A flowgraph model for bladder carcinoma

Rubio, Gregorio; García-Mora, Belén; Santamaría, C.; Pontones, J.L.

doi:10.1186/1742-4682-11-s1-s3

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…Human bladder carcinoma is a considerable global health concern, [43] although the molecular mechanisms that contribute to BCa progression remain unclear. This presents an obstacle to the development of new tools for diagnosis, prognosis, and identifying drug targets.…”

Section: Discussionmentioning

confidence: 99%

Integrated analysis of a competing endogenous RNA network reveals key lncRNAs as potential prognostic biomarkers for human bladder cancer

Zhu

Hou²,

et al. 2018

Medicine

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Human bladder cancer (BCa) is one of the most commonly diagnosed malignancies worldwide. It has high recurrence rates and low-grade malignancy, thus representing an important public health concern. An increasing number of studies suggest that long-noncoding RNAs (lncRNAs) play important roles in various biological processes and disease pathologies, including cancer.We analyzed the expression profiles of lncRNA, miRNA, and mRNA, along with the clinical information of BCa patients collected from the Cancer Genome Atlas database to identify lncRNA biomarkers for prognosis. We also constructed an lncRNA-miRNA-mRNA global triple network (competitive endogenous RNA network) by bioinformational approach.This BCa lncRNA-miRNA-mRNA network consisted of 23 miRNA nodes, 52 mRNA nodes, 59 lncRNA nodes, and 365 edges. Subsequent gene ontology (GO) and pathway analyses were performed using BinGO for Cytoscape and Database for Annotation, Visualization, and Integration Discovery, respectively, highlighting important GO terms and pathways that were enriched in the network. Subnetworks were created using 3 key lncRNAs (MAGI2-AS3, ADAMTS9-AS2, and LINC00330), revealing associations with BCa-linked mRNAs and miRNAs. Finally, an analysis of significantly differentiating RNAs found 6 DElncRNAs (AC112721.1, ADAMTS9-AS1, ADAMTS9-AS2, HCG22, MYO16-AS1, and SACS-AS1), 1 DEmiRNA (miRNA-195), and 6 DEmRNAs (CCNB1, FAM129A, MAP1B, TMEM100, AIFM3, and HOXB5) that correlated with BCa patient survival.Our results provide a novel perspective from which to study the lncRNA-related ceRNA network in BCa, contributing to the development of future diagnostic biomarkers and therapeutic targets.

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Section: Discussionmentioning

confidence: 99%

Integrated analysis of a competing endogenous RNA network reveals key lncRNAs as potential prognostic biomarkers for human bladder cancer

Zhu

Hou²,

et al. 2018

Medicine

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“…BC is an important public health issue as it is biologically very aggressive and highly prevalent in Western countries (1). In 2017, an estimated 79,030 new cases of BC and 16,870 mortalities will occur in the USA (2).…”

Section: Introductionmentioning

confidence: 99%

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

et al. 2017

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Abstract. The aim of the present study was to identify hub genes and signaling pathways associated with bladder cancer (BC) utilizing centrality analysis and pathway enrichment analysis. The differentially expressed genes (DEGs) were screened from the ArrayExpress database between normal subjects and BC patients. Co-expression networks of BC were constructed using differentially co-expressed genes and links, and hub genes were investigated by degree centrality analysis of co-expression networks in BC. The enriched signaling pathways were investigated by Kyoto Encyclopedia of Genes and Genomes database analysis based on the DEGs. The hub gene expression in BC tissues was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. A total of 329 DEGs were screened, including 147 upregulated and 182 downregulated genes. The co-expression network constructed between BC and normal controls consisted of 182 nodes and 434 edges, and the two genes in each gene pair were differentially co-expressed genes. Centrality analysis of co-expression networks suggested that the top 5 hub genes with high degree included lectin, galactoside-binding, soluble, 4 (LGALS4), protein tyrosine phosphatase, receptor type N2 (PTPRN2), transmembrane protease, serine 11E (TMPRSS11E), tripartite motif containing 31 (TRIM31) and potassium voltage-gated channel subfamily D member 3 (KCND3). Pathway analysisrevealed that the 329 DEGs were significantly enriched in 5 terms (cell cycle, DNA replication, oocyte meiosis, p53 signaling pathway and peroxisome proliferator-activated receptor signaling pathway). According to RT-qPCR and western blot analysis, 4/5 hub genes were significantly expressed, including LGALS4, PTPRN2, TMPRSS11E, TRIM31; however, KCND3 was not significantly expressed. In the present study, 5 hub genes were successfully identified (LGALS4, PTPRN2, TMPRSS11E, TRIM31 and KCND3) and 5 biological pathways that may be underlying biomarkers for early diagnosis and treatment associated with bladder cancer were revealed.

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“…Distributions of waiting times in each transition are modelled by means of a PHD made of a mixture of Erlang distributions [13]. The procedure is described in [14] and [15]. Then we make up the convolution of PHDs corresponding to the transitions 0 → 1 and 1 → 2, using Theorem 1.…”

Section: The First Approachmentioning

confidence: 99%

“…On the other hand the system of equations (14) have not a feasible solution and, in order to obtain the estimation, we use the proposal given in [17] which consists of solving the following the optimization problem (P) instead solving the system (14).…”

Section: Estimation Of the Parameters In The M Apmentioning

confidence: 99%

Modeling dependence in the inter-failure times. An analysis in Reliability models by Markovian Arrival Processes

García-Mora

Santamaría

Rubio

2018

Journal of Computational and Applied Mathematics

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The most common assumptions in reliability studies are that failures occur independently and with the same distribution. However, these two assumptions are unrealistic in practice since inter-failure times are usually correlated and not identically distributed. In this sense the Markov Arrival Process (MAP) is an active research field for managing these features. We study two versions of the MAP approach. The first one is a model which we developed in previous works. The second one is based on a non-stationary MAP of second order. We compare the results of both models with simulated data.

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A flowgraph model for bladder carcinoma

Cited by 8 publications

References 13 publications

Integrated analysis of a competing endogenous RNA network reveals key lncRNAs as potential prognostic biomarkers for human bladder cancer

Integrated analysis of a competing endogenous RNA network reveals key lncRNAs as potential prognostic biomarkers for human bladder cancer

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

Modeling dependence in the inter-failure times. An analysis in Reliability models by Markovian Arrival Processes

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