Emmanuelle M L Ruiz1, Tianhua Niu2, Mourad Zerfaoui1, Muthusamy Kunnimalaiyaan1, Paul L Friedlander3, Asim B Abdel-Mageed4, Emad Kandil5. 1. Department of Surgery, Division of General, Endocrine and Oncological Surgery, Tulane University School of Medicine, New Orleans, LA. 2. Department of Biochemistry and Molecular Biology, Tulane University School of Medicine, New Orleans, LA; Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA. 3. Department of Otolaryngology, Tulane University School of Medicine, New Orleans, LA. 4. Department of Urology, Tulane University School of Medicine, New Orleans, LA. 5. Department of Surgery, Division of General, Endocrine and Oncological Surgery, Tulane University School of Medicine, New Orleans, LA. Electronic address: ekandil@tulane.edu.
Abstract
BACKGROUND: Although well-differentiated papillary thyroid cancer may remain indolent, lymph node metastases and the recurrence rates are approximately 50% and 20%, respectively. No current biomarkers are able to predict metastatic lymphadenopathy and recurrence in early stage papillary thyroid cancer. Hence, identifying prognostic biomarkers predicting cervical lymph-node metastases would prove very helpful in determining treatment. METHODS: The database of the Cancer Genome Atlas included 495 papillary thyroid cancer samples. Using this database, we developed a machine learning model to define a gene signature that could predict lymph-node metastasis (N0 or N1). Kruskal-Wallis tests, univariate and multivariate logistic and Cox regression models, and Kaplan-Meier analyses were performed to correlate the gene signature with clinical outcomes. RESULTS: We identified a panel of 25 genes and constructed a risk score that can differentiate N0 and N1 papillary thyroid cancer samples (P < .001) with a sensitivity of 86%, a specificity of 62%, a positive predictive value of 93%, and a negative predictive value of 42%. This panel represents an independent biomarker to predict metastatic lymphadenopathy (OR = 8.06, P < .001) specifically in patients with T1 lesions (OR = 7.65, P = .002) and disease-free survival (HR = 2.64, P = .043). CONCLUSION: This novel 25-gene panel may be used as a potential prognostic marker for accurately predicting lymph-node metastasis and disease-free survival in patients with early-stage papillary thyroid cancer.
BACKGROUND: Although well-differentiated papillary thyroid cancer may remain indolent, lymph node metastases and the recurrence rates are approximately 50% and 20%, respectively. No current biomarkers are able to predict metastatic lymphadenopathy and recurrence in early stage papillary thyroid cancer. Hence, identifying prognostic biomarkers predicting cervical lymph-node metastases would prove very helpful in determining treatment. METHODS: The database of the Cancer Genome Atlas included 495 papillary thyroid cancer samples. Using this database, we developed a machine learning model to define a gene signature that could predict lymph-node metastasis (N0 or N1). Kruskal-Wallis tests, univariate and multivariate logistic and Cox regression models, and Kaplan-Meier analyses were performed to correlate the gene signature with clinical outcomes. RESULTS: We identified a panel of 25 genes and constructed a risk score that can differentiate N0 and N1 papillary thyroid cancer samples (P < .001) with a sensitivity of 86%, a specificity of 62%, a positive predictive value of 93%, and a negative predictive value of 42%. This panel represents an independent biomarker to predict metastatic lymphadenopathy (OR = 8.06, P < .001) specifically in patients with T1 lesions (OR = 7.65, P = .002) and disease-free survival (HR = 2.64, P = .043). CONCLUSION: This novel 25-gene panel may be used as a potential prognostic marker for accurately predicting lymph-node metastasis and disease-free survival in patients with early-stage papillary thyroid cancer.
Authors: Venkatesan Chandran; M G Sumithra; Alagar Karthick; Tony George; M Deivakani; Balan Elakkiya; Umashankar Subramaniam; S Manoharan Journal: Biomed Res Int Date: 2021-05-04 Impact factor: 3.411