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  • Introduction Osteosarcoma OS is the most commonly diagnosed


    Introduction Osteosarcoma (OS) is the most commonly diagnosed histological form of primary bone tumor with high morbidity, and it is mainly prevalent in teenagers and young people [1]. Pain is the most common early symptom of OS and can cause fracture of the affected bone. Currently, multiple therapeutic strategies for OS, for example, surgical resection, chemotherapy and radiotherapy, have significantly improved the prognosis of patients with OS [2]. However, the overall survival rates rarely exceed 60–65% [3]. Moreover, a significant portion of OS patients develop metastasis even after curative resection of the primary tumor. There is still a long way to go for the management of OS [4]. Therefore, with an attempt to continue to make progress in the diagnosis and management of OS, identification of sensitive and specific minimally invasive signatures is one of the most important challenges. Genetic aberration has been demonstrated as an important factor that may play significant roles in OS pathogenesis. For instance, Zhu and colleagues [5] have reported that SOX9 is over-expressed in OS tissues compared with controls. Moreover, it is indicated that FOXM1 is up-regulated, which suggests FOXM1 might be an valuable bio-signature for OS [6]. Nevertheless, these full text(pdf) do not treat the OS efficiently or selectively, because molecules frequently don’t work individually, yet co-operate with the other genes. Additionally, genetic factors can disturb the protein levels, thereby in turn perturb the molecule interactions. Network is characterized by the complicated interactions and the complex interwoven relationships that control cellular functions [7]. Hence, understanding the networks will be beneficial to provide new insights to explore the molecular pathogenesis of OS. The concept of differential co-expression network (DCN) has been employed to the studies of OS, due to statistical confidence of single connections, overlap with protein interaction, and mathematical convenience [8]. In addition, improving our knowledge of gene function in uncharacterized genes is a major task [9]. Remarkably, gene interactions can be applied to deduce functional relationships based on a principle known as “guilt by association” (GBA) [10]. GBA has been indicated to predict gene function in various types of biological networks, for example, gene co-expression network [11].
    Materials and methods
    Discussion Compared to gene focused researches, gene set and gene signature focused functional investigations appear much rewarding in understanding functional insights [24], and integrative functional genomic analysis of tumors promotes the understanding of the molecular mechanisms of cancers [25]. Network modeling on the basis of co-expression pattern analysis has been widely applied in a variety of cancers to understand the biology processes of cancers, and to obtain clinical insights [26]. Recently, a large amount of techniques have been created to extend GBA to indirect connections to predict gene functions [27,28]. Gene interactions can be applied to deduce functional relationships based on a principle known as GBA [10]. The GBA principle forms the basis for most gene function prediction methods, which typically use relational information (e.g. interactions) to predict new gene membership in gene function categories [29]. However, combination of gene function prediction and network analysis are sparse. Generally speaking, network based-GBA analysis method can make exhaustively examining issues faster and easier than the simple GBA approach. Thus, in this work, we combined GBA principle with DCN-based analysis to further explore both direct and indirect optimal gene functions for OS on the basis of GO information as well as gene expression data. Finally, a total of 105 GO terms were identified based on AUC >0.5, which had a good classification ability. Moreover, 2 out of 105 GO terms had the AUC >0.7 and were determined as the optimal gene functions including angiogenesis and regulation of immune system process.