma, an aggressive childhood cancer, 3D models have been applied to study drug responses, cell motility, and tumor–matrix interactions. Recent advances include the integration of immune cells for immunotherapy studies, mesenchymal stromal cells for tumor–stroma interactions, and bioprinted systems to manipulate matrix properties. This review examines the use of 3D culture systems in neuroblastoma, highlighting their advantages and limitations while emphasizing their potential to bridge gaps between in vitro, preclinical, and clinical applications. By improving our understanding of neuroblastoma biology, 3D models hold promise for advancing therapeutic strategies and outcomes in this childhood cancer. Full article

The MCF-7R breast cancer cell line, developed by treating the parental MCF-7 cells with increasing doses of doxorubicin, serves as a model for studying acquired multidrug resistance (MDR). MDR is a major challenge in cancer therapy, often driven by overexpression of the efflux pump P-glycoprotein (P-gp) and epigenetic modifications. While many P-gp inhibitors show promise in vitro, their nonspecific effects on the efflux pump limit in vivo application. Curcumin, a natural compound with pleiotropic action, is a nontoxic P-gp inhibitor capable of modulating multiple pathways. To explore curcumin’s molecular effects on MCF-7R cells, we analyzed the expression of genes involved in DNA methylation and transcription regulation, including ABCB1/MDR1. Reduced representation bisulfite sequencing further unveiled key epigenetic changes induced by curcumin. Our findings indicate that curcumin treatment not only modulates critical cellular processes, such as ribosome biogenesis and cytoskeletal dynamics, but also reverses the resistant phenotype, toward that of sensitive cells. This study highlights curcumin’s potential as an adjuvant therapy to overcome chemoresistance, offering new avenues for pharmacological strategies targeting epigenetic regulation to re-sensitize resistant cancer cells. Full article

Background: Tyrosyl-DNA phosphodiesterase 1 (Tdp1) repairs damages in DNA induced by abortive topoisomerase 1 activity; however, maintenance of genetic integrity may sustain cellular division of neoplastic cells. It follows that Tdp1-targeting chemical inhibitors could synergize well with existing chemotherapy drugs to deny cancer growth; therefore, identification of Tdp1 inhibitors may advance precision medicine in oncology. Objective: Current computational research efforts focus primarily on molecular docking simulations, though datasets involving three-dimensional molecular structures are often hard to curate and computationally expensive to store and process. We propose the use of simplified molecular input line entry system (SMILES) chemical representations to train supervised machine learning (ML) models, aiming to predict potential Tdp1 inhibitors. Methods: An open-sourced consensus dataset containing the inhibitory activity of numerous chemicals against Tdp1 was obtained from Kaggle. Various ML algorithms were trained, ranging from simple algorithms to ensemble methods and deep neural networks. For algorithms requiring numerical data, SMILES were converted to chemical descriptors using RDKit, an open-sourced Python cheminformatics library. Results: Out of 13 optimized ML models with rigorously tuned hyperparameters, the random forest model gave the best results, yielding a receiver operating characteristics-area under curve of 0.7421, testing accuracy of 0.6815, sensitivity of 0.6444, specificity of 0.7156, precision of 0.6753, and F1 score of 0.6595. Conclusions: Ensemble methods, especially the bootstrap aggregation mechanism adopted by random forest, outperformed other ML algorithms in classifying Tdp1 inhibitors from non-inhibitors using SMILES. The discovery of Tdp1 inhibitors could unlock more treatment regimens for cancer patients, allowing for therapies tailored to the patient’s condition. Full article

