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Frontiers in Endocrinology 2024Previous observational epidemiological studies reported an association between cathepsins and cancer, however, a causal relationship is uncertain. This study evaluated...
BACKGROUND
Previous observational epidemiological studies reported an association between cathepsins and cancer, however, a causal relationship is uncertain. This study evaluated the causal relationship between cathepsins and cancer using Mendelian randomization (MR) analysis.
METHODS
We used publicly available genome-wide association study (GWAS) data for bidirectional MR analysis. Inverse variance weighting (IVW) was used as the primary MR method of MR analysis.
RESULTS
After correction for the False Discovery Rate (FDR), two cathepsins were found to be significantly associated with cancer risk: cathepsin H (CTSH) levels increased the risk of lung cancer (OR = 1.070, 95% CI = 1.027-1.114, = 0.001, = 0.009), and CTSH levels decreased the risk of basal cell carcinoma (OR = 0.947, 95% CI = 0.919-0.975, = 0.0002, P = 0.002). In addition, there was no statistically significant effect of the 20 cancers on the nine cathepsins. Some unadjusted low P-value phenotypes are worth mentioning, including a positive correlation between cathepsin O (CTSO) and breast cancer (OR = 1.012, 95% CI = 1.001-1.025, = 0.041), cathepsin S (CTSS) and pharyngeal cancer (OR = 1.017, 95% CI = 1.001-1.034, = 0.043), and CTSS and endometrial cancer (OR = 1.055, 95% CI = 1.012-1.101, = 0.012); and there was a negative correlation between cathepsin Z and ovarian cancer (CTSZ) (OR = 0.970, 95% CI = 0.949-0.991, = 0.006), CTSS and prostate cancer (OR = 0.947, 95% CI = 0.902-0.944, = 0.028), and cathepsin E (CTSE) and pancreatic cancer (OR = 0.963, 95% CI = 0.938-0.990, = 0.006).
CONCLUSION
Our MR analyses showed a causal relationship between cathepsins and cancers and may help provide new insights for further mechanistic and clinical studies of cathepsin-mediated cancer.
Topics: Humans; Mendelian Randomization Analysis; Cathepsins; Neoplasms; Genome-Wide Association Study; Genetic Predisposition to Disease; Polymorphism, Single Nucleotide; Female; Risk Factors
PubMed: 38883596
DOI: 10.3389/fendo.2024.1428433 -
Behavioural Neurology 2024The most common and aggressive tumor is brain malignancy, which has a short life span in the fourth grade of the disease. As a result, the medical plan may be a crucial...
The most common and aggressive tumor is brain malignancy, which has a short life span in the fourth grade of the disease. As a result, the medical plan may be a crucial step toward improving the well-being of a patient. Both diagnosis and therapy are part of the medical plan. Brain tumors are commonly imaged with magnetic resonance imaging (MRI), positron emission tomography (PET), and computed tomography (CT). In this paper, multimodal fused imaging with classification and segmentation for brain tumors was proposed using the deep learning method. The MRI and CT brain tumor images of the same slices (308 slices of meningioma and sarcoma) are combined using three different types of pixel-level fusion methods. The presence/absence of a tumor is classified using the proposed Tumnet technique, and the tumor area is found accordingly. In the other case, Tumnet is also applied for single-modal MRI/CT (561 image slices) for classification. The proposed Tumnet was modeled with 5 convolutional layers, 3 pooling layers with ReLU activation function, and 3 fully connected layers. The first-order statistical fusion metrics for an average method of MRI-CT images are obtained as SSIM tissue at 83%, SSIM bone at 84%, accuracy at 90%, sensitivity at 96%, and specificity at 95%, and the second-order statistical fusion metrics are obtained as the standard deviation of fused images at 79% and entropy at 0.99. The entropy value confirms the presence of additional features in the fused image. The proposed Tumnet yields a sensitivity of 96%, an accuracy of 98%, a specificity of 99%, normalized values of the mean of 0.75, a standard deviation of 0.4, a variance of 0.16, and an entropy of 0.90.
