Identification of the HNSC88 Molecular Signature for Predicting Subtypes of Head and Neck Cancer

Author:

Chuang Yi-Hsuan1,Lin Chun-Yu12ORCID,Lee Jih-Chin3,Lee Chia-Hwa456ORCID,Liu Chia-Lin7,Huang Sing-Han1ORCID,Lee Jung-Yu1,Lai Wen-Sen8ORCID,Yang Jinn-Moon129

Affiliation:

1. Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan

2. Center for Intelligent Drug Systems and Smart Bio-Devices, National Yang Ming Chiao Tung University, Hsinchu 300, Taiwan

3. Department of Otolaryngology—Head and Neck Surgery, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan

4. School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan

5. TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 110, Taiwan

6. Ph.D. Program in Medicine Biotechnology, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan

7. Graduate Institute of Life Sciences, National Defense Medical Center, Taipei 114, Taiwan

8. Department of Otolaryngology—Head and Neck Surgery, Taichung Armed Forces General Hospital, Taichung 404, Taiwan

9. Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-Sen University, Kaohsiung 804, Taiwan

Abstract

Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan–Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.

Funder

Center for Intelligent Drug Systems and Smart Bio-devices

Research Center for Epidemic Prevention Science

Joint Research Center for AI Technology

Overseas Project for Post Graduate Research

Artificial Intelligence to Precision Health: Integrating Dynamic Physiological Signals and EMR to build a Medical Digital Twins Platform

National Yang Ming Chiao Tung University and National Health Research Institutes

Publisher

MDPI AG

Subject

Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis

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