Understanding the dynamics of gene regulatory networks (GRNs) across diverse cell types poses a challenge yet holds immense value in unraveling the molecular mechanisms governing cellular processes. Current computational methods, which rely solely on expression changes from bulk RNA-seq and/or scRNA-seq data, often result in high rates of false positives and low precision. Here, we introduce an advanced computational tool, DeepIMAGER, for inferring cell-specific GRNs through deep learning and data integration. DeepIMAGER employs a supervised approach that transforms the co-expression patterns of gene pairs into image-like representations and leverages transcription factor (TF) binding information for model training. It is trained using comprehensive datasets that encompass scRNA-seq profiles and ChIP-seq data, capturing TF-gene pair information across various cell types. Comprehensive validations on six cell lines show DeepIMAGER exhibits superior performance in ten popular GRN inference tools and has remarkable robustness against dropout-zero events. DeepIMAGER was applied to scRNA-seq datasets of multiple myeloma (MM) and detected potential GRNs for TFs of RORC, MITF, and FOXD2 in MM dendritic cells. This technical innovation, combined with its capability to accurately decode GRNs from scRNA-seq, establishes DeepIMAGER as a valuable tool for unraveling complex regulatory networks in various cell types. Full article

P53 overexpression plays a critical role in cancer pathogenesis by disrupting the intricate regulation of cellular proliferation. Despite its firmly established function as a tumor suppressor, elevated p53 levels can paradoxically contribute to tumorigenesis, influenced by factors such as exposure to carcinogens, genetic mutations, and viral infections. This phenomenon is observed across a spectrum of cancer types, including bladder (BLCA), ovarian (OV), cervical (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and uterine corpus endometrial carcinoma (UCEC). This broad spectrum of cancers is often associated with increased aggressiveness and recurrence risk. Effective therapeutic strategies targeting tumors with p53 overexpression require a comprehensive approach, integrating targeted interventions aimed at the p53 gene with conventional modalities such as chemotherapy, radiation therapy, and targeted drugs. In this extensive study, we present a detailed analysis shedding light on the multifaceted role of TP53 across various cancers, with a specific emphasis on its impact on disease-free survival (DFS). Leveraging data from the TCGA database and the GTEx dataset, along with GEPIA, UALCAN, and STRING, we identify TP53 overexpression as a significant prognostic indicator, notably pronounced in prostate adenocarcinoma (PRAD). Supported by compelling statistical significance (p < 0.05), our analysis reveals the distinct influence of TP53 overexpression on DFS outcomes in PRAD. Additionally, graphical representations of overall survival (OS) underscore the notable disparity in OS duration between tumors exhibiting elevated TP53 expression (depicted by the red line) and those with lower TP53 levels (indicated by the blue line). The hazard ratio (HR) further emphasizes the profound impact of TP53 on overall survival. Moreover, our investigation delves into the intricate TP53 protein network, unveiling genes exhibiting robust positive correlations with TP53 expression across 13 out of 27 cancers. Remarkably, negative correlations emerge with pivotal tumor suppressor genes. This network analysis elucidates critical proteins, including SIRT1, CBP, p300, ATM, DAXX, HSP 90-alpha, Mdm2, RPA70, 14-3-3 protein sigma, p53, and ASPP2, pivotal in regulating cell cycle dynamics, DNA damage response, and transcriptional regulation. Our study underscores the paramount importance of deciphering TP53 dynamics in cancer, providing invaluable insights into tumor behavior, disease-free survival, and potential therapeutic avenues. Full article

In the rapidly evolving landscape of cancer drug research, cellular lines serve as invaluable tools for understanding drug-sensitive and drug-resistant tumors. The computational representation of cellular lines is usually based on genomic profiling, even though this method cannot be applied in a large scale. This study introduces a novel approach to the computational representation of cellular lines using text mining techniques. By meticulously extracting and analyzing textual data from the scientific literature, we developed a computational representation of these cellular lines. Our methodology encompassed advanced Natural Language Processing (NLP) for text extraction and machine learning models for predictive analysis. We achieved a comprehensive description of each cellular line. To validate our findings, we generated a distance matrix for all cellular lines, leading to the construction of a dendrogram representing cellular line relationships. This dendrogram shows a resemblance with the established cell line ontology from CLO. Our results bridge the gap between cellular line representation and text mining, offering a robust computational model that can significantly impact cancer drug research. Full article

