Prognostic Biomarkers of Head and Neck Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Biomarkers".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 1071

Special Issue Editor


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Guest Editor
1. Stomatological Hospital, School of Stomatology, Southern Medical University, Guangzhou 510280, China
2. Division of Oral Biology and Medicine, School of Dentistry, University of California, Los Angeles, CA 90095, USA
Interests: head and neck cancer; prognosis prediction; cancer biomarkers

Special Issue Information

Dear Colleagues,

Head and neck cancer (HNC) is a common form of malignancy that predominantly affects regions including the nasal and oral cavities, oropharynx, pharynx, hypopharynx, larynx, thyroid, and salivary glands. Established risk factors such as alcohol and tobacco use, alongside human papillomavirus infections, play crucial roles in its pathogenesis and prognosis. Despite remarkable advances in understanding the disease's etiology and management due to the deployment of cutting-edge techniques, the prognosis for HNC has only seen a modest enhancement.

Prognostic biomarkers are biological indicators linked with the disease's long-term outcomes or progression. They could comprise genes, mRNAs, non-coding RNAs, proteins, and metabolites. The recognition of robust and precise prognostic biomarkers for HNC patients is of paramount importance. They can help identify patient subpopulations likely to experience varied outcomes or who may benefit from particular treatments. Implementing biomarkers may also curtail the incidence of negative biopsies and misdiagnoses, reducing the necessity for recurrent biopsies and imaging procedures. Furthermore, biomarkers predicting the response or resistance to HNC therapies can significantly enhance patient management. Nevertheless, the incorporation of biomarkers into clinical practice has been limited, thus necessitating the development and validation of novel prognostic biomarkers.

In this Special Issue, we solicit submissions that explore innovative biomarkers in various clinical settings to advance the management of HNC. These biomarkers could enable early therapeutic interventions, proactive monitoring for metastasis detection, and improved HNC patient management. We also welcome studies employing experimental analysis to elucidate the molecular mechanisms underpinning these chosen biomarkers. We eagerly anticipate your valuable contributions.

Dr. Li Cui
Guest Editor

Manuscript Submission Information

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Keywords

  • head and neck cancer
  • prognosis prediction
  • cancer biomarkers
  • tumor progression
  • patient management
  • molecular mechanism

Published Papers (1 paper)

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Research

19 pages, 11450 KiB  
Article
Constructing a T-Cell Receptor-Related Gene Signature for Prognostic Stratification and Therapeutic Guidance in Head and Neck Squamous Cell Carcinoma
by Ye Lu, Zizhao Mai, Jiarong Zheng, Pei Lin, Yunfan Lin, Li Cui and Xinyuan Zhao
Cancers 2023, 15(23), 5495; https://doi.org/10.3390/cancers15235495 - 21 Nov 2023
Viewed by 873
Abstract
Backgroud: The stratification of head and neck squamous cell carcinoma (HNSCC) patients based on prognostic differences is critical for therapeutic guidance. This study was designed to construct a predictive signature derived from T-cell receptor-related genes (TCRRGs) to forecast the clinical outcomes in HNSCC. [...] Read more.
Backgroud: The stratification of head and neck squamous cell carcinoma (HNSCC) patients based on prognostic differences is critical for therapeutic guidance. This study was designed to construct a predictive signature derived from T-cell receptor-related genes (TCRRGs) to forecast the clinical outcomes in HNSCC. Methods: We sourced gene expression profiles from The Cancer Genome Atlas (TCGA) HNSCC dataset, GSE41613, and GSE65858 datasets. Utilizing consensus clustering analysis, we identified two distinct HNSCC clusters according to TCRRG expression. A TCRRG-based signature was subsequently developed and validated across diverse independent HNSCC cohorts. Moreover, we established a nomogram model based on TCRRGs. We further explored differences in immune landscapes between high- and low-risk groups. Results: The TCGA HNSCC dataset was stratified into two clusters, displaying marked variations in both overall survival (OS) and immune cell infiltration. Furthermore, we developed a robust prognostic signature based on TCRRG utilizing the TCGA HNSCC train cohort, and its prognostic efficacy was validated in the TCGA HNSCC test cohort, GSE41613, and GSE65858. Importantly, the high-risk group was characterized by a suppressive immune microenvironment, in contrast to the low-risk group. Our study successfully developed a robust TCRRG-based signature that accurately predicts clinical outcomes in HNSCC, offering valuable strategies for improved treatments. Full article
(This article belongs to the Special Issue Prognostic Biomarkers of Head and Neck Cancer)
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