Dataset Information

Identification and Validation of Plasma Lipid Biomarkers for Oral Cancer Diagnosis

ABSTRACT:

Despite lipid metabolic changes in oral cancer (OC), their potential as diagnostic markers remains unclear. This study assessed the diagnostic potential of metabolites for OC detection. Plasma metabolites of patients with OC and healthy controls (HC) were profiled using untargeted and targeted metabolomics in discovery (182 OC, 364 HC) and external validation (52 OC, 52 HC) sets. Key biomarkers were selected via machine learning. Carnitine palmitoyltransferase 1 (CPT1) expression was assessed using enzyme-linked immunosorbent assay, immunohistochemistry, and public transcriptomic datasets. Functional assays based on CPT1 inhibition were conducted in OC cell lines. Machine learning identified OC diagnostic metabolites with high accuracy (AUC = 0.995) and targeted validation confirmed consistent trends. Three acylcarnitines—decanoyl-, octanoyl-, and hexanoylcarnitine—were downregulated in patient plasma, showing strong diagnostic performance (AUC = 0.941, 95% CI: 0.877–0.988) in external validation. Lipidomic and functional analyses revealed altered β-oxidation and glycerophospholipid metabolism, with CPT1 acting as a key regulator. CPT1 and acylcarnitines were abundant in OC tissues and cells. CPT1 inhibition suppressed OC cell growth and altered acylcarnitine levels. Our findings highlight three acylcarnitines as promising diagnostic biomarkers of OC and identify CPT1 as a central regulator of lipid metabolism, underscoring its relevance in OC pathogenesis and clinical application.

INSTRUMENT(S): Liquid Chromatography MS -

PROVIDER: MTBLS12655 | MetaboLights | 2026-05-02

REPOSITORIES: MetaboLights

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			Action	DRS
	a_MTBLS12655_LC-MS___metabolite_profiling.txt	Txt
	i_Investigation.txt	Txt
	m_MTBLS12655_LC-MS___metabolite_profiling_v2_maf.tsv	Tabular
	s_MTBLS12655.txt	Txt

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Project description:Objectives Our goal was to evaluate the diagnostic value of DNA methylation analysis in combination with machine learning to differentiate pleural mesothelioma (PM) from important histopathological mimics. Material and methods DNA methylation data of PM, lung adenocarcinomas, lung squamous cell carcinomas and chronic pleuritis was used to train a random forest as well as a support vector machine. These classifiers were validated using an independent validation cohort including pleural carcinosis and pleomorphic variants of lung adeno- and squamous cell carcinomas. Furthermore, we used a deconvolution method to estimate the composition of the tumor microenvironment. Results T-distributed stochastic neighbor embedding clearly separated PM from lung adenocarcinomas and squamous cell carcinomas, but there was a considerable overlap between chronic pleuritis specimens and PM with low tumor cell content. While both machine learning algorithms achieved comparable accuracies in a nested cross validation on the training cohort (random forest: 94.9%; support vector machine: 95.5%), the support vector machine outperformed the random forest in distinguishing PM from chronic pleuritis. Differential methylation analysis revealed promoter hypermethylation in PM specimens, including the tumor suppressor genes BCL11B, EBF1, FOXA1, and WNK2. Furthermore, we observed comparable accuracies for the support vector machine on the validation cohort (97.1%) while the random forest performed considerably worse (89.9%). Deconvolution of the stromal and immune cell composition revealed higher rates of regulatory T-cells and endothelial cells in tumor specimens and a heterogenous inflammation including macrophages, B-cells and natural killer cells in chronic pleuritis. Conclusion DNA methylation in combination with machine learning is a promising tool to reliably differentiate PM from chronic pleuritis and lung cancer, including pleomorphic carcinomas. Furthermore, our study highlights new candidate genes for PM carcinogenesis and shows that deconvolution of DNA methylation data can provide reasonable insights into the composition of the tumor microenvironment.

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Identification and Validation of Plasma Lipid Biomarkers for Oral Cancer Diagnosis

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