Search Results (366)

Background/Objectives: Brain metastases (BM) are a major challenge in the treatment of non-small cell lung cancer (NSCLC), particularly among patients with anaplastic lymphoma kinase rearrangements (ALK+ NSCLC), where incidence can reach up to 60% during the course of the disease. This study used in silico systems biology and artificial intelligence-based modeling to investigate the mechanistic effects of brigatinib, a second-generation ALK inhibitor, on metastatic processes in both primary tumors (PT) and established BM. Methods: We applied the Therapeutic Performance Mapping System (TPMS) technology, which integrates systems biology and artificial intelligence, to simulate the impact of brigatinib on metastasis-associated pathways in PT and BM of ALK+ NSCLC patients. Results: In these simulations, brigatinib was predicted to modulate a broad set of proteins implicated in metastasis in both PT and BM, acting mainly through IGF1R, EGFR, FLT3, and ROS1, in addition to its known target ALK. Conclusions: These results suggest brigatinib’s potential to impact key pathways involved in metastatic progression and intracranial disease control. Overall, this study provides insights into brigatinib’s multifaceted role in targeting metastatic processes in ALK+ NSCLC, underscoring its potential benefits in both PT and BM. Nonetheless, further experimental and clinical studies would confirm our results and the potential of in silico models reported here. Full article

Uveal melanoma (UVM) is a rare cancer that represents the second most common melanoma (after the cutaneous) and the most common primary intraocular malignancy in adults. Despite recent advances in the understanding of UVM pathogenesis, its prognosis remains unchanged, with half of patients dying because of liver metastasis. Erythropoietin-producing human hepatocellular receptors (EPHs) constitute the largest known family of tyrosine receptors, and, along with their ligands, EFNs, regulate key physiological processes and are implicated in cancer pathogenesis. In this study, we used open-access web bioinformatics platforms to explore and analyze big datasets provided by The Cancer Genome Atlas (TCGA) UVM cohort of patients. We profiled the genomic alterations present in a subset of UVM patients, highlighting a likely pathogenic deep deletion of EPHA7. Survival analysis showed that overexpression levels of EPHA4, EPHA5, EPHA8, EPHB2, and EFNB2 are significantly associated with poor overall survival. Additionally, high expression levels of EPHA4, EPHA5, EPHA7, EPHA8, EPHB2, EFNA2, and EFNB2 correlate with reduced progression-free interval and disease-free survival. Finally, we identified the EPHs (EPHA2, EPHA4, EPHA8, and EPHB4) and EFNs (EFNA1, EFNA3, EFNA4, and EFNB2) that are significantly overexpressed in the aggressive epithelioid histological subtype and revealed that the majority of EPHs/EFNs are overexpressed in metastatic disease. In conclusion, our results highlight that a subset of EPHs and EFNs may be associated with worse clinical outcomes (EPHA4, EPHA5, EPHA7, EPHA8, EPHB2, EFNA2, and EFNB2), and an aggressive histological subtype (EPHA2, EPHA4, EPHA8, EPHB4, EFNA1, EFNA3, EFNA4, and EFNB2). The potential correlation of these genes with clinicopathological parameters of UVM need to be evaluated and validated with bioinformatic and experimental approaches in well-characterized cohorts of UVM patients. Full article

TMBIM6, a transmembrane BAX inhibitor motif containing six proteins, is correlated with tumor progression and metastasis. While its correlation in several malignancies has been shown, its expression in prostate adenocarcinoma (PRAD) is unclear. In this work, we explored, using integrated bioinformatics, a novel ceRNA network of TMBIM6 involved in PRAD prognosis. According to TCGA and GEO datasets, we proposed a new TMBIM6/hsa-miR-222-3p/DHRS4-AS1 ceRNA axis associated with PRAD prognosis. The network was estimated by differential expression, correlation, and survival analysis. Co-expression analysis was used to identify pathways involved in tumor progression, and immune infiltration analysis suggested that there is a correlation between the expression of TMBIM6 and the abundance of epithelial cells. Overall, these results indicate that the DHRS4-AS1/hsa-miR-222-3p/TMBIM6 axis is involved in the progression of PRAD and may act as a prognostic biomarker. Our results provide a foundation for further experimental validation and potential clinical translation. Full article

