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Unraveling the regulatory cell death pathways in gastric cancer: a multi-omics study.
Sun, Jiazheng; Rao, Lixiang; Zhou, Sirui; Zeng, Yulan; Sun, Yalu.
Afiliação
  • Sun J; Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Rao L; Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhou S; Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zeng Y; Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Sun Y; Affiliated Hospital of Jining Medical University, Jining, China.
Front Pharmacol ; 15: 1447970, 2024.
Article em En | MEDLINE | ID: mdl-39314752
ABSTRACT
Gastric cancer (GC) is a prevalent form of cancer worldwide and has a high death rate, with less than 40% of patients surviving for 5 years. GC demonstrates a vital characteristic of evading regulatory cell death (RCD). However, the extent to which RCD patterns are clinically significant in GC has not been well investigated. The study created a regulatory cell death index (RCDI) signature by employing 101 machine-learning algorithms. These algorithms were based on the expression files of 1292 GC patients from 6 multicenter cohorts. RCDI is a reliable and robust determinant of the likelihood of surviving in general. Furthermore, the precision of RCDI surpasses that of the 20 signatures that have been previously disclosed. The presence of RCDI signature is closely linked to immunological characteristics, such as the infiltration of immune cells, the presence of immunotherapy markers, and the activation of immune-related functions. This suggests that there is a higher level of immune activity in cases with RCDI signature. Collectively, the use of RCDI has the potential to be a strong and encouraging method for enhancing the clinical results of individual individuals with GC.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article