Metabolic Patterns of High-Invasive and Low-Invasive Oral Squamous Cell Carcinoma Cells Using Quantitative Metabolomics and 13C-Glucose Tracing

Integrating omics data and machine learning techniques for precision detection of oral squamous cell carcinoma: evaluating single biomarkers

Introduction

Early detection of oral squamous cell carcinoma (OSCC) is critical for improving clinical outcomes. Precision diagnostics integrating metabolomics and machine learning offer promising non-invasive solutions for identifying tumor-derived biomarkers.

Methods

We analyzed a multicenter public dataset comprising 61 OSCC patients and 61 healthy controls. Plasma metabolomics data were processed to extract 29 numerical and 47 ratio features. The Extra Trees (ET) algorithm was applied for feature selection, and the TabPFN model was used for classification and prediction.

Results

The model achieved an area under the curve (AUC) of 93% and an overall accuracy of 76.6% when using top-ranked individual biomarkers. Key metabolic features significantly differentiated OSCC patients from healthy controls, providing a detailed metabolic fingerprint of the disease.

Discussion

Our findings demonstrate the utility of integrating omics data with advanced machine learning techniques to develop accurate, non-invasive diagnostic tools for OSCC. The study highlights actionable metabolic signatures that have potential applications in personalized therapeutics and early intervention strategies.

Glucose Metabolism and Glucose Transporters in Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinoma ranks seventh globally in malignancy prevalence, with persistent high mortality rates despite treatment advancements. Glucose, pivotal in cancer metabolism via the Warburg effect, enters cells via glucose transporters, notably GLUT proteins. Glycolysis, aerobic oxidation, and the pentose phosphate pathway in glucose metabolism significantly impact HNSCC progression. HNSCC exhibits elevated expression of glucose metabolism enzymes and GLUT proteins, correlating with prognosis. Heterogeneity in HNSCC yields varied metabolic profiles, influenced by factors like HPV status and disease stage. This review highlights glucose metabolism's role and potential as therapeutic targets and cancer imaging tracers in HNSCC.

Lactate-driven type I collagen deposition facilitates cancer stem cell-like phenotype of head and neck squamous cell carcinoma

Lactate is known to play a crucial role in the progression of malignancies. However, its mechanism in regulating the malignant phenotype of head and neck squamous cell carcinoma (HNSCC) remains unclear. This study found that lactate increases cancer stem cell (CSC) characteristics of HNSCC by influencing the deposition of type I collagen (Col I). Lactate promotes Col I deposition through two distinct pathways. One is to convert lactate to pyruvate, a substrate for Col I hydroxylation. The other is the activation of HIF1-α and P4HA1, the latter being a rate-limiting enzyme for Col I synthesis. Inhibition of these two pathways effectively counteracts lactate-induced enhanced cell stemness. Further studies revealed that Col I affects CSC properties by regulating cell cycle dynamics. In conclusion, our research proposes that lactate-driven Col I deposition is essential for the acquisition of CSC properties, and lactate-centric Col I deposition may be an effective target for CSCs.