Neutrophil extracellular traps (NETs)—webs of DNA and granular proteins expelled by neutrophils—have been implicated in hepatocellular carcinoma (HCC) progression. NETs promote tumor angiogenesis, facilitate invasion/metastasis, and enable immune evasion. Recent data suggest that perioperative factors, including anesthetic techniques, may modulate NET formation (NETosis), thus potentially influencing oncologic outcomes. We conducted a literature review of experimental and clinical studies on NETosis pathophysiology and involvement in HCC and how anesthetic techniques may modulate NET formation and, implicitly, cancer outcomes. NET biomarkers such as citrullinated histone H3 (CitH3), cell-free DNA (cfDNA), and myeloperoxidase–DNA complexes (MPO-DNA) are elevated in HCC patients and correlate with tumor spread, showing diagnostic and prognostic potential. Perioperative anesthetic choices may influence NET activity and immune function. Regional anesthesia and local anesthetics (e.g., lidocaine infusion) attenuate the surgical stress response and preserve anti-tumor immunity. Notably, lidocaine may modulate NET formation and, in a few studies published so far, was shown to reduce postoperative NET markers and other pro-metastatic factors (MMP-9, VEGF) in cancer surgery. In conclusion, NETosis is a process that is strongly implicated in HCC biology. Data published so far suggest that the clinical significance of NETosis may lie in its potential as a marker for disease evaluation and progression, including during the perioperative period. Preliminary results suggest that lidocaine may have a role in decreasing NETosis. Future large randomized trials are needed to exactly quantify these effects. Targeting NETs may be another way to influence HCC outcomes. Full article

Objective: Identifying biomarkers that predict metastatic potential or guide treatment selection is critical for improving breast cancer (BC) management. Previously, we established the Mitotic Network Activity Index (MNAI) as a prognostic marker in BC. Here, we bioinformatically and experimentally evaluated MNAI as a biomarker for metastasis risk and therapeutic response. Methods: We used Kaplan–Meier and Cox proportional hazard regression analyses to assess the association between MNAI and distant metastasis-free survival (DMFS) across 14 published BC datasets. A total of 16 publicly available clinical trial datasets, including the I-SPY trials, were used to evaluate the predictive value of MNAI for treatment response. Additionally, wound-healing and transmembrane assays were conducted to determine the effects of PLK1, CHEK1, and BUB1 inhibition on BC cell migration and invasion. Results: High MNAI levels were strongly associated with shorter DMFS. Multivariate analysis further confirmed MNAI as an independent risk factor for DMFS, beyond estrogen receptor status and PAM50-based molecular subtypes. Functionally, pharmacologic disruption of the mitotic network using PLK1, CHEK1, or BUB1 inhibitors significantly reduced cell migration and invasion in MDA-MB-231 and BT-549 BC cell lines. Moreover, BC cells with high MNAI increased sensitivity to microtubule-targeting agents such as docetaxel, paclitaxel, and ixabepilone but increased resistance to tamoxifen, AKT1/2 inhibitors, and mTOR inhibitors. Consistent with these findings, analysis of 16 clinical trial cohorts revealed that patients with high MNAI achieved higher pathological complete response rates to taxane-containing and ixabepilone-based therapies. Conclusions: Our findings demonstrate the MNAI as a clinically actionable biomarker that can refine risk stratification and guide the selection of targeted or chemotherapy regimens, advancing precision medicine in BC management. Full article

Objectives: Breast cancer is one of the most common malignant tumors among women worldwide, and accurate assessment of axillary lymph node metastasis (ALNM) is crucial for determining treatment strategies. Compared to conventional ultrasound, contrast-enhanced ultrasound (CEUS) can observe blood perfusion and microcirculation changes in primary breast tumors, making it a more ideal diagnostic method for ALNM. Methods: To address the issues that CEUS video sequences require a high level of diagnostic experience from clinicians, and the process is time-consuming and labor-intensive, making it challenging to generate large datasets for deep learning models, we proposed a method for predicting breast cancer ALNM that combines pre-trained fine-tuning with contrastive learning. First, within a text-video contrastive learning framework, we fine-tuned pre-trained weights from a large general dataset using a small-scale proprietary dataset. Second, during the fine-tuning phase, we employed random prompt optimization to specifically adjust the text encoder according to the characteristics of breast CEUS videos, and optimized the extracted text and video representations through an adaptive fine-tuning optimizer to better fit the current data distribution. Results: Experimental results demonstrated that our method achieved a sensitivity of 0.792 and a specificity of 0.8. Conclusions: The study demonstrates that the proposed method effectively leverages CEUS to aid in ALNM diagnosis, highlighting its potential to improve the accuracy of early breast cancer screening and to facilitate the development of more personalized treatment plans for patients. Full article

