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Chakraborty S, Ghosh Z. A systemic insight into astrocytoma biology across different grades. J Cell Physiol 2018; 234:4243-4255. [DOI: 10.1002/jcp.27193] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 07/17/2018] [Indexed: 01/05/2023]
Affiliation(s)
| | - Zhumur Ghosh
- Bioinformatics Centre, Bose Institute Kolkata India
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2
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Narsia N, Ramagiri P, Ehrmann J, Kolar Z. Transcriptome analysis reveals distinct gene expression profiles in astrocytoma grades II-IV. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 2017; 161:261-271. [PMID: 28452381 DOI: 10.5507/bp.2017.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 04/18/2017] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Astrocytoma is the most prevalent form of primary brain cancer categorized into four histological grades by the World Health Organization. Investigation into individual grades of astrocytoma by previous studies has provided some insight into dysregulation of regulatory networks associated with increasing astrocytoma grades. However, further understanding of key mechanisms that distinguish different astrocytoma grades is required to facilitate targeted therapies. METHODS In this study, we utilized a large cohort of publicly available RNA sequencing data from patients with diffuse astrocytoma (grade II), anaplastic astrocytoma (grade III), primary glioblastoma (grade IV), secondary glioblastoma (grade IV), recurrent glioblastoma (grade IV), and normal brain samples to identify genetic similarities and differences between these grades using bioinformatics applications. RESULTS Our analysis revealed a distinct gene expression pattern between grade II astrocytoma and grade IV glioblastoma (GBM). We also identified genes that were exclusively expressed in each of the astrocytoma grades. Furthermore, we identified known and novel genes involved in key pathways in our study. Gene set enrichment analysis revealed a distinct expression pattern of transcriptional regulators in primary GBM. Further investigation into molecular processes showed that the genes involved in cell proliferation and invasion were shared across all subtypes of astrocytoma. Also, the number of genes involved in metastasis, regulation of cell proliferation, and apoptosis increased with tumor grade. CONCLUSIONS We confirmed existing findings and shed light on some important genes and molecular processes that will improve our understanding of glioma biology.
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Affiliation(s)
- Nato Narsia
- Department of Clinical and Molecular Pathology and Laboratory of Molecular Pathology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Czech Republic
| | - Pradeep Ramagiri
- Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Jiri Ehrmann
- Department of Clinical and Molecular Pathology and Laboratory of Molecular Pathology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Czech Republic
| | - Zdenek Kolar
- Department of Clinical and Molecular Pathology and Laboratory of Molecular Pathology, Faculty of Medicine and Dentistry, Palacky University Olomouc and University Hospital Olomouc, Czech Republic
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Ghosh D, Funk CC, Caballero J, Shah N, Rouleau K, Earls JC, Soroceanu L, Foltz G, Cobbs CS, Price ND, Hood L. A Cell-Surface Membrane Protein Signature for Glioblastoma. Cell Syst 2017; 4:516-529.e7. [PMID: 28365151 DOI: 10.1016/j.cels.2017.03.004] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2015] [Revised: 09/08/2016] [Accepted: 03/03/2017] [Indexed: 02/08/2023]
Abstract
We present a systems strategy that facilitated the development of a molecular signature for glioblastoma (GBM), composed of 33 cell-surface transmembrane proteins. This molecular signature, GBMSig, was developed through the integration of cell-surface proteomics and transcriptomics from patient tumors in the REMBRANDT (n = 228) and TCGA datasets (n = 547) and can separate GBM patients from control individuals with a Matthew's correlation coefficient value of 0.87 in a lock-down test. Functionally, 17/33 GBMSig proteins are associated with transforming growth factor β signaling pathways, including CD47, SLC16A1, HMOX1, and MRC2. Knockdown of these genes impaired GBM invasion, reflecting their role in disease-perturbed changes in GBM. ELISA assays for a subset of GBMSig (CD44, VCAM1, HMOX1, and BIGH3) on 84 plasma specimens from multiple clinical sites revealed a high degree of separation of GBM patients from healthy control individuals (area under the curve is 0.98 in receiver operating characteristic). In addition, a classifier based on these four proteins differentiated the blood of pre- and post-tumor resections, demonstrating potential clinical value as biomarkers.
