Identification of fibroblast-related genes based on single-cell and machine learning to predict the prognosis and endocrine metabolism of pancreatic cancer.

Xu, Yinghua; Chen, Xionghuan; Liu, Nan; Chu, Zhong; Wang, Qiang

Xu, Yinghua; Chen, Xionghuan; Liu, Nan; Chu, Zhong; Wang, Qiang.

Afiliação

Xu Y; Department of Translational Medicine and Clinical Research, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Chen X; Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Liu N; Department of Trauma Surgery, Tiantai People's Hospital of Zhejiang Province, Taizhou, China.
Chu Z; Department of Translational Medicine and Clinical Research, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Wang Q; Department of Translational Medicine and Clinical Research, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Front Endocrinol (Lausanne) ; 14: 1201755, 2023.

Article em En | MEDLINE | ID: mdl-37588985

Assuntos

Adenocarcinoma; Neoplasias Pancreáticas; Humanos; Neoplasias Pancreáticas/diagnóstico; Neoplasias Pancreáticas/genética; Prognóstico; Aprendizado de Máquina; Carcinogênese; Transformação Celular Neoplásica; Fibroblastos; Fatores de Transcrição SOXC; Apolipoproteína L1; Neoplasias Pancreáticas

Palavras-chave

RiskScore; endocrine metabolism; pancreatic adenocarcinoma; prognosis; single-cell sequencing; tumorigenesis

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Pancreáticas / Adenocarcinoma Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo

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PubMed Links

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Pancreáticas / Adenocarcinoma Idioma: En Ano de publicação: 2023 Tipo de documento: Article