Cross validated serum small extracellular vesicle microRNAs for the detection of oropharyngeal squamous cell carcinoma.
Carcinoma, Squamous Cell
/ diagnosis
Extracellular Vesicles
Head and Neck Neoplasms
/ diagnosis
Humans
MicroRNAs
/ genetics
Neoplasm Recurrence, Local
Oropharyngeal Neoplasms
/ diagnosis
Papillomaviridae
Papillomavirus Infections
Reproducibility of Results
Serum
Squamous Cell Carcinoma of Head and Neck
/ diagnosis
Biomarkers
Data analysis
Oropharyngeal squamous cell carcinoma
Serum
microRNAs
Journal
Journal of translational medicine
ISSN: 1479-5876
Titre abrégé: J Transl Med
Pays: England
ID NLM: 101190741
Informations de publication
Date de publication:
10 07 2020
10 07 2020
Historique:
received:
17
05
2020
accepted:
02
07
2020
entrez:
12
7
2020
pubmed:
12
7
2020
medline:
15
5
2021
Statut:
epublish
Résumé
Oropharyngeal squamous cell carcinoma (OPSCC) is often diagnosed at an advanced stage because the disease often causes minimal symptoms other than metastasis to neck lymph nodes. Better tools are required to assist with the early detection of OPSCC. MicroRNAs (miRNAs, miRs) are potential biomarkers for early head and neck squamous cell cancer diagnosis, prognosis, recurrence, and presence of metastatic disease. However, there is no widespread agreement on a panel of miRNAs with clinically meaningful utility for head and neck squamous cell cancers. This could be due to variations in the collection, storage, pre-processing, and isolation of RNA, but several reports have indicated that the selection and reproducibility of biomarkers has been widely affected by the methods used for data analysis. The primary analysis issues appear to be model overfitting and the incorrect application of statistical techniques. The purpose of this study was to develop a robust statistical approach to identify a miRNA signature that can distinguish controls and patients with inflammatory disease from patients with human papilloma virus positive (HPV +) OPSCC. Small extracellular vesicles were harvested from the serum of 20 control patients, 20 patients with gastroesophageal reflux disease (GORD), and 40 patients with locally advanced HPV + OPSCC. MicroRNAs were purified, and expression profiled on OpenArray™. A novel cross validation method, using lasso regression, was developed to stabilise selection of miRNAs for inclusion in a prediction model. The method, named StaVarSel (for Stable Variable Selection), was used to derive a diagnostic biomarker signature. A standard cross validation approach was unable to produce a biomarker signature with good cross validated predictive capacity. In contrast, StaVarSel produced a regression model containing 11 miRNA ratios with potential clinical utility. Sample permutations indicated that the estimated cross validated prediction accuracy of the 11-miR-ratio model was not due to chance alone. We developed a novel method, StaVarSel, that was able to identify a panel of miRNAs, present in small extracellular vesicles derived from blood serum, that robustly cross validated as a biomarker for the detection of HPV + OPSCC. This approach could be used to derive diagnostic biomarkers of other head and neck cancers.
Sections du résumé
BACKGROUND
Oropharyngeal squamous cell carcinoma (OPSCC) is often diagnosed at an advanced stage because the disease often causes minimal symptoms other than metastasis to neck lymph nodes. Better tools are required to assist with the early detection of OPSCC. MicroRNAs (miRNAs, miRs) are potential biomarkers for early head and neck squamous cell cancer diagnosis, prognosis, recurrence, and presence of metastatic disease. However, there is no widespread agreement on a panel of miRNAs with clinically meaningful utility for head and neck squamous cell cancers. This could be due to variations in the collection, storage, pre-processing, and isolation of RNA, but several reports have indicated that the selection and reproducibility of biomarkers has been widely affected by the methods used for data analysis. The primary analysis issues appear to be model overfitting and the incorrect application of statistical techniques. The purpose of this study was to develop a robust statistical approach to identify a miRNA signature that can distinguish controls and patients with inflammatory disease from patients with human papilloma virus positive (HPV +) OPSCC.
METHODS
Small extracellular vesicles were harvested from the serum of 20 control patients, 20 patients with gastroesophageal reflux disease (GORD), and 40 patients with locally advanced HPV + OPSCC. MicroRNAs were purified, and expression profiled on OpenArray™. A novel cross validation method, using lasso regression, was developed to stabilise selection of miRNAs for inclusion in a prediction model. The method, named StaVarSel (for Stable Variable Selection), was used to derive a diagnostic biomarker signature.
RESULTS
A standard cross validation approach was unable to produce a biomarker signature with good cross validated predictive capacity. In contrast, StaVarSel produced a regression model containing 11 miRNA ratios with potential clinical utility. Sample permutations indicated that the estimated cross validated prediction accuracy of the 11-miR-ratio model was not due to chance alone.
CONCLUSIONS
We developed a novel method, StaVarSel, that was able to identify a panel of miRNAs, present in small extracellular vesicles derived from blood serum, that robustly cross validated as a biomarker for the detection of HPV + OPSCC. This approach could be used to derive diagnostic biomarkers of other head and neck cancers.
Identifiants
pubmed: 32650803
doi: 10.1186/s12967-020-02446-1
pii: 10.1186/s12967-020-02446-1
pmc: PMC7350687
doi:
Substances chimiques
MicroRNAs
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
280Commentaires et corrections
Type : ErratumIn
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