A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications
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...
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MDPI AG
2021-08-01
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| Series: | Journal of Personalized Medicine |
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| Online Access: | https://www.mdpi.com/2075-4426/11/8/782 |
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| author | Li-Hsing Chi Alexander T. H. Wu Michael Hsiao Yu-Chuan (Jack) Li |
| author_facet | Li-Hsing Chi Alexander T. H. Wu Michael Hsiao Yu-Chuan (Jack) Li |
| author_sort | Li-Hsing Chi |
| collection | DOAJ |
| description | 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 <i>p</i> 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. |
| first_indexed | 2024-03-10T08:40:55Z |
| format | Article |
| id | doaj.art-559b1cb0e8cf43d48f4372dff0581a64 |
| institution | Directory Open Access Journal |
| issn | 2075-4426 |
| language | English |
| last_indexed | 2024-03-10T08:40:55Z |
| publishDate | 2021-08-01 |
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| spelling | doaj.art-559b1cb0e8cf43d48f4372dff0581a642023-11-22T08:19:18ZengMDPI AGJournal of Personalized Medicine2075-44262021-08-0111878210.3390/jpm11080782A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic IndicationsLi-Hsing Chi0Alexander T. H. Wu1Michael Hsiao2Yu-Chuan (Jack) Li3The Ph.D. Program for Translational Medicine, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 11031, TaiwanThe Ph.D. Program for Translational Medicine, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 11031, TaiwanGenomics Research Center, Academia Sinica, Taipei 115024, TaiwanThe Ph.D. Program for Translational Medicine, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 11031, TaiwanSurvival 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 <i>p</i> 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.https://www.mdpi.com/2075-4426/11/8/782head and neck squamous cell carcinoma (HNSCC)the Cancer Genome Atlas (TCGA)transcriptomic analysissurvival analysisoptimal cutoffeffect size |
| spellingShingle | Li-Hsing Chi Alexander T. H. Wu Michael Hsiao Yu-Chuan (Jack) Li A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications Journal of Personalized Medicine head and neck squamous cell carcinoma (HNSCC) the Cancer Genome Atlas (TCGA) transcriptomic analysis survival analysis optimal cutoff effect size |
| title | A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications |
| title_full | A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications |
| title_fullStr | A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications |
| title_full_unstemmed | A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications |
| title_short | A Transcriptomic Analysis of Head and Neck Squamous Cell Carcinomas for Prognostic Indications |
| title_sort | transcriptomic analysis of head and neck squamous cell carcinomas for prognostic indications |
| topic | head and neck squamous cell carcinoma (HNSCC) the Cancer Genome Atlas (TCGA) transcriptomic analysis survival analysis optimal cutoff effect size |
| url | https://www.mdpi.com/2075-4426/11/8/782 |
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