Machine learning-derived identification of tumor-infiltrating immune cell-related signature for improving prognosis and immunotherapy responses in patients with skin cutaneous melanoma.

Leng, Shaolong; Nie, Gang; Yi, Changhong; Xu, Yunsheng; Zhang, Lvya; Zhu, Linyu

Leng, Shaolong; Nie, Gang; Yi, Changhong; Xu, Yunsheng; Zhang, Lvya; Zhu, Linyu.

Afiliação

Leng S; Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Nie G; Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Yi C; Department of Interventional Radiology, Cancer Hospital of Shantou University Medical College, Shantou, China.
Xu Y; Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Zhang L; Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China. zhangly265@mail.sysu.edu.cn.
Zhu L; Department of Dermatovenereology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China. zhulinyu@sysush.com.

Cancer Cell Int ; 23(1): 214, 2023 Sep 26.

Article em En | MEDLINE | ID: mdl-37752452

Palavras-chave

Immunotherapy; Machine learning; Skin cutaneous melanoma; Tumor microenvironment

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Clinical_trials / Diagnostic_studies / Prognostic_studies Idioma: En Revista: Cancer Cell Int Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

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