Brain cancer prognosis: independent validation of a clinical bioinformatics approach

Fronza, Raffaele; Tramonti, Michele; Atchley, William R.; Nardini, Christine

doi:10.1186/2043-9113-2-2

Cited by 3 publications

(3 citation statements)

References 24 publications

(44 reference statements)

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“…Recent approaches have focused on examining the latent or underlying biological pathways in data repositories from glioma studies and the correlations or relationships between them. For example, Fronza et al 57 examined the interactions between groups of mRNAs and miRNAs in four glioma data sets and identified miRNA clusters, which they validated as being associated with survival in GBM. Wuchty et al 58 also examined miRNAs and mRNAs in order to discover significant miRNA-mRNA interactions in GBMs.…”

Section: Epigenetic Systems Biology and Regulatory Networkmentioning

confidence: 99%

Epigenetic pathways and glioblastoma treatment

et al. 2013

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Glioblastoma multiforme (GBM) is the most common malignant adult brain tumor. Standard GBM treatment includes maximal safe surgical resection with combination radiotherapy and adjuvant temozolomide (TMZ) chemotherapy. Alarmingly, patient survival at five-years is below 10%. This is in part due to the invasive behavior of the tumor and the resulting inability to resect greater than 98% of some tumors. In fact, recurrence after such treatment may be inevitable, even in cases where gross total resection is achieved. The Cancer Genome Atlas (TCGA) research network performed whole genome sequencing of GBM tumors and found that GBM recurrence is linked to epigenetic mechanisms and pathways. Central to these pathways are epigenetic enzymes, which have recently emerged as possible new drug targets for multiple cancers, including GBM. Here we review GBM treatment, and provide a systems approach to identifying epigenetic drivers of GBM tumor progression based on temporal modeling of putative GBM cells of origin. We also discuss advances in defining epigenetic mechanisms controlling GBM initiation and recurrence and the drug discovery considerations associated with targeting epigenetic enzymes for GBM treatment.

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Section: Epigenetic Systems Biology and Regulatory Networkmentioning

confidence: 99%

Epigenetic pathways and glioblastoma treatment

et al. 2013

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“…The use of multivariate statistics (such as factor analysis) to analyze jointly different types of omics data, for example mRNA and miRNA, was shown to successfully identify features that could not be found by the differential analysis of the mRNA and miRNA data sets separately [81,82].…”

Section: A Case Study: Application To Immune Diseasesmentioning

confidence: 99%

Understanding human diseases with high-throughput quantitative measurement and analysis of molecular signatures

Yang

Wei

Tang

et al. 2013

Sci. China Life Sci.

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Microarray and deep sequencing technologies have provided unprecedented opportunities for mapping genome mutations, RNA transcripts, transcription factor binding, and histone modifications at high resolution at the genome-wide level. This has revolutionized the way in which transcriptomes, regulatory networks and epigenetic regulations have been studied and large amounts of heterogeneous data have been generated. Although efforts are being made to integrate these datasets unbiasedly and efficiently, how best to do this still remains a challenge. Here we review major impacts of high-throughput genome-wide data generation, their relevance to human diseases, and various bioinformatics approaches for data integration. Finally, we provide a case study on inflammatory diseases. genomics, epigenomics, phenomics, integration, data analysis Citation:Yang L, Wei G, Tang K, et al. Understanding human diseases with high-throughput quantitative measurement and analysis of molecular signatures. Sci China Life Sci, 2013Sci, , 56: 213 -219, doi: 10.1007 Twelve years ago, the unveiling of the first human reference genome sequence [1,2] inspired researchers to believe that genome-based discoveries would revolutionize the study and clinical treatment of human diseases. As genome sequences from different individuals became available, comparative genomics using computational approaches emerged as a powerful method for understanding gene functions at the genome-wide level. These approaches unveiled more variations between individuals than were initially expected [3]. Genomic variations (including single nucleotide polymorphism (SNPs) and insertions and deletions (indels)) responsible for some of hereditary diseases have been identified and applied to examine genomes of thousands of individuals for correlations between the presence of variants and traits of interests [4]. First microarrays were used, then exon sequencing, and now whole genome sequencing has become a popular tool [5,6]. Currently, many variations from numerous sites in the genome have been successfully connected with different human diseases including various types of cancers [6] using DNA sequencing technology which underwent a 14000-fold drop in cost between 1999 and 2009 [7], and computational imputation methods [8].Though most studies have focused on the connection between genomic variations (both common and rare) and human diseases, mechanisms underlying many of the DNA variations have not been clearly addressed. Genome information alone is not sufficient to interpret complex diseases [9]. Evidence at epigenome, post-transcriptome, and even the human microbiome levels is beginning to shed new light on human disease-related studies beyond the genome level.

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“…这些结果更好地描绘了 RA 在欧洲和亚洲人群中的分布, 同时也表研究分析元基因组数据时发现 [80] , 相对于单组学数据分析的结果, 多组学数据整合分析可挖掘更多的信息. 应用多元统计方法(如因子分析)对来自不同组学的数据进行整合分析, 如 mRNA 和 miRNA 数据, 已鉴定出一些采用单种组学数据分析时无法发现的特征 [81,82] .…”

Section: 高通量异质数据的整合分析unclassified