Showing 1,941 - 1,960 results of 12,926 for search 'CPPCC~', query time: 0.90s Refine Results
  1. 1941
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  5. 1945

    Response Gene to Complement 32 is associated with poor patient survival and an inflamed tumor-immune microenvironment in clear cell renal cell carcinoma by Lingling Li, Xiaocui Bu, Shuhui Wang, Yan Liu, Chongdao Chen, Wei Zhang, Peng Zhao

    Published 2025-02-01
    “…It has been well established that tumor-infiltrating lymphocytes (TILs) play a critical role in the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC). However, the mechanism on the interactions between TILs and tumor cells in the tumor-immune microenvironment remains unclear. …”
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  6. 1946

    Predicting correlation relationships of entities between attack patterns and techniques based on word embedding and graph convolutional network by Weicheng QIU, Xiuzhen CHEN, Yinghua MA, Jin MA, Zhihong ZHOU

    Published 2023-08-01
    “…Threat analysis relies on knowledge bases that contain a large number of security entities.The scope and impact of security threats and risks are evaluated by modeling threat sources, attack capabilities, attack motivations, and threat paths, taking into consideration the vulnerability of assets in the system and the security measures implemented.However, the lack of entity relations between these knowledge bases hinders the security event tracking and attack path generation.To complement entity relations between CAPEC and ATT&CK techniques and enrich threat paths, an entity correlation prediction method called WGS was proposed, in which entity descriptions were analyzed based on word embedding and a graph convolution network.A Word2Vec model was trained in the proposed method for security domain to extract domain-specific semantic features and a GCN model to capture the co-occurrence between words and sentences in entity descriptions.The relationship between entities was predicted by a Siamese network that combines these two features.The inclusion of external semantic information helped address the few-shot learning problem caused by limited entity relations in the existing knowledge base.Additionally, dynamic negative sampling and regularization was applied in model training.Experiments conducted on CAPEC and ATT&CK database provided by MITRE demonstrate that WGS effectively separates related entity pairs from irrelevant ones in the sample space and accurately predicts new entity relations.The proposed method achieves higher prediction accuracy in few-shot learning and requires shorter training time and less computing resources compared to the Bert-based text similarity prediction models.It proves that word embedding and graph convolutional network based entity relation prediction method can extract new entity correlation relationships between attack patterns and techniques.This helps to abstract attack techniques and tactics from low-level vulnerabilities and weaknesses in security threat analysis.…”
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  7. 1947
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  15. 1955

    Kinomic profiling to predict sunitinib response of patients with metastasized clear cell Renal Cell Carcinoma by Jeannette C. Oosterwijk-Wakka, Liesbeth Houkes, Loes F.M. van der Zanden, Lambertus A.L.M. Kiemeney, Kerstin Junker, Anne Y Warren, Tim Eisen, Ulrich Jaehde, Marius T Radu, Rob Ruijtenbeek, Egbert Oosterwijk

    Published 2025-02-01
    “…Materials and Methods: EuroTARGET partners collected ccRCC and matched normal kidney tissue samples immediately after surgery, snap-frozen and stored at -80°C until use. …”
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  16. 1956

    Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma by Yunxun Liu, Zhiwei Yan, Cheng Liu, Rui Yang, Qingyuan Zheng, Jun Jian, Minghui Wang, Lei Wang, Xiaodong Weng, Zhiyuan Chen, Xiuheng Liu

    Published 2025-01-01
    “…GGT6, an essential metabolic indicator and component of MRPS, has been proven to support proliferation and invasion in ccRCC. MRPS has the potential to be a highly effective tool in improving the clinical results of patients with ccRCC.…”
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  17. 1957

    Cancer Stem Cell Marker Endoglin (CD105) Induces Epithelial Mesenchymal Transition (EMT) but Not Metastasis in Clear Cell Renal Cell Carcinoma by Junhui Hu, Wei Guan, Libin Yan, Zhangqun Ye, Lily Wu, Hua Xu

    Published 2019-01-01
    “…Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of kidney cancer. …”
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  18. 1958
  19. 1959

    Stratification system with dual human endogenous retroviruses for predicting immunotherapy efficacy in metastatic clear-cell renal cell carcinoma by Li Xu, Stephane Oudard, Catherine Sautes-Fridman, Xiaoping Su, Gabriel G. Malouf, Virginie Verkarre, Salma Kotti, Fangrong Yan, Yann-Alexandre Vano, Wolf Herve Fridman, Cheng-Ming Sun, Xiaofan Lu, Wenxuan Cheng

    Published 2025-01-01
    “…While the dysregulation of ERV transcription has been linked to immune cell infiltration in various cancers, its relationship with immune checkpoint inhibitor (ICI) response in solid tumors, particularly metastatic clear-cell renal cell carcinoma (ccRCC), remains inadequately explored.Methods This study analyzed patients with metastatic ccRCC from two prospective clinical trials, encompassing 181 patients receiving nivolumab in the CheckMate trials (−009 to –010 and −025) and 48 patients treated with the ipilimumab-nivolumab combination in the BIONIKK trial. …”
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  20. 1960