Topics: Humans; Brain Neoplasms; Magnetic Resonance Imaging; Meningioma; Multimodal Imaging; Tomography, X-Ray Computed; Deep Learning; Sarcoma; Image Processing, Computer-Assisted; Brain; Neural Networks, Computer; Meningeal Neoplasms
PubMed: 38882177
DOI: 10.1155/2024/4678554 -
Translational Cancer Research May 2024Osteosarcoma (OS) is an exceptionally aggressive bone neoplasm that predominantly impacts the paediatric and adolescent population, exhibiting unfavourable prognosis....
BACKGROUND
Osteosarcoma (OS) is an exceptionally aggressive bone neoplasm that predominantly impacts the paediatric and adolescent population, exhibiting unfavourable prognosis. The importance of RNA binding motif protein 14 () in the aetiology of OS is not well understood, despite its established involvement in several other types of cancer.
METHODS
In this study, we conducted an analysis of the expression profiles of in cancer tissues and cell lines. To achieve this, we will utilised data obtained from various databases including The Cancer Genome Atlas Program (TCGA) project, The Genotype-Tissue Expression (GTEx) Project, Gene Expression Omnibus (GEO) database, and cancer cell line encyclopedia (CCLE) data. Furthermore, this study also aims to examine the effects of on the proliferation, migration, and invasive properties of OS cells using cell functional gain and loss studies. In this study, we carried out an in-depth investigation to explore possible molecular pathways that underlie the regulation of the malignant phenotype found in OS by . This investigation involved integrating data from overexpression, knockdown RNA-seq experiments, and an array comprising 6,096 perturbed genes obtained from the Genetic Perturbation Similarity Analysis Database (GPSAdb). This research offers an opportunity to build a robust conceptual framework for the potential advancement of novel therapeutic approaches that are especially aimed at attacking OS.
RESULTS
plays an active role in OS by significantly contributing to the enhancement of cellular proliferation, migration, and invasion. At the molecular level, it is probable that exerts control over the malignant characteristics of OS through its modulation of the Hippo signalling system.
CONCLUSIONS
The above-mentioned findings underscore the significant importance of as an intriguing target for therapy for the mitigation and management of OS. This particular protein holds an excellent opportunity for the development of novel and efficacious therapeutic approaches that possess the potential to yield favorable results for patients affected with OS.
PubMed: 38881928
DOI: 10.21037/tcr-23-2070 -
Journal of Orthopaedic Surgery and... Jun 2024Ubiquitin/ubiquitin-like (Ub/UBL)-related genes have been reported to be associated with the survival of osteosarcoma patients but have not yet been systematically...
BACKGROUND
Ubiquitin/ubiquitin-like (Ub/UBL)-related genes have been reported to be associated with the survival of osteosarcoma patients but have not yet been systematically explored.
METHODS
The prognostic value of Ub/UBL-related genes, immune cell infiltration and clinicopathological features of patients were explored by Cox and LASSO regression analyses. A prognostic model was established and then validated in the GSE21257 dataset. The differential expression of hub genes in osteosarcoma was confirmed by qRT-PCR, western blotting and immunohistochemistry.
RESULTS
Tripartite Motif Containing 8 (TRIM8) and Ubiquitin Like With PHD And Ring Finger Domains 2 (UHRF2) were screened as genes with prognostic value in osteosarcoma. Kaplan-Meier analysis and scatter plots indicated that patients in the high gene significance score group tended to have a worse prognosis. The concordance index, calibration analysis and receiver operating characteristic analysis suggested that the model had good prediction accuracy and high sensitivity and specificity. Decision curve analysis revealed that patients could obtain greater net benefit from this model. Functional analyses of the differentially expressed genes indicated that they were involved in important functions and pathways. TRIM8 and UHRF2 were confirmed to be highly expressed in osteosarcoma cell lines and tissues.
