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Stromal score is a promising index in tumor patients' outcome determination.
Xu, Xiaoxian; Xu, Yu; Hu, Wangxiong; Hong, Wenjie; Wang, Yichen; Zhang, Xiaojing; Fan, Xiaoji; Wang, Tingzhang; Lou, Hanmei; Yang, Yanmei; Qian, Jianhua.
Afiliação
  • Xu X; Department of Gynecology and Obstetrics, The First Affiliated Hospital, Zhejiang University School of Medicine, NO. 79 Qingchun Road, Shangcheng District, Hangzhou City, 310003, Zhejiang Province, China.
  • Xu Y; Department of Gynecologic Radiation, Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, No. 1 Banshan Road, Gongshu District, Hangzhou City, 310022, Zhejiang Province, China.
  • Hu W; Jianggao Town Health Center of Baiyun District, Technical Cooperation Hospital of Guangzhou First People's Hospital, Guangzhou, Guangdong, 510460, China.
  • Hong W; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education), The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310009, China.
  • Wang Y; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, China.
  • Zhang X; Department of Gynecologic Radiation, Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, No. 1 Banshan Road, Gongshu District, Hangzhou City, 310022, Zhejiang Province, China.
  • Fan X; Department of Gynecologic Radiation, Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, No. 1 Banshan Road, Gongshu District, Hangzhou City, 310022, Zhejiang Province, China.
  • Wang T; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, China.
  • Lou H; Key Laboratory of Microbial Technology and Bioinformatics of Zhejiang Province, Hangzhou, China.
  • Yang Y; Department of Gynecologic Radiation, Zhejiang Key Laboratory of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, No. 1 Banshan Road, Gongshu District, Hangzhou City, 310022, Zhejiang Province, China.
  • Qian J; Key Laboratory of Reproductive and Genetics, Ministry of Education, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310006, China.
Heliyon ; 9(11): e22432, 2023 Nov.
Article em En | MEDLINE | ID: mdl-38034609
ABSTRACT

Background:

Immune status is widely acknowledged as a valuable marker for predicting cancer prognosis and therapy response. However, there has been a limited understanding of the stromal landscape in cancer.

Methods:

By employing ESTIMATE, stromal- and immune-scores were inferred for 6193 tumor samples spanning 12 cancer types sourced from The Cancer Genome Atlas (TCGA). Subsequently, the samples were categorized into seven groups based on their stromal and immune scores. A comparison of prognosis, lymphocyte and stromal cell infiltration, and the response to programmed death ligand 1 (PD-L1) therapy was conducted among these subtypes.

Results:

It was unveiled by the analysis that, in the majority of cancer types, stromal score exhibited a more potent predictive capability for outcomes compared to the immune score. Furthermore, it was observed that in four cancer types, intermediate immune infiltration coupled with low stromal infiltration correlated with the most favorable overall survival, whereas an unfavorable outcome was predicted in colorectal cancer (CRC) and stomach adenocarcinoma (STAD) when high immune infiltration coexisted with intermediate or high stromal infiltration.

Conclusion:

In summary, while high immune scores frequently correlate with a positive prognosis, such correlation is not universal. A potential strategy to address the current limitations of the immune score in specific circumstances could involve a focus on stromal scores or a subtle integration of stromal and immune status.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article