Patients with head and neck squamous cell carcinoma are at increased risk of developing a second primary malignancy, which is associated with poor prognosis and early death. To help improve clinical outcome, we aimed to identify biomarkers for second primary malignancy risk prediction using the routinely obtained formalin-fixed paraffin-embedded tissues of the index head and neck cancer. Liquid chromatography-tandem mass spectrometry was initially performed for candidate biomarker discovery in 16 pairs of primary cancer tissues and their matched normal mucosal epithelia from head and neck squamous cell carcinoma patients with or without second primary malignancy. The 32 candidate proteins differentially expressed between head and neck cancers with and without second primary malignancy were identified. Among these, 30 selected candidates and seven more from literature review were further studied using NanoString nCounter gene expression assay in an independent cohort of 49 head and neck cancer patients. Focusing on the p16-negative cases, we showed that a multivariate logistic regression model comprising the expression levels of ITPR3, KMT2D, EMILIN1, and the patient's age can accurately predict second primary malignancy occurrence with 88% sensitivity and 75% specificity. Furthermore, using Cox proportional hazards regression analysis and survival analysis, high expression levels of ITPR3 and DSG3 were found to be significantly associated with shorter time to second primary malignancy development (log-rank test P = 0.017). In summary, we identified a set of genes whose expressions may serve as the prognostic biomarkers for second primary malignancy occurrence in head and neck squamous cell carcinomas. In combination with the histopathologic examination of index tumor, these biomarkers can be used to guide the optimum frequency of second primary malignancy surveillance, which may lead to early diagnosis and better survival outcome.

With the wealth of data accumulated from completely sequenced genomes and other high-throughput experiments, global studies of biological systems, by simultaneously investigating multiple biological entities (e.g. genes, transcripts, proteins), has become a routine. Network representation is frequently used to capture the presence of these molecules as well as their relationship. Network biology has been widely used in molecular biology and genetics, where several network properties have been shown to be functionally important. Here, we discuss how such methodology can be useful to translational biomedical research, where scientists traditionally focus on one or a small set of genes, diseases, and drug candidates at any one time. We first give an overview of network representation frequently used in biology: what nodes and edges represent, and review its application in preclinical research to date. Using cancer as an example, we review how network biology can facilitate system-wide approaches to identify targeted small molecule inhibitors. These types of inhibitors have the potential to be more specific, resulting in high efficacy treatments with less side effects, compared to the conventional treatments such as chemotherapy. Global analysis may provide better insight into the overall picture of human diseases, as well as identify previously overlooked problems, leading to rapid advances in medicine. From the clinicians’ point of view, it is necessary to bridge the gap between theoretical network biology and practical biomedical research, in order to improve the diagnosis, prevention, and treatment of the world’s major diseases.