Background and Methods: Using a nanoparticle-based enrichment (Proteonano) methodology on human plasma samples, we achieved a substantial increase in identified proteins from ~700 to >5000 proteins compared to neat plasma digest. In a small-scale pilot test, we applied this methodology to a small cohort of plasma samples from pancreatic cancer (PC) patients with different disease stages: (I) primary tumor and (II) metastases, and compared them with healthy controls. Most identified proteins are within the Human Plasma Proteome Project (HPPP) database, and more than 300 proteins are on the list of FDA-approved drug targets. Results: We observed a large and significant increase in ribosomal proteins in the plasma of patients with metastatic PC. ADH1C and ADH1B, both members of the alcohol dehydrogenase family, were particularly upregulated in patients with liver metastasis. Fifteen other predicted secreted and/or cell surface–associated proteins with known cancer associations are also significantly altered and would otherwise go undetected in neat, digested plasma. Conclusions: The significant increase in proteome depth allows a strong foundation for future large-scale experimental and comparative analysis. Lastly, similar conclusions could be reached from comparing different mass spectrometers (Orbitrap Astral and Orbitrap Ascend) and columns (depth and throughput) setups on the same dataset, although the depth approach on the newer Orbitrap Astral instrumentations can reveal additional insights in the plasma proteome. Full article

Background: Oral squamous cell carcinoma (OSCC) is a common malignant tumor of the head and neck, and glycolysis plays a key role in its development. In the early stages of the study, we prepared a nanoemulsion containing Astragaloside IV (AS-IV) and Brucea javanica oil (BJO). This Astragaloside–Brucea Javanica Oil nanoemulsion (AS/BJO-NE) demonstrated a stronger inhibitory effect on the proliferation, invasion, and migration of OSCC cells than either AS-IV or BJO alone. Preliminary experiments also showed that AS/BJO-NEs inhibited glycolysis in OSCC cells. The aim of this study was to investigate how AS/BJO-NEs act against OSCC by targeting glycolysis-related genes and pathways. Methods: Prepare AS/BJO-NEs and determine their particle size, PDI, and potential. Network pharmacology and bioinformatics analysis were employed to identify the core genes and pathways of AS/BJO-NEs involved in regulating glycolysis in OSCC. In vitro and vivo experiments were performed to investigate the effects of AS/BJO-NEs on OSCC tumor development and core gene expression levels. Results: Aurora kinase A (AURKA) is a critical target through which AS/BJO-NEs regulate glycolytic metabolism in OSCC. Combined in vitro and in vivo experiments revealed that AS/BJO-NEs suppress glycolysis-related enzymes HK2 and PKM2 through the AURKA/PI3K/AKT/HIF-1α signaling axis, consequently inhibiting OSCC proliferation, invasion, metastasis, and subcutaneous tumorigenesis. Conclusions: Bioinformatics analysis combined with in vitro and vivo experiments demonstrated that AS/BJO-NEs downregulate OSCC glycolysis via the AURKA/PI3K/AKT/HIF-1α pathway at the metabolic level, thereby inhibiting OSCC progression. Elucidation of this mechanism provides theoretical support and experimental evidence for the anti-OSCC effects of AS/BJO-NEs. Full article

Experimental characterisation of ex vivo specimens is limited by specimen availability and high costs, whereas 3D printing provides a cost-effective alternative for producing multiple replicas. This study aimed to develop a methodology for evaluating the individual and combined effects of material composition and geometry on the biomechanical performance of 3D-printed vertebrae. CT scans of healthy human vertebrae and with lytic metastases were segmented to fabricate synthetic models through Digital Anatomy Printing. Three types of 3D-printed models were produced: Healthy vertebrae, Metastatic vertebrae, and Healed vertebrae (metastatic geometry filled with healthy material). All models were tested under axial compression to measure the strength, stiffness, and strain. Repeatability across replicas was assessed as well as comparison of mechanical properties among the different vertebral types. Results showed excellent repeatability, with coefficients of variation below 5% for strength and stiffness-related parameters. The Metastatic models exhibited significant reductions in strength compared to Healthy ones, while stiffness remained similar, consistent with ex vivo data trends. Healed models highlighted the role of material composition in driving mechanical behaviour, independently of geometry. This work provides the first quantitative assessment of 3D-printed vertebrae with metastatic lesions, supporting their future potential as standardised alternatives to cadaveric testing. Full article

