1
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Zhong F, Yao F, Xu S, Zhang J, Liu J, Wang X. Identification and validation of hub genes and molecular classifications associated with chronic myeloid leukemia. Front Immunol 2024; 14:1297886. [PMID: 38283355 PMCID: PMC10811081 DOI: 10.3389/fimmu.2023.1297886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/28/2023] [Indexed: 01/30/2024] Open
Abstract
Background Chronic myeloid leukemia (CML) is a kind of malignant blood tumor, which is prone to drug resistance and relapse. This study aimed to identify novel diagnostic and therapeutic targets for CML. Methods Differentially expressed genes (DEGs) were obtained by differential analysis of the CML cohort in the GEO database. Weighted gene co-expression network analysis (WGCNA) was used to identify CML-related co-expressed genes. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen hub genes and construct a risk score model based on hub genes. Consensus clustering algorithm was used for the identification of molecular subtypes. Clinical samples and in vitro experiments were used to verify the expression and biological function of hub genes. Results A total of 378 DEGs were identified by differential analysis. 369 CML-related genes were identified by WGCNA analysis, which were mainly enriched in metabolism-related signaling pathways. In addition, CML-related genes are mainly involved in immune regulation and anti-tumor immunity, suggesting that CML has some immunodeficiency. Immune infiltration analysis confirmed the reduced infiltration of immune killer cells such as CD8+ T cells in CML samples. 6 hub genes (LINC01268, NME8, DMXL2, CXXC5, SCD and FBN1) were identified by LASSO regression analysis. The receiver operating characteristic (ROC) curve confirmed the high diagnostic value of the hub genes in the analysis and validation cohorts, and the risk score model further improved the diagnostic accuracy. hub genes were also associated with cell proliferation, cycle, and metabolic pathway activity. Two molecular subtypes, Cluster A and Cluster B, were identified based on hub gene expression. Cluster B has a lower risk score, higher levels of CD8+ T cell and activated dendritic cell infiltration, and immune checkpoint expression, and is more sensitive to commonly used tyrosine kinase inhibitors. Finally, our clinical samples validated the expression and diagnostic efficacy of hub genes, and the knockdown of LINC01268 inhibited the proliferation of CML cells, and promoted apoptosis. Conclusion Through WGCNA analysis and LASSO regression analysis, our study provides a new target for CML diagnosis and treatment, and provides a basis for further CML research.
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Affiliation(s)
| | | | | | | | - Jing Liu
- Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
| | - Xiaozhong Wang
- Jiangxi Province Key Laboratory of Laboratory Medicine, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China
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2
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Akuwudike P, López-Riego M, Marczyk M, Kocibalova Z, Brückner F, Polańska J, Wojcik A, Lundholm L. Short- and long-term effects of radiation exposure at low dose and low dose rate in normal human VH10 fibroblasts. Front Public Health 2023; 11:1297942. [PMID: 38162630 PMCID: PMC10755029 DOI: 10.3389/fpubh.2023.1297942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 11/20/2023] [Indexed: 01/03/2024] Open
Abstract
Introduction Experimental studies complement epidemiological data on the biological effects of low doses and dose rates of ionizing radiation and help in determining the dose and dose rate effectiveness factor. Methods Human VH10 skin fibroblasts exposed to 25, 50, and 100 mGy of 137Cs gamma radiation at 1.6, 8, 12 mGy/h, and at a high dose rate of 23.4 Gy/h, were analyzed for radiation-induced short- and long-term effects. Two sample cohorts, i.e., discovery (n = 30) and validation (n = 12), were subjected to RNA sequencing. The pool of the results from those six experiments with shared conditions (1.6 mGy/h; 24 h), together with an earlier time point (0 h), constituted a third cohort (n = 12). Results The 100 mGy-exposed cells at all abovementioned dose rates, harvested at 0/24 h and 21 days after exposure, showed no strong gene expression changes. DMXL2, involved in the regulation of the NOTCH signaling pathway, presented a consistent upregulation among both the discovery and validation cohorts, and was validated by qPCR. Gene set enrichment analysis revealed that the NOTCH pathway was upregulated in the pooled cohort (p = 0.76, normalized enrichment score (NES) = 0.86). Apart from upregulated apical junction and downregulated DNA repair, few pathways were consistently changed across exposed cohorts. Concurringly, cell viability assays, performed 1, 3, and 6 days post irradiation, and colony forming assay, seeded just after exposure, did not reveal any statistically significant early effects on cell growth or survival patterns. Tendencies of increased viability (day 6) and reduced colony size (day 21) were observed at 12 mGy/h and 23.4 Gy/min. Furthermore, no long-term changes were observed in cell growth curves generated up to 70 days after exposure. Discussion In conclusion, low doses of gamma radiation given at low dose rates had no strong cytotoxic effects on radioresistant VH10 cells.
