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Among all cancers occurring in the head and neck region, oral squamous cell carcinoma (OSCC) is the most common oral malignant tumours characterized by its aggressiveness and metastasis. The development of transcriptomics technology has greatly facilitated the diagnosis of various cancers. However, identifying genetic biomarkers is limited by data from a single batch of OSCC samples, and integrating analysis across different platforms remains a great challenge. In this study, we integrated five OSCC transcriptome datasets using an innovative strategy capable of mitigating batch effect, and extracting information from different datasets based on changes in the relative expression of gene pairs. By leveraging a machine learning method, we developed a prediction model including 27 differential gene pairs (DGPs) to discriminate OSCC from control samples, achieving an area under the receiver operating characteristic curve (AUC) of 0.8987 for the training set. Moreover, the model demonstrated commendable performance in four external validation sets, with AUCs of 0.9926, 0.9688, 0.8052 and 0.8565, respectively. Subsequently, a prognostic model was constructed based on six key gene pairs through univariate and multivariate Cox regression analysis. The AUCs of the model at 1‐year and 3‐year overall survival time prediction were 0.717 and 0.779 in an independent dataset. Our result demonstrates the effectiveness of this new method of integrating data and identifying DGPs. Using DGPs can significantly improve the performance of both diagnostic and prognostic models. [ABSTRACT FROM AUTHOR]

Service providers today rely on service-chaining policy enforced by omnipresent middleboxes (MBoxes) to achieve crucial performance and security guarantees in their networks. On the other hand, they are currently struggling to satisfy growing traffic demands on their traditional services. Given the promise of emerging technologies, such as SDN and NFV, we argue that the enforcement of service-chaining policy can greatly benefit from the integration of them. However, it is challenging to synthesize these two technologies due to the need to meet the requirements of flexibility, scalability, and auto-provisioning without modifying SDN standards (e.g. OpenFlow) and MBoxes. In this article we present the design of OpenSCaaS, an open platform for service chain as a service, by using the tangible capabilities of SDN together with NFV. OpenSCaaS allows service-providers to design a general architecture based on SDN to achieve flexibility, encapsulate Service Chain Identifier (SC-ID) based on Source MAC to guarantee scalability, and realize auto-provisioning based on NFV to offer adaptability. In doing so, we take a significant step to address industry and academic concerns about the integration of SDN and NFV. [ABSTRACT FROM PUBLISHER]