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Affiliation(s)
| | - Cory C Funk
- Institute for Systems Biology, Seattle, WA 98109, USA
| | | | - Nameeta Shah
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | | | - John C Earls
- Institute for Systems Biology, Seattle, WA 98109, USA; Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Liliana Soroceanu
- California Pacific Medical Center Research Institute, San Francisco, CA 94107, USA
| | - Greg Foltz
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Charles S Cobbs
- The Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment, Swedish Neuroscience Institute, Seattle, WA 98122, USA
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA 98109, USA; Department of Computer Science and Engineering, University of Washington, Seattle, WA 98195, USA
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA 98109, USA.
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Pollak J, Rai KG, Funk CC, Arora S, Lee E, Zhu J, Price ND, Paddison PJ, Ramirez JM, Rostomily RC. Ion channel expression patterns in glioblastoma stem cells with functional and therapeutic implications for malignancy. PLoS One 2017; 12:e0172884. [PMID: 28264064 PMCID: PMC5338779 DOI: 10.1371/journal.pone.0172884] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Accepted: 02/01/2017] [Indexed: 12/11/2022] Open
Abstract
Ion channels and transporters have increasingly recognized roles in cancer progression through the regulation of cell proliferation, migration, and death. Glioblastoma stem-like cells (GSCs) are a source of tumor formation and recurrence in glioblastoma multiforme, a highly aggressive brain cancer, suggesting that ion channel expression may be perturbed in this population. However, little is known about the expression and functional relevance of ion channels that may contribute to GSC malignancy. Using RNA sequencing, we assessed the enrichment of ion channels in GSC isolates and non-tumor neural cell types. We identified a unique set of GSC-enriched ion channels using differential expression analysis that is also associated with distinct gene mutation signatures. In support of potential clinical relevance, expression of selected GSC-enriched ion channels evaluated in human glioblastoma databases of The Cancer Genome Atlas and Ivy Glioblastoma Atlas Project correlated with patient survival times. Finally, genetic knockdown as well as pharmacological inhibition of individual or classes of GSC-enriched ion channels constrained growth of GSCs compared to normal neural stem cells. This first-in-kind global examination characterizes ion channels enriched in GSCs and explores their potential clinical relevance to glioblastoma molecular subtypes, gene mutations, survival outcomes, regional tumor expression, and experimental responses to loss-of-function. Together, the data support the potential biological and therapeutic impact of ion channels on GSC malignancy and provide strong rationale for further examination of their mechanistic and therapeutic importance.
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Affiliation(s)
- Julia Pollak
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington, United States of America
| | - Karan G. Rai
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington, United States of America
| | - Cory C. Funk
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Sonali Arora
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Eunjee Lee
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Hematology and Medical Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Patrick J. Paddison
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Jan-Marino Ramirez
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington, United States of America
- Department of Neurosurgery, University of Washington, Seattle, Washington, United States of America
| | - Robert C. Rostomily
- Department of Neurosurgery, University of Washington, Seattle, Washington, United States of America
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, Washington, United States of America
- Houston Methodist Research Institute, Houston, Texas, United States of America
- Department of Neurosurgery, Houston Methodist Hospital, Houston, Texas, United States of America
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Comparative transcriptomics reveals similarities and differences between astrocytoma grades. BMC Cancer 2015; 15:952. [PMID: 26673168 PMCID: PMC4682229 DOI: 10.1186/s12885-015-1939-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 11/01/2015] [Indexed: 11/23/2022] Open
Abstract
Background Astrocytomas are the most common primary brain tumors distinguished into four histological grades. Molecular analyses of individual astrocytoma grades have revealed detailed insights into genetic, transcriptomic and epigenetic alterations. This provides an excellent basis to identify similarities and differences between astrocytoma grades. Methods We utilized public omics data of all four astrocytoma grades focusing on pilocytic astrocytomas (PA I), diffuse astrocytomas (AS II), anaplastic astrocytomas (AS III) and glioblastomas (GBM IV) to identify similarities and differences using well-established bioinformatics and systems biology approaches. We further validated the expression and localization of Ang2 involved in angiogenesis using immunohistochemistry. Results Our analyses show similarities and differences between astrocytoma grades at the level of individual genes, signaling pathways and regulatory networks. We identified many differentially expressed genes that were either exclusively observed in a specific astrocytoma grade or commonly affected in specific subsets of astrocytoma grades in comparison to normal brain. Further, the number of differentially expressed genes generally increased with the astrocytoma grade with one major exception. The cytokine receptor pathway showed nearly the same number of differentially expressed genes in PA I and GBM IV and was further characterized by a significant overlap of commonly altered genes and an exclusive enrichment of overexpressed cancer genes in GBM IV. Additional analyses revealed a strong exclusive overexpression of CX3CL1 (fractalkine) and its receptor CX3CR1 in PA I possibly contributing to the absence of invasive growth. We further found that PA I was significantly associated with the mesenchymal subtype typically observed for very aggressive GBM IV. Expression of endothelial and mesenchymal markers (ANGPT2, CHI3L1) indicated a stronger contribution of the micro-environment to the manifestation of the mesenchymal subtype than the tumor biology itself. We further inferred a transcriptional regulatory network associated with specific expression differences distinguishing PA I from AS II, AS III and GBM IV. Major central transcriptional regulators were involved in brain development, cell cycle control, proliferation, apoptosis, chromatin remodeling or DNA methylation. Many of these regulators showed directly underlying DNA methylation changes in PA I or gene copy number mutations in AS II, AS III and GBM IV. Conclusions This computational study characterizes similarities and differences between all four astrocytoma grades confirming known and revealing novel insights into astrocytoma biology. Our findings represent a valuable resource for future computational and experimental studies. Electronic supplementary material The online version of this article (doi:10.1186/s12885-015-1939-9) contains supplementary material, which is available to authorized users.