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Affiliation(s)
- Pamela Akuwudike
- Centre for Radiation Protection Research, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Milagrosa López-Riego
- Centre for Radiation Protection Research, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Michal Marczyk
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
- Yale Cancer Center, Yale School of Medicine, New Haven, CT, United States
| | - Zuzana Kocibalova
- Centre for Radiation Protection Research, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Fabian Brückner
- Centre for Radiation Protection Research, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
| | - Joanna Polańska
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Andrzej Wojcik
- Centre for Radiation Protection Research, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
- Institute of Biology, Jan Kochanowski University, Kielce, Poland
| | - Lovisa Lundholm
- Centre for Radiation Protection Research, Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden
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3
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Wu S, Zhang H, Fouladdel S, Li H, Keller E, Wicha MS, Omenn GS, Azizi E, Guan Y. Cellular, transcriptomic and isoform heterogeneity of breast cancer cell line revealed by full-length single-cell RNA sequencing. Comput Struct Biotechnol J 2020; 18:676-685. [PMID: 32257051 PMCID: PMC7114460 DOI: 10.1016/j.csbj.2020.03.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Revised: 01/28/2020] [Accepted: 03/11/2020] [Indexed: 12/13/2022] Open
Abstract
Tumor heterogeneity is generated through a combination of genetic and epigenetic mechanisms, the latter of which plays an important role in the generation of stem like cells responsible for tumor formation and metastasis. Although the development of single cell transcriptomic technologies holds promise to deconvolute this complexity, a number of these techniques have limitations including drop-out and uneven coverage, which challenge the further delineation of tumor heterogeneity. We adopted deep and full-length single-cell RNA sequencing on Fluidigm's Polaris platform to reveal the cellular, transcriptomic, and isoform heterogeneity of SUM149, a triple negative breast cancer (TNBC) cell line. We first validate the quality of the TNBC sequencing data with the sequencing data from erythroleukemia K562 cell line as control. We next scrutinized well-defined marker genes for cancer stem-like cell to identify different cell populations. We then profile the isoform expression data to investigate the heterogeneity of alternative splicing patterns. Though classified as triple-negative breast cancer, the SUM149 stem cells show heterogeneous expression of marker receptors (ER, PR, and HER2) across the cells. We identified three cell populations that express patterns of stemness: epithelial-mesenchymal transition (EMT) cancer stem cells (CSCs), mesenchymal-epithelial transition (MET) CSCs and Dual-EMT-MET CSCs. These cells also manifested a high level of heterogeneity in alternative splicing patterns. For example, CSCs have shown different expression patterns of the CD44v6 exon, as well as different levels of truncated EGFR transcripts, which may suggest different potentials for proliferation and invasion among cancer stem cells. Our study identified features of the landscape of previously underestimated cellular, transcriptomic, and isoform heterogeneity of cancer stem cells in triple-negative breast cancers.
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Affiliation(s)
- Shaocheng Wu
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor 48109, MI, United States
- Bioinformatics Graduate Program, University of British Columbia, 570 West 7th Avenue, V5Z 4S6 Vancouver, BC, Canada
- Department of Molecular Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Hongjiu Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor 48109, MI, United States
- Microsoft, Inc., Bellevue, WA, United States
| | - Shamileh Fouladdel
- Comprehensive Cancer Center, University of Michigan, Ann Arbor 48109, MI, United States
| | - Hongyang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor 48109, MI, United States
| | - Evan Keller
- Comprehensive Cancer Center, University of Michigan, Ann Arbor 48109, MI, United States
- Department of Urology, Biointerfaces Institute and Single Cell Spatial Analysis Program, University of Michigan, Ann Arbor 48109, MI, United States
| | - Max S. Wicha
- Comprehensive Cancer Center, University of Michigan, Ann Arbor 48109, MI, United States
| | - Gilbert S. Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor 48109, MI, United States
| | - Ebrahim Azizi
- Comprehensive Cancer Center, University of Michigan, Ann Arbor 48109, MI, United States
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor 48109, MI, United States
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4
