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Effective TME-related signature to predict prognosis of patients with head and neck squamous cell carcinoma.
Wan, Lingfei; Li, Yuanshuai; Pan, Wenting; Yong, Yuting; Yang, Chao; Li, Chen; Zhao, Xingxing; Li, Ruihong; Yue, Wen; Yan, Xinlong.
Affiliation
  • Wan L; College of Life Science and Bioengineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China.
  • Li Y; Stem Cell and Regenerative Medicine Lab, Beijing Institute of Radiation Medicine, Beijing, China.
  • Pan W; College of Life Science and Bioengineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China.
  • Yong Y; College of Life Science and Bioengineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China.
  • Yang C; Stem Cell and Regenerative Medicine Lab, Beijing Institute of Radiation Medicine, Beijing, China.
  • Li C; College of Life Science and Bioengineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China.
  • Zhao X; Stem Cell and Regenerative Medicine Lab, Beijing Institute of Radiation Medicine, Beijing, China.
  • Li R; Department of Nucleus Radiation-Related Injury Treatment, PLA Rocket Force Characteristic Medical Center, Beijing, China.
  • Yue W; College of Life Science and Bioengineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China.
  • Yan X; College of Life Science and Bioengineering, Faculty of Environmental and Life Sciences, Beijing University of Technology, Beijing, China.
Front Mol Biosci ; 10: 1232875, 2023.
Article in En | MEDLINE | ID: mdl-37670814
ABSTRACT

Introduction:

The tumor microenvironment (TME) is crucial for the development of head and neck squamous cell carcinoma (HNSCC). However, the correlation of the characteristics of the TME and the prognosis of patients with HNSCC remains less known.

Methods:

In this study, we calculated the immune and stromal cell scores using the "estimate" R package. Kaplan-Meier survival and CIBERSORT algorithm analyses were applied in this study.

Results:

We identified seven new markers FCGR3B, IGHV3-64, AC023449.2, IGKV1D-8, FCGR2A, WDFY4, and HBQ1. Subsequently, a risk model was constructed and all HNSCC samples were grouped into low- and high-risk groups. The results of both the Kaplan-Meier survival and receiver operating characteristic curve (ROC) analyses showed that the prognosis indicated by the model was accurate (0.758, 0.756, and 0.666 for 1-, 3- and 5-year survival rates). In addition, we applied the CIBERSORT algorithm to reveal the significant differences in the infiltration levels of immune cells between the two risk groups.

Discussion:

Our study elucidated the roles of the TME and identified new prognostic biomarkers for patients with HNSCC.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Front Mol Biosci Year: 2023 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Front Mol Biosci Year: 2023 Document type: Article