Prognostic cancer gene signatures share common regulatory motifs

Wang, Ying; Goodison, Steve; Li, Xiaoman; Hu, Haiyan

doi:10.1038/s41598-017-05035-3

Cited by 29 publications

(23 citation statements)

References 60 publications

(68 reference statements)

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“…In contrast, significant overlap was observed among regulators (i.e., miRNAs and TFs) in the networks. The low conservation of target genes in the prognostic co-regulatory network was concordant with previous studies finding that prognostic genes themselves lack cross-cancer conservation [24,25], which also led to the lack of cross-cancer conservation of FFLs that comprise prognostic genes. However, when we focused on miRNAs and TFs that regulate target genes, some miRNAs and TFs played roles in multiple prognostic co-regulatory networks.…”

Section: The Landscape Of Prognostic Mirna-tf Co-regulatory Networksupporting

confidence: 88%

Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types

Jiang

et al. 2019

RNA Biology

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The study of cancer prognosis serves as an important part of cancer research. Large-scale cancer studies have identified numerous genes and microRNAs (miRNAs) associated with prognosis. These informative genes and miRNAs represent potential biomarkers to predict survival and to elucidate the molecular mechanism of tumour progression. MiRNAs and transcription factors (TFs) can work cooperatively as essential mediators of gene expression, and their dysregulation affects cancer prognosis. A panoramic view of cancer prognosis at the system level, considering the co-regulation roles of miRNA and TF, remains elusive. Here, we establish 12 prognosis-related miRNA-TF co-regulatory networks. The characteristics of prognostic target genes and their regulators in the network are depicted. Although the target genes and co-regulatory patterns exhibit cancer-specific properties, some miRNAs and TFs are highly conserved across cancers. We illustrate and interpret the roles of these conserved regulators by building a model associated with cancer hallmarks, functional enrichment analysis, network community detection, and exhaustive literature research. The elaborated system-level prognostic miRNA-TF coregulation landscape, including the highlighted roles of conserved regulators, provides a novel and powerful insights into further biological and medical discoveries.

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Section: The Landscape Of Prognostic Mirna-tf Co-regulatory Networksupporting

confidence: 88%

Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types

Jiang

et al. 2019

RNA Biology

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“…Eight of the 236 genes were previously known in 17 cancers [1] ( Table S1). Very few genes were found to be shared across cancer types, which were consistent with the literatures [1,13]. It is notable that the subunits of P-and V-ATPases (such as ATP13A3, ATP1A3, and ATP6V1C2) were in the list.…”

Section: Mapping and Characterization Of Prognostic Genessupporting

confidence: 86%

Prognostic gene expression signature revealed the involvement of mutational pathways in cancer genome

Liu¹,

Li²,

Luo³

et al. 2020

J. Cancer

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Background: Over the years, many efforts have been made to use the gene expression profiles of cancer types/subtypes to identify the prognostic genes with their potential clinical applications. However, one major challenge remains is to predict the common prognostic genes using simultaneously the dataset of multiple cancers, especially by considering the differences in survival, expression and the associated mutated pathways. Methods: Herein, we carried out a comprehensive examination for the prognostic genes and linked them to the mutational status of 29 cancers, so as to find independent prognostic genes and mechanisms. Additionally, their diagnostic value of them was also assessed. Results: our extensive analysis revealed: 1) the number of prognostic and diagnostic genes differs greatly across the cancers, 2) the potentially implicated 22 genes harbor the diagnostic as well as prognostic capacity, 3) the universal prognostic genes (CDC20, CDCA8, ASPM, ERCC6L, and GTSE1) were found to be involved in the spindle assembly checkpoint, 4) the prognostic genes were found to be statistically linked to the frequently mutated TP53-, MAPK-, PI3K-and AKT-related pathways. We also manually mined possible biological mechanisms for some of the statistical links in literatures. Conclusions: Taken together, we identified the prognostic genes and in addition we assessed their diagnostic capacity. Our analysis provides an important insight about the considerable overlapping between gene expression variation and the further associated altered mutational pathways across the cancer genome. We thus hypothesized that cancer related (mutated) genes are tightly connected and are capable to reshape the genome in multiple cancer types.

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“…Identification of gene signatures is crucial in cancer genomics. Prognostic gene signatures within a cancer type constitute a set of genes whose expression changes reveal important information about tumor diagnosis, prognosis and even therapeutic response [9, 12]. The dependence on the use of heatmaps to apply published gene signatures for tumor subtyping is increasing and along with it, the challenges in obtaining results.…”

Section: Discussionmentioning

confidence: 99%

NOJAH: Not Just Another Heatmap for Genome-Wide Cluster Analysis

Rupji

Dwivedi

Kowalski

2018

Preprint

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23Since their inception, several tools have been developed for cluster analysis and 24 heatmap construction. The application of such tools to the number and types of genome-25 wide data available from next generation sequencing (NGS) technologies requires the 26 adaptation of statistical concepts, such as in defining a most variable gene set, and more 27 intricate cluster analyses method to address multiple omic data types. Additionally, the 28 growing number of publicly available datasets has created the desire to estimate the 29 statistical significance of a gene signature derived from one dataset to similarly group 30 samples based on another dataset. The currently available number of tools and their 31 combined use for generating heatmaps, along with the several adaptations of statistical 32 concepts for addressing the higher dimensionality of genome-wide NGS-derived data, 33 has created a further challenge in the ability to replicate heatmap results. We introduce 34 NOJAH (NOt Just Another Heatmap), an interactive tool that defines and implements a 35 workflow for genome-wide cluster analysis and heatmap construction by creating and 36 combining several tools into a single user interface. NOJAH includes several newly 37 developed scripts for techniques that though frequently applied are not sufficiently 38 documented to allow for replicability of results. These techniques include: defining a most 39 variable gene set (a.k.a., 'core genes'), estimating the statistical significance of a gene 40 signature to separate samples into clusters, and performing a result merging integrated 41 cluster analysis. With only a user uploaded dataset, NOJAH provides as output, among 42 other things, the minimum documentation required for replicating heatmap results. 43 Additionally, NOJAH contains five different existing R packages that are connected in the 44 interface by their functionality as part of a defined workflow for genome-wide cluster 45 analysis. The NOJAH application tool is available at 46

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Prognostic cancer gene signatures share common regulatory motifs

Cited by 29 publications

References 60 publications

Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types

Exploration of prognosis-related microRNA and transcription factor co-regulatory networks across cancer types

Prognostic gene expression signature revealed the involvement of mutational pathways in cancer genome

NOJAH: Not Just Another Heatmap for Genome-Wide Cluster Analysis

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