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Wang Y, Wang J, Li R, Shi Q, Xue Z, Zhang Y. ThreaDomEx: a unified platform for predicting continuous and discontinuous protein domains by multiple-threading and segment assembly. Nucleic Acids Res 2019; 45:W400-W407. [PMID: 28498994 PMCID: PMC5793814 DOI: 10.1093/nar/gkx410] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Accepted: 04/28/2017] [Indexed: 12/21/2022] Open
Abstract
We develop a hierarchical pipeline, ThreaDomEx, for both continuous domain (CD) and discontinuous domain (DCD) structure predictions. Starting from a query sequence, ThreaDomEx first threads it through the PDB to identify multiple structure templates, where a profile of domain conservation score (DC-score) is derived for domain-segment assignment. To further detect DCDs that consist of separated segments along the sequence, a boundary-clustering algorithm is used to refine the DCD-linker locations. In case that the templates do not contain DCDs, a domain-segment assembly process, guided by symmetry comparison, is applied for further DCD detections. ThreaDomEx was tested a set of 1111 proteins and achieved a normalized domain overlap score of 89.3% compared to experimental data, which is significantly higher than other state-of-the-art methods. It also recalls 26.7% of DCDs with 72.7% precision on the proteins for which threading failed to detect any DCDs. The server provides facilities for users to interactively refine the domain models by adjusting DC-score threshold, deleting and adding domain linkers, and assembling domain segments, which are particularly helpful for the hard targets for which current methods have a low accuracy while human-expert knowledge and experimental insights can be used for refining models. ThreaDomEX server is available at http://zhanglab.ccmb.med.umich.edu/ThreaDomEx.
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Affiliation(s)
- Yan Wang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jian Wang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Ruiming Li
- School of Software, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Qiang Shi
- School of Software, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Zhidong Xue
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.,School of Software, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
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5
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Zhang C, Wei X, Omenn GS, Zhang Y. Structure and Protein Interaction-Based Gene Ontology Annotations Reveal Likely Functions of Uncharacterized Proteins on Human Chromosome 17. J Proteome Res 2018; 17:4186-4196. [PMID: 30265558 PMCID: PMC6438760 DOI: 10.1021/acs.jproteome.8b00453] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Understanding the function of human proteins is essential to decipher the molecular mechanisms of human diseases and phenotypes. Of the 17 470 human protein coding genes in the neXtProt 2018-01-17 database with unequivocal protein existence evidence (PE1), 1260 proteins do not have characterized functions. To reveal the function of poorly annotated human proteins, we developed a hybrid pipeline that creates protein structure prediction using I-TASSER and infers functional insights for the target protein from the functional templates recognized by COFACTOR. As a case study, the pipeline was applied to all 66 PE1 proteins with unknown or insufficiently specific function (uPE1) on human chromosome 17 as of neXtProt 2017-07-01. Benchmark testing on a control set of 100 well-characterized proteins randomly selected from the same chromosome shows high Gene Ontology (GO) term prediction accuracies of 0.69, 0.57, and 0.67 for molecular function (MF), biological process (BP), and cellular component (CC), respectively. Three pipelines of function annotations (homology detection, protein-protein interaction network inference, and structure template identification) have been exploited by COFACTOR. Detailed analyses show that structure template detection based on low-resolution protein structure prediction made the major contribution to the enhancement of the sensitivity and precision of the annotation predictions, especially for cases that do not have sequence-level homologous templates. For the chromosome 17 uPE1 proteins, the I-TASSER/COFACTOR pipeline confidently assigned MF, BP, and CC for 13, 33, and 49 proteins, respectively, with predicted functions ranging from sphingosine N-acyltransferase activity and sugar transmembrane transporter to cytoskeleton constitution. We highlight the 13 proteins with confident MF predictions; 11 of these are among the 33 proteins with confident BP predictions and 12 are among the 49 proteins with confident CC. This study demonstrates a novel computational approach to systematically annotate protein function in the human proteome and provides useful insights to guide experimental design and follow-up validation studies of these uncharacterized proteins.
