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Integrative Analysis of Bioinformatics and Machine Learning Algorithms Identifies a Novel Diagnostic Model Based on Costimulatory Molecule for Predicting Immune Microenvironment Status in Lung Adenocarcinoma.
Zhai, Wen-Yu; Duan, Fang-Fang; Wang, Yi-Zhi; Wang, Jun-Ye; Zhao, Ze-Rui; Lin, Yao-Bin; Rao, Bing-Yu; Chen, Si; Zheng, Lie; Long, Hao.
  • Zhai WY; Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China; Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China.
  • Duan FF; Department of Medical Oncology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China.
  • Wang YZ; Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China; Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China.
  • Wang JY; Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China; Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China.
  • Zhao ZR; Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China; Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China.
  • Lin YB; Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China; Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China.
  • Rao BY; Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China; Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China.
  • Chen S; Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China; Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China.
  • Zheng L; Medical Imaging Division, Department of Medical Imaging and Interventional Radiology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China. Electronic address: zhenglie@sysucc.org.cn.
  • Long H; Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China; Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China. Electronic address: longhao@sysucc.o
Am J Pathol ; 192(10): 1433-1447, 2022 10.
Article en En | MEDLINE | ID: mdl-35948079
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Adenocarcinoma del Pulmón / Neoplasias Pulmonares Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Search on Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Adenocarcinoma del Pulmón / Neoplasias Pulmonares Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article