CONCLUSIONS
TRIM8 and UHRF2 are potential prognostic genes in osteosarcoma, and these results provide insights into the roles of these genes and their implications for patient outcomes.
Topics: Osteosarcoma; Humans; Prognosis; Bone Neoplasms; Male; Female; Biomarkers, Tumor; Ubiquitin-Protein Ligases; Ubiquitin
PubMed: 38879525
DOI: 10.1186/s13018-024-04781-1 -
Pathology, Research and Practice Jun 2024Soft tissue and bone tumors comprise a wide category of neoplasms. Their diversity frequently raises diagnostic challenges, and therapeutic options are continuously... (Review)
Review
Soft tissue and bone tumors comprise a wide category of neoplasms. Their diversity frequently raises diagnostic challenges, and therapeutic options are continuously developing. The therapeutic success rate and long-term prognosis of patients have improved substantially due to new advances in immunohistochemical and molecular biology techniques. A fundamental contribution to these achievements has been the study of the tumor microenvironment and the reclassification of new entities with the updating of the molecular pathogenesis in the revised 5th edition of the Classification of Soft Tissue Tumors, edited by the World Health Organization. The proposed molecular diagnostic techniques include the well-known in situ hybridization and polymerase chain reaction methods, but new techniques such as copy-number arrays, multiplex probes, single-nucleotide polymorphism, and sequencing are also proposed. This review aims to synthesize the most recent pathogenetic and molecular classifications of soft tissue and bone tumors, considering the major impact of these diagnostic tools, which are becoming indispensable in clinicopathological practice.
PubMed: 38878666
DOI: 10.1016/j.prp.2024.155406 -
BMC Pulmonary Medicine Jun 2024The diagnostic complexities that arise in radiographic distinction between ectopic pleural thymoma and other thoracic neoplasms are substantial, with instances of...
BACKGROUND
The diagnostic complexities that arise in radiographic distinction between ectopic pleural thymoma and other thoracic neoplasms are substantial, with instances of co-occurring T-cell lymphocytosis and osseous metastasis being exceedingly rare.
CASE PRESENTATION
A 51-year-old woman was admitted to our hospital with dyspnea and chest pain. Upon imaging examination, she was found to have diffuse and nodular pleural thickening on the left side, collapse of the left lung and a compression in the second thoracic vertebrae. All lesions showed significant F-FDG uptake on F-FDG PET/CT examination. Furthermore, she exhibited T-cell lymphocytosis in her peripheral blood, lymph nodes, and bone marrow. After ruling out malignant pleural mesothelioma (MPM), lung cancer with pleural metastasis, and T-cell lymphoma, the definitive diagnosis asserted was ectopic pleural thymoma with T-cell lymphocytosis and bone metastasis.
CONCLUSION
Physicians need to expand their knowledge of the imaging features of ectopic pleural thymoma. Cases with T-cell lymphocytosis may exhibit increased aggressiveness and prone to bone metastasis.
Topics: Humans; Female; Middle Aged; Thymoma; Lymphocytosis; Pleural Neoplasms; Bone Neoplasms; Positron Emission Tomography Computed Tomography; Thymus Neoplasms; T-Lymphocytes; Fluorodeoxyglucose F18; Diagnosis, Differential; Pleura
PubMed: 38877486
DOI: 10.1186/s12890-024-03090-x -
Scientific Reports Jun 2024PDE1B had been found to be involved in various diseases, including tumors and non-tumors. However, little was known about the definite role of PDE1B in osteosarcoma....