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Affiliation(s)
- Chengxin Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
| | - Xiaoqiong Wei
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, People’s Republic of China
| | - Gilbert S. Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
- Departments of Internal Medicine and Human Genetics and School of Public Health, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
| | - Yang Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
- Department of Biological Chemistry, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
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6
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Alternative Splicing as a Target for Cancer Treatment. Int J Mol Sci 2018; 19:ijms19020545. [PMID: 29439487 PMCID: PMC5855767 DOI: 10.3390/ijms19020545] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2017] [Revised: 01/29/2018] [Accepted: 01/29/2018] [Indexed: 02/06/2023] Open
Abstract
Alternative splicing is a key mechanism determinant for gene expression in metazoan. During alternative splicing, non-coding sequences are removed to generate different mature messenger RNAs due to a combination of sequence elements and cellular factors that contribute to splicing regulation. A different combination of splicing sites, exonic or intronic sequences, mutually exclusive exons or retained introns could be selected during alternative splicing to generate different mature mRNAs that could in turn produce distinct protein products. Alternative splicing is the main source of protein diversity responsible for 90% of human gene expression, and it has recently become a hallmark for cancer with a full potential as a prognostic and therapeutic tool. Currently, more than 15,000 alternative splicing events have been associated to different aspects of cancer biology, including cell proliferation and invasion, apoptosis resistance and susceptibility to different chemotherapeutic drugs. Here, we present well established and newly discovered splicing events that occur in different cancer-related genes, their modification by several approaches and the current status of key tools developed to target alternative splicing with diagnostic and therapeutic purposes.
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7
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Paik YK, Omenn GS, Hancock WS, Lane L, Overall CM. Advances in the Chromosome-Centric Human Proteome Project: looking to the future. Expert Rev Proteomics 2017; 14:1059-1071. [PMID: 29039980 DOI: 10.1080/14789450.2017.1394189] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
INTRODUCTION The mission of the Chromosome-Centric Human Proteome Project (C-HPP), is to map and annotate the entire predicted human protein set (~20,000 proteins) encoded by each chromosome. The initial steps of the project are focused on 'missing proteins (MPs)', which lacked documented evidence for existence at protein level. In addition to remaining 2,579 MPs, we also target those annotated proteins having unknown functions, uPE1 proteins, alternative splice isoforms and post-translational modifications. We also consider how to investigate various protein functions involved in cis-regulatory phenomena, amplicons lncRNAs and smORFs. Areas covered: We will cover the scope, historic background, progress, challenges and future prospects of C-HPP. This review also addresses the question of how we can best improve the methodological approaches, select the optimal biological samples, and recommend stringent protocols for the identification and characterization of MPs. A new strategy for functional analysis of some of those annotated proteins having unknown function will also be discussed. Expert commentary: If the project moves well by reshaping the original goals, the current working modules and team work in the proposed extended planning period, it is anticipated that a progressively more detailed draft of an accurate chromosome-based proteome map will become available with functional information.
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Affiliation(s)
- Young-Ki Paik
- a Yonsei Proteome Research Center and Department of Biochemistry , Yonsei University , Seoul , Korea
| | - Gilbert S Omenn
- b Department of Computational Medicine & Bioinformatics , University of Michigan , Ann Arbor , MI , USA
| | - William S Hancock
- c Department of Chemical Biology , Northeastern University , Boston , Massachusetts 02115 , USA
| | - Lydie Lane
- d Department of Human Protein Sciences, Faculty of Medicine , University of Geneva , Geneva , Switzerland.,e Swiss Institute of Bioinformatics , Geneva , Switzerland
| | - Christopher M Overall
- f Centre for Blood Research, Departments of Oral Biological & Medical Sciences, and Biochemistry & Molecular Biology, Faculty of Dentistry , University of British Columbia , Vancouver , Canada
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8
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Staunton L, Tonry C, Lis R, Espina V, Liotta L, Inzitari R, Bowden M, Fabre A, O'Leary J, Finn SP, Loda M, Pennington SR. Pathology-Driven Comprehensive Proteomic Profiling of the Prostate Cancer Tumor Microenvironment. Mol Cancer Res 2017; 15:281-293. [PMID: 28057717 DOI: 10.1158/1541-7786.mcr-16-0358] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 12/11/2016] [Accepted: 12/13/2016] [Indexed: 11/16/2022]
Abstract
Prostate cancer is the second most common cancer in men worldwide. Gleason grading is an important predictor of prostate cancer outcomes and is influential in determining patient treatment options. Clinical decisions based on a Gleason score of 7 are difficult as the prognosis for individuals diagnosed with Gleason 4+3 cancer is much worse than for those diagnosed with Gleason 3+4 cancer. Laser capture microdissection (LCM) is a highly precise method to isolate specific cell populations or discrete microregions from tissues. This report undertook a detailed molecular characterization of the tumor microenvironment in prostate cancer to define the proteome in the epithelial and stromal regions from tumor foci of Gleason grades 3 and 4. Tissue regions of interest were isolated from several Gleason 3+3 and Gleason 4+4 tumors using telepathology to leverage specialized pathology expertise to support LCM. Over 2,000 proteins were identified following liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of all regions of interest. Statistical analysis revealed significant differences in protein expression (>100 proteins) between Gleason 3 and Gleason 4 regions-in both stromal and epithelial compartments. A subset of these proteins has had prior strong association with prostate cancer, thereby providing evidence for the authenticity of the approach. Finally, validation of these proteins by immunohistochemistry has been obtained using an independent cohort of prostate cancer tumor specimens.Implications: This unbiased strategy provides a strong foundation for the development of biomarker protein panels with significant diagnostic and prognostic potential. Mol Cancer Res; 15(3); 281-93. ©2017 AACR.
