Unknown

Dataset Information

0

Salivary Metabolomics for Prognosis of Oral Squamous Cell Carcinoma.


ABSTRACT: This study aimed to identify salivary metabolomic biomarkers for predicting the prognosis of oral squamous cell carcinoma (OSCC) based on comprehensive metabolomic analyses. Quantified metabolomics data of unstimulated saliva samples collected from patients with OSCC (n = 72) were randomly divided into the training (n = 35) and validation groups (n = 37). The training data were used to develop a Cox proportional hazards regression model for identifying significant metabolites as prognostic factors for overall survival (OS) and disease-free survival. Moreover, the validation group was used to develop another Cox proportional hazards regression model using the previously identified metabolites. There were no significant between-group differences in the participants' characteristics, including age, sex, and the median follow-up periods (55 months [range: 3-100] vs. 43 months [range: 0-97]). The concentrations of 5-hydroxylysine (p = 0.009) and 3-methylhistidine (p = 0.012) were identified as significant prognostic factors for OS in the training group. Among them, the concentration of 3-methylhistidine was a significant prognostic factor for OS in the validation group (p = 0.048). Our findings revealed that salivary 3-methylhistidine is a prognostic factor for OS in patients with OSCC.

SUBMITTER: Ishikawa S 

PROVIDER: S-EPMC8769065 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

altmetric image

Publications

Salivary Metabolomics for Prognosis of Oral Squamous Cell Carcinoma.

Ishikawa Shigeo S   Sugimoto Masahiro M   Konta Tsuneo T   Kitabatake Kenichiro K   Ueda Shohei S   Edamatsu Kaoru K   Okuyama Naoki N   Yusa Kazuyuki K   Iino Mitsuyoshi M  

Frontiers in oncology 20220105


This study aimed to identify salivary metabolomic biomarkers for predicting the prognosis of oral squamous cell carcinoma (OSCC) based on comprehensive metabolomic analyses. Quantified metabolomics data of unstimulated saliva samples collected from patients with OSCC (n = 72) were randomly divided into the training (n = 35) and validation groups (n = 37). The training data were used to develop a Cox proportional hazards regression model for identifying significant metabolites as prognostic facto  ...[more]

Similar Datasets

| S-EPMC8225878 | biostudies-literature
| S-EPMC6533497 | biostudies-literature
| S-EPMC5122805 | biostudies-literature
| S-EPMC7913841 | biostudies-literature
| S-EPMC7873065 | biostudies-literature
| S-EPMC3032819 | biostudies-literature
| S-EPMC8267678 | biostudies-literature
| S-EPMC5881539 | biostudies-literature
| S-EPMC10023534 | biostudies-literature