Proteomics-based Predictive Model for the Early Detection of Metastasis and Recurrence in Head and Neck Cancer

Abstract

Background/Aim: Head and neck squamous cell carcinoma (HNSCC) presents high morbidity, an overall poor prognosis and survival, and a compromised quality of life of the survivors. Early tumor detection, prediction of its behavior and prognosis as well as the development of novel therapeutic strategies are urgently needed for a more successful HNSCC management. Materials and Methods: In this study, a proteomics analysis of HNSCC tumor and nontumor samples was performed and a model to predict the risk of recurrence and metastasis development was built. Results: This predictive model presented good accuracy (>80%) and comprises as variables the tumor staging along with DHB12, HMGB3 and COBA1 proteins. Differences at the intensity levels of these proteins were correlated with the development of metastasis and recurrence as well as with patient's survival. Conclusion: The translation of proteomic predictive models to routine clinical practice may contribute to a more precise and individualized clinical management of the HNSCC patients, reducing recurrences and improving patients' quality of life. The capability of generalization of this proteomic model to predict the recurrence and metastases development should be evaluated and validated in other HNSCC populations.

The Prediction of a 3-Protein-Based Model on the Prognosis of Head and Neck Squamous Cell Carcinoma

“…In a unique study, You et al [36] used cDNA microarrays and bioinformatics methods to study radioresistance in head and neck carcinoma and identified 4 key functional pathways and molecular markers that greatly promoted radio-resistance. A recent report by Ribeiro et al [37] studied tumor specimens of 40 patients with HNSCC undergoing tumor resection, and tumoradjacent tissues from 32 of the patients. The authors identified a proteomic signature based on 3 proteins (DHB12, HMGB3, and COBA1) and developed a model that included the 3 proteins and tumor stage that exhibited >80% predictive accuracy for the development of metastasis and recurrence.…”

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“…2. 6 | RNA extraction, RT-PCR, and qPCR RNA from cell pellets was extracted with RNAiso Plus (Takara, #9109), and the concentration was measured. Subsequently, 1 μg RNA was used for reverse transcription to obtain cDNA using PrimeScript RT Master Mix (Takara, #RR036A) according to the manufacturer's protocol.…”

Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis

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