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    • Similar to other solid tumors the

    2018-11-01

    Similar to other solid tumors, the characteristics such as tumor size, tumor differentiation, tumor node numbers, vascular invasion, and metastasis status are important prognosis factors for HCC patients (Noh et al., 2016). These characteristics constitute the tumor-node-metastasis (TNM) classification system for HCC patients (Sobin, 2003). In addition to these tumor characteristics, biomarkers for preserved liver function and the liver damage status of the HCC patients, such as the Child-Pugh stage; the α-fetoprotein (AFP), bilirubin, and albumin levels; and ECOG status are also associated with the prognosis of HCC patients. These additionally markers have led to the establishment of various conventional staging systems, including the Japan Integrated Staging (JIS) system (Kudo et al., 2003), the Barcelona Clinic Liver Cancer (BCLC) classification system (Llovet et al., 1999), the Cancer of the Liver Italian Program (CLIP) scoring system (No-author-listed, 1998) and the Chinese University Prognostic Index (CUPI) scoring system (Leung et al., 2002). These systems are widely used to guide the treatment methods and/or predict the outcomes of HCC patients. However, the clinical performance of these systems depends on the patient characteristics, the treatments performed, and the disease etiology of the patients (Marrero et al., 2010; Subramaniam et al., 2013). Moreover, although these staging and scoring systems can stratify the HCC patients into appropriate risk categories, a great deal of divergence remains within each risk category due to the molecular heterogeneity of tumor Finally and the tumor microenvironment (Fridman et al., 2012). An in-depth characterization and understanding of the molecular basis of the tumor and its corresponding microenvironment are critical for improving the diagnosis, identifying prognostic and predictive biomarkers, and developing effective therapeutic strategies (Koren and Bentires-Alj, 2015). Genome-wide expression profiling methods provide detailed information regarding the diversity of diseases and are valuable for the disease diagnosis, therapeutic response prediction and prognosis evaluation. Currently, many studies have assessed the prognostic effects of array-based gene expression signatures obtained from HCC tumors (Andersen et al., 2010; Boyault et al., 2007; Cairo et al., 2008; Chew et al., 2012; Chiang et al., 2008; Coulouarn et al., 2008; Hoshida et al., 2009; Iizuka et al., 2003; Kaposi-Novak et al., 2006; Kim et al., 2012; Ko et al., 2014; Kurokawa et al., 2004; Lee et al., 2004; Lim et al., 2013; Minguez et al., 2011; Roessler et al., 2010; Roessler et al., 2012; Sakai et al., 2008; Villanueva et al., 2008; Wang et al., 2007; Woo et al., 2010; Woo et al., 2008; Yamashita et al., 2008; Ye et al., 2003; Yoshioka et al., 2009) or from adjacent, non-tumor tissues (Budhu et al., 2006; Hoshida et al., 2008; Okamoto et al., 2006). These investigations have identified gene signatures that predict recurrence and/or mortality for HCC patients; however, none has entered clinical use, perhaps due to their low reproducibility and lack of standardized determination methods. Interestingly, overlapping genes between these gene signatures are rare, and this rarity may be related to the disease stage of the patients, the main hypothesis of the study, the platform applied, and/or the data mining methods that were utilized. However, these gene signatures with little overlap might be functionally linked with each other and form a systematic molecular regulation network that is robust for patient stratification. Herein, we systematically evaluated the generalization of the prognostic gene signatures in independent HCC case cohorts and established a functional protein-protein interaction (PPI) network for gene signatures that have reproducible prognostic values. Through the PPI network topological analysis, we identified those critical molecules in the network and determined their values as biomarkers for prognosis assessment or as therapeutic targets for HCC patients.