ONGene: A literature-based database for human oncogenes

Liu, Yining; Sun, Jingjing; Zhao, Min

doi:10.1016/j.jgg.2016.12.004

Cited by 236 publications

(221 citation statements)

References 14 publications

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“…test p = 4.3 x 10 -8 ; Figure S2a and Supplementary File 2). These pathways encompass several known oncogenes (such as NRAS, CCND1, and ABL1) and tumour suppressor genes (such as SMAD4, CDKN1B and SMAD3) [33][34][35] (Figure 2C). Next, we used the list of significantly up-regulated proteins within each disease subtypes to assessed for the molecular processes that drive the disease subtypes.…”

Section: Altered Signalling Pathways and Molecular Processes Distingumentioning

confidence: 99%

Proteogenomic Analysis of Pancreatic Cancer Subtypes

Kafita

Nkhoma

Zulu

et al. 2020

Preprint

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Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. There is a need, therefore, to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset that includes proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes.We explore the clinical characteristic of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways are altered among pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes which can be leveraged to devise more effective treatments.

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Section: Altered Signalling Pathways and Molecular Processes Distingumentioning

confidence: 99%

Proteogenomic Analysis of Pancreatic Cancer Subtypes

Kafita

Nkhoma

Zulu

et al. 2020

Preprint

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“…Therefore, the CellAge senescence promoters and inhibitors of senescence were overlapped with oncogenes from the tumour suppressor gene (TSG) database (TSGene 2.0) (n=1,018) (Zhao et al, 2013) and the ONGene database (n=698) (Liu et al, 2017) (SI Table 21 and 22 respectively). The number of significant genes overlapping are shown in Figure 4A, while the significant p-values from the overlap analysis are shown in Figure 4B (p<0.05, Fisher's exact test with BH correction).…”

Section: Are Cs Genes Associated With Cancer Genes?mentioning

confidence: 99%

A Multidimensional Systems Biology Analysis of Cellular Senescence in Ageing and Disease

Avelar

Ortega

Tăcutu

et al. 2019

Preprint

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Cellular senescence, a permanent state of replicative arrest in otherwise proliferating cells, is a hallmark of ageing and has been linked to ageing-related diseases like cancer. Senescent cells have been shown to accumulate in tissues of aged organisms which in turn can lead to chronic inflammation. Many genes have been associated with cell senescence, yet a comprehensive understanding of cell senescence pathways is still lacking. To this end, we created CellAge (http://genomics.senescence.info/cells), a manually curated database of 279 human genes associated with cellular senescence, and performed various integrative and functional analyses. We observed that genes promoting cell senescence tend to be overexpressed with age in human tissues and are also significantly overrepresented in anti-longevity and tumour-suppressor gene databases.By contrast, genes inhibiting cell senescence overlapped with pro-longevity genes and oncogenes.Furthermore, an evolutionary analysis revealed a strong conservation of senescence-associated genes in mammals, but not in invertebrates. Using the CellAge genes as seed nodes, we also built protein-protein interaction and co-expression networks. Clusters in the networks were enriched for cell cycle and immunological processes. Network topological parameters also revealed novel potential senescence-associated regulators. We then used siRNAs and observed that of 26 candidates tested, 19 induced markers of senescence. Overall, our work provides a new resource for researchers to study cell senescence and our systems biology analyses provide new insights and novel genes regarding cell senescence.

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“…The signaling nodes particularly vulnerable to such transforming “hits” include receptor tyrosine kinases (EGFR, HER2, and PDGFR) at the plasma membrane, cytoplasmic signal transducing kinases (SRC family, RAF family, and ABL), and crucial regulatory GTPases (especially RAS family), transcription factors (MYC, CTNNB1) as well as various elements of the cell cycle control (CCND1), survival genes (BCL2 family) cell senescence apparatus (hTERT), and several others. In addition, proto‐oncogenes exist within the molecular machinery that controls chromatin architecture and cellular epigenome (H3F3A, SETD2, EZH2, and IDH1/2) . While oncogenes are associated with gain‐of‐function alterations, their effects are mirrored by, and often dependent upon, loss‐of‐function mutations in the corresponding negative regulators as exemplified by tumor suppressors such as TP53, APC, PTEN, VHL, RB1, or CDKN2A and several others.…”

Section: Introduction—genetics Of Cellular Interactions In Cancermentioning

confidence: 99%

Oncogenic Regulation of Extracellular Vesicle Proteome and Heterogeneity

et al. 2019

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Mutational and epigenetic driver events profoundly alter intercellular communication pathways in cancer. This effect includes deregulated release, molecular composition, and biological activity of extracellular vesicles (EVs), membranous cellular fragments ranging from a few microns to less than 100 nm in diameter and filled with bioactive molecular cargo (proteins, lipids, and nucleic acids). While EVs are usually classified on the basis of their physical properties and biogenetic mechanisms, recent analyses of their proteome suggest a larger than expected molecular diversity, a notion that is also supported by multicolour nano‐flow cytometry and other emerging technology platforms designed to analyze single EVs. Both protein composition and EV diversity are markedly altered by oncogenic transformation, epithelial to mesenchymal transition, and differentiation of cancer stem cells. Interestingly, only a subset of EVs released from mutant cells may carry oncogenic proteins (e.g., EGFRvIII), hence, these EVs are often referred to as “oncosomes”. Indeed, oncogenic transformation alters the repertoire of EV‐associated proteins, increases the presence of pro‐invasive cargo, and alters the composition of distinct EV populations. Molecular profiling of single EVs may reveal a more intricate effect of transforming events on the architecture of EV populations in cancer and shed new light on their biological role and diagnostic utility.

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ONGene: A literature-based database for human oncogenes

Cited by 236 publications

References 14 publications

Proteogenomic Analysis of Pancreatic Cancer Subtypes

Proteogenomic Analysis of Pancreatic Cancer Subtypes

A Multidimensional Systems Biology Analysis of Cellular Senescence in Ageing and Disease

Oncogenic Regulation of Extracellular Vesicle Proteome and Heterogeneity

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