PDE1B had been found to be involved in various diseases, including tumors and non-tumors. However, little was known about the definite role of PDE1B in osteosarcoma. Therefore, we mined public data on osteosarcoma to reveal the prognostic values and immunological roles of the PDE1B gene. Three osteosarcoma-related datasets from online websites were utilized for further data analysis. R 4.3.2 software was utilized to conduct difference analysis, prognostic analysis, gene set enrichment analysis (GSEA), nomogram construction, and immunological evaluations, respectively. Experimental verification of the PDE1B gene in osteosarcoma was conducted by qRT-PCR and western blot, based on the manufacturer's instructions. The PDE1B gene was discovered to be lowly expressed in osteosarcoma, and its low expression was associated with poor OS (all P < 0.05). Experimental verifications by qRT-PCR and western blot results remained consistent (all P < 0.05). Univariate and multivariate Cox regression analyses indicated that the PDE1B gene had independent abilities in predicting OS in the TARGET osteosarcoma dataset (both P < 0.05). GSEA revealed that PDE1B was markedly linked to the calcium, cell cycle, chemokine, JAK STAT, and VEGF pathways. Moreover, PDE1B was found to be markedly associated with immunity (all P < 0.05), and the TIDE algorithm further shed light on that patients with high-PDE1B expression would have a better immune response to immunotherapies than those with low-PDE1B expression, suggesting that the PDE1B gene could prevent immune escape from osteosarcoma. The PDE1B gene was found to be a tumor suppressor gene in osteosarcoma, and its high expression was related to a better OS prognosis, suppressing immune escape from osteosarcoma.
Topics: Osteosarcoma; Humans; Biomarkers, Tumor; Prognosis; Tumor Microenvironment; Bone Neoplasms; Male; Female; Gene Expression Regulation, Neoplastic; Cyclic Nucleotide Phosphodiesterases, Type 1
PubMed: 38877061
DOI: 10.1038/s41598-024-64627-y -
JCO Clinical Cancer Informatics Jun 2024Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication...
PURPOSE
Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innovative treatment strategies. We have created and implemented MOSAIC, an artificial intelligence (AI)-based framework designed for multimodal analysis, classification, and personalized prognostic assessment in rare cancers. Clinical validation was performed on myelodysplastic syndrome (MDS), a rare hematologic cancer with clinical and genomic heterogeneities.
METHODS
We analyzed 4,427 patients with MDS divided into training and validation cohorts. Deep learning methods were applied to integrate and impute clinical/genomic features. Clustering was performed by combining Uniform Manifold Approximation and Projection for Dimension Reduction + Hierarchical Density-Based Spatial Clustering of Applications with Noise (UMAP + HDBSCAN) methods, compared with the conventional Hierarchical Dirichlet Process (HDP). Linear and AI-based nonlinear approaches were compared for survival prediction. Explainable AI (Shapley Additive Explanations approach [SHAP]) and federated learning were used to improve the interpretation and the performance of the clinical models, integrating them into distributed infrastructure.
RESULTS
UMAP + HDBSCAN clustering obtained a more granular patient stratification, achieving a higher average silhouette coefficient (0.16) with respect to HDP (0.01) and higher balanced accuracy in cluster classification by Random Forest (92.7% ± 1.3% and 85.8% ± 0.8%). AI methods for survival prediction outperform conventional statistical techniques and the reference prognostic tool for MDS. Nonlinear Gradient Boosting Survival stands in the internal (Concordance-Index [C-Index], 0.77; SD, 0.01) and external validation (C-Index, 0.74; SD, 0.02). SHAP analysis revealed that similar features drove patients' subgroups and outcomes in both training and validation cohorts. Federated implementation improved the accuracy of developed models.
CONCLUSION
MOSAIC provides an explainable and robust framework to optimize classification and prognostic assessment of rare cancers. AI-based approaches demonstrated superior accuracy in capturing genomic similarities and providing individual prognostic information compared with conventional statistical methods. Its federated implementation ensures broad clinical application, guaranteeing high performance and data protection.