Colorectal cancer (CRC) is the third most common cancer worldwide, imposing a significant burden on public health. Despite the use of various therapeutic strategies, the prognosis for patients with metastatic and drug-resistant CRC remains poor, which underscores the need for further investigations into cancer mechanisms to develop more effective treatments. Rodents, particularly mice, are the most frequently used animal models for CRC research. However, as the demand for more precise simulations and higher ethical standards in animal experimentation grows, the applicability of rodent models may face increasing limitations. This review highlights a variety of non-rodent animals, including model organisms such as zebrafish (Danio rerio), fruit flies (Drosophila melanogaster), and Caenorhabditis elegans (C. elegans), as well as the chorioallantoic membrane (CAM) model and mammals such as rabbits (Oryctolagus cuniculus), dogs (Canis lupus familiaris), and pigs (Sus scrofa domesticus), which have been utilized in CRC research. Each of these alternatives offers specific advantages in certain areas of cancer research. Their use has enabled new insights into the mechanisms of carcinogenesis, metastasis, and drug resistance in CRC, as well as the development of novel therapies. Full article

Background: While machine learning has advanced in medicine, its widespread use in clinical applications, especially in predicting breast cancer metastasis, is still limited. We have been dedicated to constructing a deep feed-forward neural network (DFNN) model to predict breast cancer metastasis n years in advance. However, the challenge lies in efficiently identifying optimal hyperparameter values through grid search, given the constraints of time and resources. Issues such as the infinite possibilities for continuous hyperparameters like L1 and L2, as well as the time-consuming and costly process, further complicate the task. Methods: To address these challenges, we developed the Single-Hyperparameter Grid Search (SHGS) strategy, serving as a preselection method before grid search. Our experiments with SHGS applied to DFNN models for breast cancer metastasis prediction focused on analyzing eight target hyperparameters (epochs, batch size, dropout, L1, L2, learning rate, decay, and momentum). Results: We created three figures, each depicting the experimental results obtained from three LSM-I-10+-year datasets. These figures illustrate the relationship between model performance and the target hyperparameter values. Our experiments achieved maximum test AUC scores of 0.770, 0.762, and 0.886 for the 10-year, 12-year, and 15-year datasets, respectively. For each hyperparameter, we analyzed whether changes in this hyperparameter would affect model performance, examined whether there were specific patterns, and explored how to choose values for the hyperparameter. Conclusions: Our experimental findings reveal that the optimal value of a hyperparameter is not only dependent on the dataset but is also significantly influenced by the settings of other hyperparameters. Additionally, our experiments suggest a reduced range of values for a target hyperparameter, which may be helpful for “low-budget” grid search. This approach serves as a foundation for the subsequent use of grid search to enhance model performance. Full article

Background: Non-small cell lung cancer (NSCLC) progression is driven by dysregulated competing endogenous RNA (ceRNA) networks, where non-coding RNAs sequester miRNAs to modulate oncogene expression. The tumor-suppressor miR-145 is frequently downregulated in NSCLC, but its lncRNA-mediated regulation remains incompletely characterized. Methods: Integrated transcriptomic analysis of NSCLC datasets (GSE135304: blood RNA from 712 patients; GSE203510: plasma miRNAs) was used to identify dysregulated genes (|log2FC| > 0.1, p < 0.05) and miRNAs (|log2FC| > 1, p < 0.05). Experimentally validated targets from miRTarBase/TarBase were intersected with dysregulated genes, followed by WikiPathways/GO enrichment. ceRNA networks were constructed via co-expression analysis. RT-qPCR validated miR-145-3p expression in A549/MRC-5 cells and NSCLC tissues. GEPIA assessed FN1/CCND1 clinical relevance. Results: We identified 8271 dysregulated genes and 52 miRNAs. miR-145-3p, critical in immune regulation, was significantly downregulated (log2FC = −1.24, p = 0.036). Intersection analysis revealed 27 miR-145-3p targets (e.g., FN1, CCND1, SMAD3) enriched in immune pathways (FDR < 0.05) and TGF-β-mediated EMT within the dysregulated geneset. Six immune-linked hub genes emerged. LncRNAs LOC729919 and LOC100134412 showed strong co-expression with hub genes and competitively bind miR-145-3p, derepressing the expression of the metastasis drivers FN1 (ECM regulator) and CCND1 (cell cycle controller). This ceRNA axis operates within a broader dysregulation of ATM-dependent DNA damage, Hippo signaling, and cell cycle pathways. RT-qPCR confirmed significant miR-145-3p suppression in NSCLC models (p < 0.05). GEPIA revealed a significant FN1-CCND1 co-expression (p = 0.0017). Conclusions: We characterize a novel LOC729919/LOC100134412–miR-145–FN1/CCND1 ceRNA axis in NSCLC pathogenesis. FN1’s prognostic value and functional linkage to CCND1 underscores its potential clinical relevance for therapeutic disruption. Full article

