A Meta-analysis of Gene Expression of Biomarkers and Construction of Multigene Prognosis Assessment Model in Nasopharyngeal Carcinoma - Sim, Chor Chien (2020)
Nasopharyngeal carcinoma (NPC) is a common cancer in Southeast Asia, including Malaysia. Overall survival rate of NPC patients is poor due to late diagnosis at the advanced stage. Thus, definitive prognostic biomarkers and multigene prognosis assessment models are crucial to better manage NPC. Cyclooxygenase-2 (COX-2) was identified as a significant prognostic biomarker via meta-analysis in head and neck (HNC) cancer, oral squamous cell carcinoma (OSCC) and breast cancer. Prognostic significance of COX-2 in NPC regarding lymph node metastasis has been done via meta-analysis. However, prognostic significance of COX-2 in NPC in term of overall survival (OS) rate analysed via meta-analysis remains unexplored. This study aims to evaluate COX-2 expression with OS and treatment response via meta-analysis by referring to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. Results show that over expression of COX-2 predicts worse survival rate outcome for NPC (hazard ratio greater than one) but not treatment response (odd ratio less than one). Hazard ratio is a time dependent statistic measure while odd ratio is a time independent statistic measure. Hence, evaluation of COX-2 expression in NPC patients is important for management and treatment of NPC. Most existing NPC prognosis assessment models are constructed with small sample sizes and thus less relevant and applicable for medical practices. This study therefore aimed to construct a multigene NPC prognosis assessment model by pooling results of more significant meta-analysis studies on prognostic biomarkers of NPC via modification of PRISMA. Through this study, a multigene NPC prognosis assessment model consisting of ten prognostic biomarkers, including COX-2 done from this study is constructed. OS of NPC patients can be determined computationally as pooled hazard ratios (HRs) via this model by selecting the over expressed biomarker(s). The model can also predict the NPC prognosis assessment risk score of the patient too. Through this model, patients can estimate the overall survival rate time effectively. To provide understanding and explanation to the multigene NPC prognosis assessment model generated computationally, pathways and interactions involved by all the prognostic biomarkers are studied to determine the physiological interactions among all the prognostic biomarkers incorporated. The information on the pathways and interactions are extracted from the literature and summarized as a molecular pathway network. The pathway network model explains the possible complex interplay among different prognostic biomarkers in the oncogenesis of NPC.