Proteomic Analysis of Biomarkers Predicting Treatment Response in Patients with Head and Neck Cancers

Head and neck cancers (HNCs) are the sixth most commonly diagnosed cancer and the eighth leading cause of cancer-related mortality worldwide, with squamous cell carcinoma being the most prevalent type. The global incidence of HNCs is steadily increasing, projected to rise by approximately 30% per year by 2030, a trend observed in both developed and undeveloped countries. This study involved serum proteomic profiling to identify predictive clinical biomarkers in cancer patients undergoing chemoradiotherapy (CRT). Fifteen HNC patients at Tikur Anbessa Specialized Hospital, Radiotherapy (RT) center in Addis Ababa were enrolled. Serum samples were collected before and after RT, and patients were classified as responders (R) or non-responders (NR). Protein concentrations in the serum were determined using the Bradford assay, followed by nano-HPLC-MS/MS for protein profiling. Progenesis QI for proteomics identified 55 differentially expressed proteins (DEPs) between R and NR, with a significance of <i>p</i> < 0.05 and a fold-change (FC) ≥ 1.5. The top five-up-regulated proteins included <i>MAD1L1</i>, <i>PSMC2</i>, <i>TRIM29</i>, <i>C5</i>, and <i>SERPING1</i>, while the top five-down-regulated proteins were <i>RYR1</i>, <i>HEY2</i>, <i>HIF1A</i>, <i>TF</i>, and <i>CNN3</i>. Notably, about 16.4% of the DEPs were involved in cellular responses to DNA damage from cancer treatments, encompassing proteins related to deoxyribonucleic acid (DNA) damage sensing, checkpoint activation, DNA repair, and apoptosis/cell cycle regulation. The analysis of the relative abundance of ten proteins with high confidence scores identified three DEPs: <i>ADIPOQ</i>, <i>HEY2</i>, and <i>FUT10</i> as potential predictive biomarkers for treatment response. This study highlighted the identification of three potential predictive biomarkers-<i>ADIPOQ</i>, <i>HEY2</i>, and <i>FUT10</i>-through serum proteomic profiling in HNC patients undergoing RT, emphasizing their significance in predicting treatment response.