Bone metastases from osteosarcoma occur in only 10% of patients, and related preclinical models are lacking. A patient diagnosed with pelvic osteosarcoma developed a metachronous scapular metastasis and was treated with multi-agent chemotherapy and surgery. Patient-derived tissue fragments (PDTFs) were obtained from leftover material after diagnosis and biobanking. PDTFs were grown on chick chorioallantoic membrane, establishing an in vivo-like predictive model. Additionally, we obtained a patient-derived cell culture, OS-MET-R-092, which has been maintained in vitro for nearly one year. OS-MET-R-092 cells were authenticated based on short tandem repeats and on their morphology when grown on commercial 3D scaffolds. Using U-2 OS and SaOS-2 as controls, we characterized growth, clonogenic potential, ability to form spheroids, migration, osteogenic differentiation, and expression of related genes. OS-MET-R-092 cells showed a low proliferation rate, impaired differentiation potential, and migratory abilities comparable to SaOS-2, while expressing higher levels of some MMPs and CD44. Functionally, OS-MET-R-092 cells demonstrated a resistant phenotype to doxorubicin, cisplatin, gemcitabine, and docetaxel, corroborated by higher expression of chemo-resistance-related genes. Collectively, OS-MET-R-092 represents a valuable tool for studying bone metastasis from osteosarcoma across various experimental settings and serves as the foundational building block for composite and translatable 3D models. Full article

Dysregulated microRNA-mediated control of matrix metalloproteinase-2 (MMP-2) plays a pivotal role in lung cancer (LC) progression, though the inflammatory signaling mechanisms governing its regulation remain poorly understood. This study reveals how S100A4-activated RAGE signaling modulates MMP-2 expression through microRNA-125b-5p (miR-125b-5p) in human LC cells. Potential miRNA target genes were computationally predicted using TargetScan algorithms. Functional interaction between miR-125b-5p and MMP-2 3′UTR was experimentally validated through dual-luciferase reporter assays incorporating full-length MMP-2 3′UTR sequence. Further validation was performed through transfection with miRNA inhibitors or mimics. To delineate the underlying mechanisms, key pathways were inhibited using small-molecule antagonists targeting p38-MAPK and NF-κB. Our analysis identified a conserved miR-125b-5p binding site in the MMP-2 3′UTR. In A549 cells, S100A4 induced reciprocal regulation, simultaneously upregulating MMP-2 and downregulating miR-125b-5p, with luciferase assays confirming direct targeting. Pre-miR-125b-5p transfection effectively reduced endogenous MMP-2 levels, while p38-MAPK/NF-κB activation mediated this regulation by suppressing miR-125b-5p consequently elevating MMP-2 expression. These findings were further validated in another human LC cell, SHP-77. These findings provide the first evidence demonstrating that miR-125b-5p directly regulates MMP-2 in LC, establishing S100A4-RAGE⟶p38/NF-κB⟶miR-125b-5p⟶MMP-2 axis as a novel regulatory pathway. The results position miR-125b-5p as a dual-action biomarker and therapeutic target against MMP-2-driven LC metastasis, offering new insights into critical inflammation-to-cancer connections. Full article

The cluster of differentiation 44 (CD44) is a member of the hyaluronic acid (HA) receptor family of cell adhesion molecules. Besides HA, this transmembrane protein also serves as a receptor for other components of the extracellular matrix (ECM), including fibronectin, collagen, and osteopontin (OPN). The CD44-HA axis is involved in a wide range of physiological and cancer-related processes, particularly in cell adhesion and migration, lymphocyte activation, as well as tumour progression and metastasis. The possibility of modulating the CD44-HA interaction with a pharmacological inhibitor has therefore been recognized as an emerging anti-cancer strategy. With its expression in a wide variety, CD44 has also become the most common surface biomarker of cancer stem cells. Due to the rapid progress of research on this crucial receptor, some published and deposited variants were often poorly described or lacked accession numbers in the available protein databases, which created confusion and hindered relevant research. In this work, we attempted to examine the protein sequences of the known CD44 variants and match them between the two UniProt and the National Centre for Biotechnology Information (NCBI) Protein databases. The deposited sequences were aligned to the CD44 canonical sequence and grouped based on the observed differences. Analysis of CD44–ligand experimental structures available in the Protein Data Bank (PDB) was also performed to identify the most promising small-molecule inhibitors of the CD44-HA interaction. Full article