Abstract
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.
Highlights
Cervical lymph node metastasis is the leading cause of poor prognosis in oral tongue squamous cell carcinoma and occurs in the early stages
Micrometastasis indicates that a small number of cancer cells that have spread from the primary tumor to other parts of the body are too few to be detected by screening or physical examination
Transcriptome data are whole genome-scale molecular profiles generated by high-throughput RNA profiling techniques such as microarrays and RNA sequencing (RNA-seq), which are known to have great potential to identify micrometastasis in cancer p atients[4,5,6]
Summary
Cervical lymph node metastasis is the leading cause of poor prognosis in oral tongue squamous cell carcinoma and occurs in the early stages. 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. Micrometastasis indicates that a small number of cancer cells that have spread from the primary tumor to other parts of the body are too few to be detected by screening or physical examination For this reason, clinicians recommend lymphadenectomy for patients who do not require resection[3]. Despite advances in high-throughput RNA profiling technology, the cost of production per sample is still at a non-negligible level, and the number of genes to consider is relatively large compared to the number of samples, which is a challenge for many researchers. The SLR approach can provide a comprehensive understanding of the underlying mechanisms by which the disease progresses and influences p rognosis[15, 16]
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