Next Article in Journal
TPMT*3C as a Predictor of 6-Mercaptopurine-Induced Myelotoxicity in Thai Children with Acute Lymphoblastic Leukemia
Previous Article in Journal
Low Albumin, Low Bilirubin, and High Alfa-Fetoprotein Are Associated with a Rapid Renal Function Decline in a Large Population Follow-Up Study
Previous Article in Special Issue
Identification of Therapeutic Targets for the Selective Killing of HBV-Positive Hepatocytes
Article

A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications

1
The Ph.D. Program for Translational Medicine, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 11031, Taiwan
2
Division of Oral and Maxillofacial Surgery, Department of Dentistry, Wan Fang Hospital, Taipei Medical University, Taipei 11600, Taiwan
3
Division of Oral and Maxillofacial Surgery, Department of Dentistry, Taipei Medical University Hospital, Taipei Medical University, Taipei 11031, Taiwan
4
Genomics Research Center, Academia Sinica, Taipei 115024, Taiwan
5
Department of Biochemistry, College of Medicine, Kaohsiung Medical University, Kaohsiung 807378, Taiwan
6
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No.172-1, Sec. 2, Keelung Rd., Taipei 106339, Taiwan
*
Authors to whom correspondence should be addressed.
Academic Editors: Ali Salehzadeh-Yazdi and Mohieddin Jafari
J. Pers. Med. 2021, 11(8), 782; https://doi.org/10.3390/jpm11080782
Received: 6 July 2021 / Revised: 3 August 2021 / Accepted: 4 August 2021 / Published: 11 August 2021
(This article belongs to the Special Issue Systems Medicine and Bioinformatics)
Survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method for discovering gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). A cutoff point is usually used in survival analysis for patient dichotomization when using continuous gene expression values. There is some optimization software for cutoff determination. However, the software’s predetermined cutoffs are usually set at the medians or quantiles of gene expression values. There are also few clinicopathological features available in pre-processed datasets. We applied an in-house workflow, including data retrieving and pre-processing, feature selection, sliding-window cutoff selection, Kaplan–Meier survival analysis, and Cox proportional hazard modeling for biomarker discovery. In our approach for the TCGA HNSCC cohort, we scanned human protein-coding genes to find optimal cutoff values. After adjustments with confounders, clinical tumor stage and surgical margin involvement were found to be independent risk factors for prognosis. According to the results tables that show hazard ratios with Bonferroni-adjusted p values under the optimal cutoff, three biomarker candidates, CAMK2N1, CALML5, and FCGBP, are significantly associated with overall survival. We validated this discovery by using the another independent HNSCC dataset (GSE65858). Thus, we suggest that transcriptomic analysis could help with biomarker discovery. Moreover, the robustness of the biomarkers we identified should be ensured through several additional tests with independent datasets. View Full-Text
Keywords: head and neck squamous cell carcinoma (HNSCC); the Cancer Genome Atlas (TCGA); transcriptomic analysis; survival analysis; optimal cutoff; effect size; calcium/calmodulin dependent protein kinase II inhibitor 1 (CAMK2N1); calmodulin like 5 (CALML5); Fc fragment of IgG binding protein (FCGBP); mindfulness meditation head and neck squamous cell carcinoma (HNSCC); the Cancer Genome Atlas (TCGA); transcriptomic analysis; survival analysis; optimal cutoff; effect size; calcium/calmodulin dependent protein kinase II inhibitor 1 (CAMK2N1); calmodulin like 5 (CALML5); Fc fragment of IgG binding protein (FCGBP); mindfulness meditation
Show Figures

Graphical abstract

MDPI and ACS Style

Chi, L.-H.; Wu, A.T.H.; Hsiao, M.; Li, Y.-C. A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications. J. Pers. Med. 2021, 11, 782. https://doi.org/10.3390/jpm11080782

AMA Style

Chi L-H, Wu ATH, Hsiao M, Li Y-C. A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications. Journal of Personalized Medicine. 2021; 11(8):782. https://doi.org/10.3390/jpm11080782

Chicago/Turabian Style

Chi, Li-Hsing, Alexander T.H. Wu, Michael Hsiao, and Yu-Chuan Li 2021. "A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications" Journal of Personalized Medicine 11, no. 8: 782. https://doi.org/10.3390/jpm11080782

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop