Results 251 to 260 of about 337,050 (372)

Sunitinib treatment for patients with clear-cell metastatic renal cell carcinoma: clinical outcomes and plasma angiogenesis markers [PDF]

, 2009
Loukas Kontovinis, Konstantinos Papazisis, Panagiota Touplikioti, Charalambos Andreadis, D. Mouratidou, Alexandros Kortsaris   +5 more
openalex   +1 more source

Radiomics‐Based Unsupervised Clustering Identifies Subtypes Associated With Prognosis and Immune Microenvironment in Clear Cell Renal Cell Carcinoma: A Multicenter Study

Advanced Science, EarlyView.
This study developed an unsupervised radiomics system integrating CT imaging and multi‐omics data to stratify clear cell renal cell carcinoma into two subtypes with distinct clinical outcomes. Cluster 1 showed lower recurrence risk and active immunity, while Cluster 2 exhibited higher recurrence risk, enriched VHL/KDM5C mutations, immunosuppressive ...
Yusheng Guo, Bingxin Gong, Yi Li, Jie Lou, Shuai Shan, Xiaodong Zhang, Qiang Lu, Dongyong Zhu, Qing Sun, Lianwei Miao, Yuanxi Li, Yu‐Dong Zhang, Wei Tan, Lian Yang, Chuansheng Zheng   +14 more
wiley   +1 more source

Molecular Mechanisms of Drug Resistance in Clear Cell Renal Cell Carcinoma. [PDF]

Cancers (Basel)
Bianchi N, Ancona P, Aguiari G.
europepmc   +1 more source

Systematic Comparative Protein Expression Profiling of Clear Cell Renal Cell Carcinoma [PDF]

, 2009
Rudolf Lichtenfels, Sven P. Dressler, Monica Zobawa, Christian V. Recktenwald, Angelika Ackermann, Derek Atkins, Michael Kersten, Andrea Hesse, Maria Puttkammer, Friedrich Lottspeich, Barbara Seliger   +10 more
openalex   +1 more source

SGCD: High‐Resolution Spatial Domain Characterization via Data Interpolation and Cell‐Type Deconvolution

Advanced Science, EarlyView.
SGCD presents a novel approach for tissue spatial domain identification by employing interpolation to estimate inter‐spot gene expression and deconvolution to resolve cell‐type composition in both sampled and interstitial regions. By integrating gene expression, cell type, and spatial coordinates within a graph contrastive learning framework, SGCD ...
Tianjiao Zhang, Shenghe Li, Ruolan Zhang, Hongfei Zhang, Zhongqian Zhao, Hao Sun, Zhenao Wu, Guohua Wang   +7 more
wiley   +1 more source

Deletion of the transcription factor ATF4 in a model of clear cell renal cell carcinoma. [PDF]

Neoplasia
Chi Y, Chikara S, Aguila EMD, Zhang T, Geri JB, Nanus DM, Gudas LJ.   +6 more
europepmc   +1 more source

Is there any potential link among caspase-8, p-p38 MAPK and bcl-2 in clear cell renal cell carcinomas? A comparative immunohistochemical analysis with clinical connotations [PDF]

, 2009
Vassilis Samaras, Maria Tsopanomichalou, Angeliki Stamatelli, Christos Arnaoutoglou, Efstathios Samaras, Marianthi Arnaoutoglou, Hercules Poulias, Calypso Barbatis   +7 more
openalex   +1 more source

A Network‐Driven Framework for Drug Response Precision Prediction of Acute Myeloid Leukemia

Advanced Science, EarlyView.
A network‐based precision medicine platform, named NetAML is offered for personalized treatment of AML. It combines network analysis and machine learning to develop 87 models predicting drug sensitivity for clinical drugs. NetAML identifies interpretable gene signatures that drive differential drug responses, enabling the prediction of individual ...
Yinyin Wang, Rui Liu, Yinnan Zhang, Xiang Luo, Chengzhuang Yu, Shentong Fang, Ninghua Tan, Jing Tang   +7 more
wiley   +1 more source

Recent progress in non-clear cell renal cell carcinoma: biology and therapeutic strategies. [PDF]

Ther Adv Med Oncol
Yildirim A, Moses MW, Gerke M, Jani Y, Thompson JC, Akova U, Bilen MA.   +6 more
europepmc   +1 more source

Possible role of the RhoC/ROCK pathway in progression of clear cell renal cell carcinoma

, 2008
Hideyuki Abe, Takao Kamai, Toshihiko Tsujii, Fumihiko Nakamura, Tomoko Mashidori, Tomoya Mizuno, Miho J. Tanaka, Katsuhisa Tatsumiya, Nobutaka Furuya, Akinori Masuda, Tomonori Yamanishi, Ken‐Ichiro Yoshida   +11 more
openalex   +2 more sources