Topics: Humans; Prognosis; Artificial Intelligence; Precision Medicine; Female; Rare Diseases; Male; Deep Learning; Neoplasms; Myelodysplastic Syndromes; Algorithms; Middle Aged; Aged; Cluster Analysis
PubMed: 38875514
DOI: 10.1200/CCI.24.00008 -
Medicine Jun 2024This study aimed to assess hematological diseases next-generation sequencing (NGS) panel enhances the diagnosis and classification of myeloid neoplasms (MN) using the... (Observational Study)
Observational Study
This study aimed to assess hematological diseases next-generation sequencing (NGS) panel enhances the diagnosis and classification of myeloid neoplasms (MN) using the 5th edition of the WHO Classification of Hematolymphoid Tumors (WHO-HAEM5) and the International Consensus Classification (ICC) of Myeloid Tumors. A cohort of 112 patients diagnosed with MN according to the revised fourth edition of the WHO classification (WHO-HAEM4R) underwent testing with a 141-gene NGS panel for hematological diseases. Ancillary studies were also conducted, including bone marrow cytomorphology and routine cytogenetics. The cases were then reclassified according to WHO-HAEM5 and ICC to assess the practical impact of these 2 classifications. The mutation detection rates were 93% for acute myeloid leukemia (AML), 89% for myelodysplastic syndrome (MDS), 94% for myeloproliferative neoplasm (MPN), and 100% for myelodysplasia/myeloproliferative neoplasm (MDS/MPN) (WHO-HAEM4R). NGS provided subclassified information for 26 and 29 patients with WHO-HAEM5 and ICC, respectively. In MPN, NGS confirmed diagnoses in 16 cases by detecting JAK2, MPL, or CALR mutations, whereas 13 "triple-negative" MPN cases revealed at least 1 mutation. NGS panel testing for hematological diseases improves the diagnosis and classification of MN. When diagnosed with ICC, NGS produces more classification subtype information than WHO-HAEM5.
Topics: Humans; High-Throughput Nucleotide Sequencing; Female; Male; Middle Aged; Aged; Myeloproliferative Disorders; Adult; Myelodysplastic Syndromes; Mutation; Aged, 80 and over; Janus Kinase 2; World Health Organization; Leukemia, Myeloid, Acute; Receptors, Thrombopoietin; Calreticulin; Young Adult
PubMed: 38875377
DOI: 10.1097/MD.0000000000038556 -
PloS One 2024Ewing sarcoma is the second most common bone cancer in children, and while patients who present with metastatic disease at the time of diagnosis have a dismal prognosis....
Ewing sarcoma is the second most common bone cancer in children, and while patients who present with metastatic disease at the time of diagnosis have a dismal prognosis. Ewing sarcoma tumors are driven by the fusion gene EWS/Fli1, and while these tumors are genetically homogenous, the transcriptional heterogeneity can lead to a variety of cellular processes including metastasis. In this study, we demonstrate that in Ewing sarcoma cells, the canonical Wnt/β-Catenin signaling pathway is heterogeneously activated in vitro and in vivo, correlating with hypoxia and EWS/Fli1 activity. Ewing sarcoma cells predominantly express β-Catenin on the cell membrane bound to CDH11, which can respond to exogenous Wnt ligands leading to the immediate activation of Wnt/β-Catenin signaling within a tumor. Knockdown of CDH11 leads to delayed and decreased response to exogenous Wnt ligand stimulation, and ultimately decreased metastatic propensity. Our findings strongly indicate that CDH11 is a key component of regulating Wnt//β-Catenin signaling heterogeneity within Ewing sarcoma tumors, and is a promising molecular target to alter Wnt//β-Catenin signaling in Ewing sarcoma patients.
Topics: Sarcoma, Ewing; Humans; Cadherins; Wnt Signaling Pathway; Cell Line, Tumor; beta Catenin; Animals; Bone Neoplasms; Mice; Oncogene Proteins, Fusion; Proto-Oncogene Protein c-fli-1; RNA-Binding Protein EWS
PubMed: 38875295
DOI: 10.1371/journal.pone.0305490