Hydrogel-based artificial scaffolds are essential for advancing cell culture models from 2D to 3D, enabling a more realistic representation of physiological conditions. These hydrogels can be customized through crosslinking to mimic the extracellular matrix. While the impact of extracellular matrix scaffolds on cell behavior is widely acknowledged, mechanosensing has become a crucial factor in regulating various cellular functions. cancer cells’ malignant properties depend on mechanical cues from their microenvironment, including factors like stiffness, shear stress, and pressure. Developing hydrogels capable of modulating stiffness holds great promise for better understanding cell behavior under distinct mechanical stress stimuli. In this study, we aim to 3D culture various cancer cell lines, including MCF-7, HT-29, HeLa, A549, BT-474, and SK-BR-3. We utilize a non-degradable hydrogel formed from alpha acrylate-functionalized dendritic polyglycerol (dPG) and thiol-functionalized 4-arm polyethylene glycol (PEG) via the thiol-Michael click reaction. Due to its high multivalent hydroxy groups and bioinert ether backbone, dPG polymer was an excellent alternative as a crosslinking hub and is highly compatible with living microorganisms. The rheological viscoelasticity of the hydrogels is tailored to achieve a mechanical stiffness of approximately 1 kPa, suitable for cell growth. Cancer cells are in situ encapsulated within these 3D network hydrogels and cultured with cell media. The grown tumor spheroids were characterized by fluorescence and confocal microscopies. The average grown size of all tumoroid types was ca. 150 µm after 25 days of incubation. Besides, the stability of a swollen gel remains constant after 2 months at physiological conditions, highlighting the nondegradable potential. The successful formation of multicellular tumor spheroids (MCTSs) for all cancer cell types demonstrates the versatility of our hydrogel platform in 3D cell growth. Full article

Neutrophils are important cellular mediators of injury and repair in diseases including ischemic heart disease, atherosclerosis, and sepsis. Myeloperoxidase-derived (MPO)-oxidants released from neutrophils are potential mediators of endothelial injury in disease. MPO-derived HOCl attacks plasmalogen phospholipid to liberate 2-chlorofatty aldehyde (2-ClFALD). Both 2-ClFALD and its oxidation product, 2-chlorofatty acid (2-ClFA), are electrophilic lipids, and both probably react with proteins through several mechanisms. In the present study, we investigate protein modification specifically by 2-ClFALD under non-reducing conditions (e.g., without stabilizing Schiff base bonds), which likely reflects nucleophilic targeting of the electrophilic chlorinated carbon. Protein modification by the ω-alkyne analog of 2-chlorohexadecanal (2-ClHDA), 2-ClHDyA, was compared to that with the ω-alkyne analog of 2-chlorohexadecanoic acid (2-ClHA), 2-ClHyA, in multiple cell lines, which demonstrated 2-ClFALD preferentially modifies proteins compared to 2-ClFA. The 2-ClHDyA modified proteins from EA.hy926 cells and human lung microvascular endothelial cells analyzed by shotgun proteomics and over-representation analysis included adherens junction, cell adhesion molecule binding, and cell substrate junction enrichment categories. It is possible that proteins in these groups may have roles in previously described 2-ClFALD-elicited endothelial barrier dysfunction. Full article

Methylglyoxal (MGO) is a highly reactive cellular metabolite that glycates lysine and arginine residues to form post-translational modifications known as advanced glycation end products. Because of their low abundance and low stoichiometry, few studies have reported their occurrence and site-specific locations in proteins. Proteomic analysis of WIL2-NS B lymphoblastoid cells in the absence and presence of exogenous MGO was conducted to investigate the extent of MGO modifications. We found over 500 MGO modified proteins, revealing an over-representation of these modifications on many glycolytic enzymes, as well as ribosomal and spliceosome proteins. Moreover, MGO modifications were observed on the active site residues of glycolytic enzymes that could alter their activity. We similarly observed modification of glycolytic enzymes across several epithelial cell lines and peripheral blood lymphocytes, with modification of fructose bisphosphate aldolase being observed in all samples. These results indicate that glycolytic proteins could be particularly prone to the formation of MGO adducts. Full article

