29
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Identification of Biomarkers Correlated with the TNM Staging and Overall Survival of Patients with Bladder Cancer

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objective: To identify candidate biomarkers correlated with clinical prognosis of patients with bladder cancer (BC).

          Methods: Weighted gene co-expression network analysis was applied to build a co-expression network to identify hub genes correlated with tumor node metastasis (TNM) staging of BC patients. Functional enrichment analysis was conducted to functionally annotate the hub genes. Protein-protein interaction network analysis of hub genes was performed to identify the interactions among the hub genes. Survival analyses were conducted to characterize the role of hub genes on the survival of BC patients. Gene set enrichment analyses were conducted to find the potential mechanisms involved in the tumor proliferation promoted by hub genes.

          Results: Based on the results of topological overlap measure based clustering and the inclusion criteria, top 50 hub genes were identified. Hub genes were enriched in cell proliferation associated gene ontology terms (mitotic sister chromatid segregation, mitotic cell cycle and, cell cycle, etc.) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (cell cycle, Oocyte meiosis, etc.). 17 hub genes were found to interact with ≥5 of the hub genes. Survival analysis of hub genes suggested that lower expression of MMP11, COL5A2, CDC25B, TOP2A, CENPF, CDCA3, TK1, TPX2, CDCA8, AEBP1, and FOXM1were associated with better overall survival of BC patients. BC samples with higher expression of hub genes were enriched in gene sets associated with P53 pathway, apical junction, mitotic spindle, G2M checkpoint, and myogenesis, etc.

          Conclusions: We identified several candidate biomarkers correlated with the TNM staging and overall survival of BC patients. Accordingly, they might be used as potential diagnostic biomarkers and therapeutic targets with clinical utility.

          Related collections

          Most cited references54

          • Record: found
          • Abstract: found
          • Article: not found

          Bladder cancer.

          Bladder cancer is a complex disease associated with high morbidity and mortality rates if not treated optimally. Awareness of haematuria as the major presenting symptom is paramount, and early diagnosis with individualised treatment and follow-up is the key to a successful outcome. For non-muscle-invasive bladder cancer, the mainstay of treatment is complete resection of the tumour followed by induction and maintenance immunotherapy with intravesical BCG vaccine or intravesical chemotherapy. For muscle-invasive bladder cancer, multimodal treatment involving radical cystectomy with neoadjuvant chemotherapy offers the best chance for cure. Selected patients with muscle-invasive tumours can be offered bladder-sparing trimodality treatment consisting of transurethral resection with chemoradiation. Advanced disease is best treated with systemic cisplatin-based chemotherapy; immunotherapy is emerging as a viable salvage treatment for patients in whom first-line chemotherapy cannot control the disease. Developments in the past 2 years have shed light on genetic subtypes of bladder cancer that might differ from one another in response to various treatments.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Understanding network concepts in modules

            Background Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory. Results Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks. Conclusion Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer

              Background While several molecular markers of bladder cancer prognosis have been identified, the limited value of current prognostic markers has created the need for new molecular indicators of bladder cancer outcomes. The aim of this study was to identify genetic signatures associated with disease prognosis in bladder cancer. Results We used 272 primary bladder cancer specimens for microarray analysis and real-time reverse transcriptase polymerase chain reaction (RT-PCR) analysis. Microarray gene expression analysis of randomly selected 165 primary bladder cancer specimens as an original cohort was carried out. Risk scores were applied to stratify prognosis-related gene classifiers. Prognosis-related gene classifiers were individually analyzed with tumor invasiveness (non-muscle invasive bladder cancer [NMIBC] and muscle invasive bladder cancer [MIBC]) and prognosis. We validated selected gene classifiers using RT-PCR in the original (165) and independent (107) cohorts. Ninety-seven genes related to disease progression among NMIBC patients were identified by microarray data analysis. Eight genes, a progression-related gene classifier in NMIBC, were selected for RT-PCR. The progression-related gene classifier in patients with NMIBC was closely correlated with progression in both original and independent cohorts. Furthermore, no patient with NMIBC in the good-prognosis signature group experienced cancer progression. Conclusions We identified progression-related gene classifier that has strong predictive value for determining disease outcome in NMIBC. This gene classifier could assist in selecting NMIBC patients who might benefit from more aggressive therapeutic intervention or surveillance.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                28 November 2017
                2017
                : 8
                : 947
                Affiliations
                [1] 1Department of Urology, Zhongnan Hospital of Wuhan University , Wuhan, China
                [2] 2Department of Biological Repositories, Zhongnan Hospital of Wuhan University , Wuhan, China
                [3] 3Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University , Wuhan, China
                Author notes

                Edited by: Oreste Gualillo, Servicio Gallego de Salud, Spain

                Reviewed by: Alex Zhavoronkov, Biogerontology Research Foundation, United Kingdom; Monica Catarina Botelho, Istituto Nacional de Saúde, Portugal

                *Correspondence: Xinghuan Wang wangxinghuan@ 123456whu.edu.cn

                This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology

                †These authors have contributed equally to this work.

                Article
                10.3389/fphys.2017.00947
                5712410
                29234286
                25d0f4f3-979f-4a16-8655-e14ca98d5ebf
                Copyright © 2017 Li, Liu, Liu, Meng, Yin, Fang, Huang, Cao, Weng, Zeng and Wang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 May 2017
                : 08 November 2017
                Page count
                Figures: 2, Tables: 3, Equations: 0, References: 68, Pages: 8, Words: 6250
                Categories
                Physiology
                Original Research

                Anatomy & Physiology
                bladder cancer,biomarkers,wgcna
                Anatomy & Physiology
                bladder cancer, biomarkers, wgcna

                Comments

                Comment on this article