The present study aimed to investigate the role of microRNA (miRNA/miR)-184 in osteosarcoma growth, development and metastasis, and the effects of miRNA-184 on the proliferation, invasion and metastasis of osteosarcoma cells and associated mechanisms. In vitro, miR-184 was transfected into U-2OS cells and 143B cells. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the expression of miR-184. MTT was utilized to detect cell proliferation. A Transwell assay was applied to detect cell invasiveness. In vivo, an osteosarcoma tibial orthotopic metastatic tumor model was established, and western blotting and RT-qPCR were used to detect the expression of Wnt and β-catenin. Following the overexpression of miR-184, the proliferation and cell invasion ability were significantly increased in U-2OS and 143B cells. Following inhibition of miR-184, cell proliferation and cell invasion ability were significantly decreased. In nude mice, tumor volume significantly increased following overexpression of miR-184, and Wnt and phosphorylated β-catenin levels were significantly increased. Following miR-184 inhibition, tumor volume was significantly decreased, and Wnt and phosphorylated β-catenin levels were significantly decreased. The results of the present study indicated that the Wnt/β-catenin signaling pathway serves a key function in the mechanism of osteosarcoma. Inhibition of miRNA-184 may reduce tumor volume of osteosarcoma via regulation of the Wnt/β-catenin signaling pathway and may provide a novel strategy for the future diagnosis and treatment of osteosarcoma.
BackgroundTo assess clinical application of computed tomography (CT)-guided 125I seed implantation for patients who cannot endure or unwillingly receive repeated surgery, chemotherapy, or radiotherapy for unmanageable cervical lymph node metastases in head and neck cancer (HNC).MethodsThirty-one consecutive patients received CT-guided 125I seed implantation between February 2010 and December 2013. To evaluate the clinical efficiency, karnofsky performance score (KPS), numeric rating scale (NRS), and tumor volume at 3-, and 6-month post-implantation were compared with pre-implantation, along with local control rate (LCR), overall survival rate (OSR), and complications at 3, 6 months, 1, and 2 years.ResultsThe tumor volume was obviously decreased at 3-, and 6-month post-implantation (21.23 ± 8.83 versus 9.19 ± 7.52 cm2; 21.23 ± 8.83 versus 6.42 ± 9.79 cm2; P < 0.05) compared with pre-implantation. The NRS was statistically reduced (3.06 ± 1.06 versus 7.77 ± 0.92; 2.39 ± 1.15 versus 7.77 ± 0.92; P < 0.05), while KPS was significantly improved (83.18 ± 5.97 versus 73.60 ± 7.90; 82.86 ± 5.43 versus 73.60 ± 7.90; P < 0.05) postoperatively at 3 and 6 months, respectively. The LCR at 3, 6 months, 1, and 2 years was 96.30, 83.87, 64.51, and 45.16 %, respectively. The OSR was 100, 100, 67.74, and 45.16 %, respectively. Three cases experienced grade I and two had grade II acute radiation toxicity.ConclusionsCT-guided seed implantation may be feasible and safe for HNC patients whose neck nodes are not manageable by routine strategies with fewer complications, higher LCR, and significant pain relief.
Objective Cancer is one of the main causes of death worldwide. Although immunotherapy brings hope for cancer treatment, it is also accompanied by immune checkpoint inhibitor-related adverse events (irAEs). Immune checkpoint inhibitor pneumonia (CIP) is a potentially fatal adverse event, but there is still a lack of effective markers and prediction models to identify patients at increased risk of CIP. Methods A total of 369 cancer patients treated between 2017 and 2022 with immune checkpoint inhibitors at Shengjing Hospital of China Medical University and Liaoning People's Hospital were recruited for this study. Independent variables were selected by differences and binary logistic regression analysis, and a risk assessment nomogram was constructed for CIP risk. The accuracy and discriminative abilities of the nomogram were evaluated by calibration plots, receiver operating characteristic curves (ROCs) and decision curve analyses (DCAs). Results Binary logistic regression analysis showed that smoking history, acute phase proteins [interleukin (IL-6) and C-reactive protein (CRP)], CD8 + T lymphocyte count and serum alveolar protein [surface protein-A (SP-A) and Krebs Von den Lungen-6 (KL-6)] were significantly associated with CIP risk. A nomogram consisting of these variables was established and validated by different analyses. Conclusions We developed an effective risk nomogram for CIP prediction in immune-checkpoint inhibitor administrated cancer patients, which will further assist early detection of immunotherapy-related adverse events.
Backgrounds: Knowledge discovery from breast cancer treatment records has promoted downstream clinical studies such as careflow mining and therapy analysis. However, the clinical treatment text from electronic health data might be recorded by different doctors under their hospital guidelines, making the final data rich in author-and domain-specific idiosyncrasies. Therefore, breast cancer treatment entity normalization becomes an essential task for the above downstream clinical studies. The latest studies have demonstrated the superiority of deep learning methods in named entity normalization tasks. Fundamentally, most existing approaches adopt pipeline implementations that treat it as an independent process after named entity recognition, which can propagate errors to later tasks. In addition, despite its importance in clinical and translational research, few studies directly deal with the normalization task in Chinese clinical text due to the complexity of composition forms. Methods: To address these issues, we propose PASCAL, an end-to-end and accurate framework for breast cancer treatment entity normalization (TEN). PASCAL leverages a gated convolutional neural network to obtain a representation vector that can capture contextual features and long-term dependencies. Additionally, it treats treatment entity recognition (TER) as an auxiliary task that can provide meaningful information to the primary TEN task and as a particular regularization to further optimize the shared parameters. Finally, by concatenating the context-aware vector and probabilistic distribution vector from TEN, we utilize the conditional random field layer (CRF) to model the normalization sequence and predict the TEN sequential results. Results: To evaluate the effectiveness of the proposed framework, we employ the three latest sequential models as baselines and build the model in single-and multitask on a real-world database. Experimental results show that our method achieves better accuracy and efficiency than state-of-the-art approaches. Conclusions: The effectiveness and efficiency of the presented pseudo cascade learning framework were validated for breast cancer treatment normalization in clinical text. We believe the predominant performance lies in its ability to extract valuable information from unstructured text data, which will significantly contribute to downstream tasks, such as treatment recommendations, breast cancer staging and careflow mining.
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