Biological systems are dynamic systems featuring two very common characteristics; Initial conditions and progression over time. Conceptualizing this on tumour models it can lead to important conclusions about disease progression, as well as the disease’s “starting point”. In the present study we tried to answer two questions: (a) which are the evolving properties of proliferating tumour cells that started from different initial conditions and (b) we have attempted to prove that cell proliferation follows chaotic orbits and it can be described by the use of Poincaré maps. As a model we have used the acute lymphoblastic leukemia cell line CCRF-CEM. Measurements of cell population were taken at certain time points every 24 h or 48 h. In addition to the population measurements flow cytometry studies have been conducted in order to examine the apoptotic and necrotic rate of the system and also the DNA content of the cells as they progress through. The cells exhibited a proliferation rate of nonlinear nature with aperiodic oscillatory behavior. In addition to that, the (positive) Lyapunov indices and the Poincaré representations in phase-space that we performed confirmed the presence of chaotic orbits. Several studies have dealt with the complex dynamic behaviour of animal populations, but few with cellular systems. This type of approach could prove useful towards the understanding of leukemia dynamics, with particular interest in the understanding of leukemia onset and progression. Full article

Background: Chronic skin wounds are a growing financial burden for healthcare providers, causing discomfort/immobility to patients. Whilst animal chronic wound models have been developed to allow for mechanistic studies and to develop/test potential therapies, such systems are not good representations of the human chronic wound state. As an alternative, human chronic wound fibroblasts (CWFs) have permitted an insight into the dysfunctional cellular mechanisms that are associated with these wounds. However, such cells strains have a limited replicative lifespan and therefore a limited reproducibility/usefulness. Objectives: To develop/characterise immortalised cell lines of CWF and patient-matched normal fibroblasts (NFs). Methods and Results: Immortalisation with human telomerase resulted in both CWF and NF proliferating well beyond their replicative senescence end-point (respective cell strains senesced as normal). Gene expression analysis demonstrated that, whilst proliferation-associated genes were up-regulated in the cell lines (as would be expected), the immortalisation process did not significantly affect the disease-specific genotype. Immortalised CWF (as compared to NF) also retained a distinct impairment in their wound repopulation potential (in line with CWF cell strains). Conclusions: These novel CWF cell lines are a credible animal alternative and could be a valuable research tool for understanding both the aetiology of chronic skin wounds and for therapeutic pre-screening. Full article

Plant phenolics have shown to activate apoptotic cell death in different tumourigenic cell lines. In this study, we evaluated the effects of juniper berry extract (Juniperus communis L.) on p53 protein, gene expression and DNA fragmentation in human neuroblastoma SH-SY5Y cells. In addition, we analyzed the phenolic composition of the extract. We found that juniper berry extract activated cellular relocalization of p53 and DNA fragmentation-dependent cell death. Differentially expressed genes between treated and non-treated cells were evaluated with the cDNA-RDA (representational difference analysis) method at the early time point of apoptotic process when p53 started to be activated and no caspase activity was detected. Twenty one overexpressed genes related to cellular stress, protein synthesis, cell survival and death were detected. Interestingly, they included endoplasmic reticulum (ER) stress inducer and sensor HSPA5 and other ER stress-related genes CALM2 and YKT6 indicating that ER stress response was involved in juniper berry extract mediated cell death. In composition analysis, we identified and quantified low concentrations of fifteen phenolic compounds. The main groups of them were flavones, flavonols, phenolic acids, flavanol and biflavonoid including glycosides of quercetin, apigenin, isoscutellarein and hypolaetin. It is suggested that juniper berry extract induced the p53-associated apoptosis through the potentiation and synergism by several phenolic compounds. Full article