Subnetwork representation learning for discovering network biomarkers in predicting lymph node metastasis in early oral cancer

Cervical lymph node metastasis is the leading cause of poor prognosis in oral tongue squamous cell carcinoma and also occurs in the early stages. The current clinical diagnosis depends on a physical examination that is not enough to determine whether micrometastasis remains. The transcriptome profiling technique has shown great potential for predicting micrometastasis by capturing the dynamic activation state of genes. However, there are several technical challenges in using transcriptome data to model patient conditions: (1) An Insufficient number of samples compared to the number of genes, (2) Complex dependence between genes that govern the cancer phenotype, and (3) Heterogeneity between patients between cohorts that differ geographically and racially. We developed a computational framework to learn the subnetwork representation of the transcriptome to discover network biomarkers and determine the potential of metastasis in early oral tongue squamous cell carcinoma. Our method achieved high accuracy in predicting the potential of metastasis in two geographically and racially different groups of patients. The robustness of the model and the reproducibility of the discovered network biomarkers show great potential as a tool to diagnose lymph node metastasis in early oral cancer.

The Role of Arginine Metabolism in Oral Tongue Squamous Cell Carcinoma

Early diagnosis and treatment do not prevent the high morbidity and poor prognosis of oral tongue squamous cell carcinoma (TSCC). Earlier studies have shown that ARG1 signaling is deregulated in TSCC. Here, we investigated the complexity of ARG1 metabolism in this cancer subsite to appreciate the therapeutic potential of this potential biological vulnerability. Various functional studies show that ARG1 overexpression in oral cancer cells inhibits cell proliferation and invasion compared with controls. Further, RNA-sequencing revealed numerous differentially expressed genes (DEGs) and associated networks were dysregulated by ARG1 overexpression, including hypoxia-inducible factor (HIFα) signaling, the natural killer cell signaling pathway and interferon signaling. Our work provides a foundation for understanding the mechanism of action of disrupted arginine metabolism in oral tongue squamous cell carcinoma. This may impact the community for developing further therapeutic approaches.