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Data mining of an acoustic biomarker in tongue cancers and its clinical validation.

ABSTRACT: The promise of speech disorders as biomarkers in clinical examination has been identified in a broad spectrum of neurodegenerative diseases. However, to the best of our knowledge, a validated acoustic marker with established discriminative and evaluative properties has not yet been developed for oral tongue cancers. Here we cross-sectionally collected a screening dataset that included acoustic parameters extracted from 3 sustained vowels /ɑ/, /i/, /u/ and binary perceptual outcomes from 12 consonant-vowel syllables. We used a support vector machine with linear kernel function within this dataset to identify the formant centralization ratio (FCR) as a dominant predictor of different perceptual outcomes across gender and syllable. The Acoustic analysis, Perceptual evaluation and Quality of Life assessment (APeQoL) was used to validate the FCR in 33 patients with primary resectable oral tongue cancers. Measurements were taken before (pre-op) and four to six weeks after (post-op) surgery. The speech handicap index (SHI), a speech-specific questionnaire, was also administrated at these time points. Pre-op correlation analysis within the APeQoL revealed overall consistency and a strong correlation between FCR and SHI scores. FCRs also increased significantly with increasing T classification pre-operatively, especially for women. Longitudinally, the main effects of T classification, the extent of resection, and their interaction effects with time (pre-op vs. post-op) on FCRs were all significant. For pre-operative FCR, after merging the two datasets, a cut-off value of 0.970 produced an AUC of 0.861 (95% confidence interval: 0.785-0.938) for T_3-4 patients. In sum, this study determined that FCR is an acoustic marker with the potential to detect disease and related speech function in oral tongue cancers. These are preliminary findings that need to be replicated in longitudinal studies and/or larger cohorts.

SUBMITTER: Xiao Y

PROVIDER: S-EPMC8178493 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:Glutathione peroxidase-1 (GPX1) is a member of the GPX family, which considered an enzyme that interacts with oxidative stress. GPX1 differential expression is closely correlated with carcinogenesis and disease progression. In this study, we used bioinformatics analysis to investigate GPX1 expression level and explore the prognostic information in different human cancers. Methods:Expression was analyzed via the Oncomine database and Gene Expression Profiling Interactive Analysis tool, and potential prognostic analysis was evaluated using the UALCAN, GEPIA, and DriverDBv3 databases. Then, the UALCAN database was used to find the promoter methylation of GPX1 in defied cancer types. While GPX1 related functional networks were found within the GeneMANIA interactive tool and Cytoscape software. Moreover, Metascape online website was used to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Results:We found that GPX1 was commonly overexpressed in most human cancers. High expression of GPX1 could lead to poor outcomes in Brain Lower Grade Glioma, while GPX1 over expression was correlated with better prognosis in Kidney renal papillary cell carcinoma (KIPP). High GPX1 expression was marginally associated with poor prognosis in acute myeloid leukemia (AML). Gene regulation network suggested that GPX1 mainly involved in pathways including the glutathione metabolism, ferroptosis, TP53 regulates metabolic genes, reactive oxygen species (ROS) metabolic process, and several other signaling pathways. Conclusions:Our findings revealed that GPX1 showed significant expression differences among cancers and served as a prognostic biomarker for defined cancer types. The data mining effectively revealed useful information about GPX1 expression, prognostic values, and potential functional networks in cancers, thus providing researchers with an available way to further explore the mechanism underlying carcinogenesis of genes of interest in different cancers.

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Data mining of an acoustic biomarker in tongue cancers and its clinical validation.

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