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Obulkasim A, Fornerod M, Zwaan MC, Reinhardt D, van den Heuvel-Eibrink MM. Subtype prediction in pediatric acute myeloid leukemia: classification using differential network rank conservation revisited. BMC Bioinformatics 2015; 16:305. [PMID: 26399969 PMCID: PMC4580220 DOI: 10.1186/s12859-015-0737-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2014] [Accepted: 09/11/2015] [Indexed: 11/10/2022] Open
Abstract
Background One of the most important application spectrums of transcriptomic data is cancer phenotype classification. Many characteristics of transcriptomic data, such as redundant features and technical artifacts, make over-fitting commonplace. Promising classification results often fail to generalize across datasets with different sources, platforms, or preprocessing. Recently a novel differential network rank conservation (DIRAC) algorithm to characterize cancer phenotypes using transcriptomic data. DIRAC is a member of a family of algorithms that have shown useful for disease classification based on the relative expression of genes. Combining the robustness of this family’s simple decision rules with known biological relationships, this systems approach identifies interpretable, yet highly discriminate networks. While DIRAC has been briefly employed for several classification problems in the original paper, the potentials of DIRAC in cancer phenotype classification, and especially robustness against artifacts in transcriptomic data have not been fully characterized yet. Results In this study we thoroughly investigate the potentials of DIRAC by applying it to multiple datasets, and examine the variations in classification performances when datasets are (i) treated and untreated for batch effect; (ii) preprocessed with different techniques. We also propose the first DIRAC-based classifier to integrate multiple networks. We show that the DIRAC-based classifier is very robust in the examined scenarios. To our surprise, the trained DIRAC-based classifier even translated well to a dataset with different biological characteristics in the presence of substantial batch effects that, as shown here, plagued the standard expression value based classifier. In addition, the DIRAC-based classifier, because of the integrated biological information, also suggests pathways to target in specific subtypes, which may enhance the establishment of personalized therapy in diseases such as pediatric AML. In order to better comprehend the prediction power of the DIRAC-based classifier in general, we also performed classifications using publicly available datasets from breast and lung cancer. Furthermore, multiple well-known classification algorithms were utilized to create an ideal test bed for comparing the DIRAC-based classifier with the standard gene expression value based classifier. We observed that the DIRAC-based classifier greatly outperforms its rival. Conclusions Based on our experiments with multiple datasets, we propose that DIRAC is a promising solution to the lack of generalizability in classification efforts that uses transcriptomic data. We believe that superior performances presented in this study may motivate other to initiate a new aline of research to explore the untapped power of DIRAC in a broad range of cancer types. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0737-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Askar Obulkasim
- Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands.
| | - Maarten Fornerod
- Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands.
| | - Michel C Zwaan
- Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands.,Dutch Children's Oncology Group, Erasmus-MC Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Dirk Reinhardt
- AML-BFM Study Group, Pediatric Hematology/Oncology, Essen, Germany
| | - Marry M van den Heuvel-Eibrink
- Department of Pediatric Oncology/Hematology, Erasmus-MC Sophia Childrens Hospital, Rotterdam, The Netherlands.,Dutch Children's Oncology Group, Erasmus-MC Sophia Children's Hospital, Rotterdam, The Netherlands.,Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
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A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories. Comput Struct Biotechnol J 2014; 11:91-9. [PMID: 25379147 PMCID: PMC4212277 DOI: 10.1016/j.csbj.2014.08.010] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well.
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