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Affiliation(s)
- Lisa Staunton
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Claire Tonry
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Rosina Lis
- Center for Molecular Oncologic Pathology, Harvard Medical School, Boston, Massachusetts
| | - Virginia Espina
- Center for Applied Proteomics, George Mason University, Fairfax, Virginia
| | - Lance Liotta
- Center for Applied Proteomics, George Mason University, Fairfax, Virginia
| | - Rosanna Inzitari
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Michaela Bowden
- Center for Molecular Oncologic Pathology, Harvard Medical School, Boston, Massachusetts
| | - Aurelie Fabre
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.,Department of Histopathology, St Vincent's University Hospital, Dublin, Ireland
| | - John O'Leary
- Department of Histopathology, St. James's Hospital, Dublin, Ireland
| | - Stephen P Finn
- Department of Histopathology, St. James's Hospital, Dublin, Ireland
| | - Massimo Loda
- Center for Molecular Oncologic Pathology, Harvard Medical School, Boston, Massachusetts.,Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Stephen R Pennington
- UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland.
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9
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Annotation of Alternatively Spliced Proteins and Transcripts with Protein-Folding Algorithms and Isoform-Level Functional Networks. Methods Mol Biol 2017; 1558:415-436. [PMID: 28150250 DOI: 10.1007/978-1-4939-6783-4_20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Tens of thousands of splice isoforms of proteins have been catalogued as predicted sequences from transcripts in humans and other species. Relatively few have been characterized biochemically or structurally. With the extensive development of protein bioinformatics, the characterization and modeling of isoform features, isoform functions, and isoform-level networks have advanced notably. Here we present applications of the I-TASSER family of algorithms for folding and functional predictions and the IsoFunc, MIsoMine, and Hisonet data resources for isoform-level analyses of network and pathway-based functional predictions and protein-protein interactions. Hopefully, predictions and insights from protein bioinformatics will stimulate many experimental validation studies.
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10
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Guo J, Cui Y, Yan Z, Luo Y, Zhang W, Deng S, Tang S, Zhang G, He QY, Wang T. Phosphoproteome Characterization of Human Colorectal Cancer SW620 Cell-Derived Exosomes and New Phosphosite Discovery for C-HPP. J Proteome Res 2016; 15:4060-4072. [PMID: 27470641 DOI: 10.1021/acs.jproteome.6b00391] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Jiahui Guo
- Key Laboratory of Functional
Protein Research of Guangdong Higher Education Institutes, Institute
of Life and Health Engineering, College of Life Science and Technology, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China
| | - Yizhi Cui
- Key Laboratory of Functional
Protein Research of Guangdong Higher Education Institutes, Institute
of Life and Health Engineering, College of Life Science and Technology, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China
| | - Ziqi Yan
- Key Laboratory of Functional
Protein Research of Guangdong Higher Education Institutes, Institute
of Life and Health Engineering, College of Life Science and Technology, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China
| | - Yanzhang Luo
- Key Laboratory of Functional
Protein Research of Guangdong Higher Education Institutes, Institute
of Life and Health Engineering, College of Life Science and Technology, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China
| | - Wanling Zhang
- Key Laboratory of Functional
Protein Research of Guangdong Higher Education Institutes, Institute
of Life and Health Engineering, College of Life Science and Technology, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China
| | - Suyuan Deng
- Key Laboratory of Functional
Protein Research of Guangdong Higher Education Institutes, Institute
of Life and Health Engineering, College of Life Science and Technology, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China
| | - Shengquan Tang
- Key Laboratory of Functional
Protein Research of Guangdong Higher Education Institutes, Institute
of Life and Health Engineering, College of Life Science and Technology, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China
| | - Gong Zhang
- Key Laboratory of Functional
Protein Research of Guangdong Higher Education Institutes, Institute
of Life and Health Engineering, College of Life Science and Technology, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China
| | - Qing-Yu He
- Key Laboratory of Functional
Protein Research of Guangdong Higher Education Institutes, Institute
of Life and Health Engineering, College of Life Science and Technology, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China
| | - Tong Wang
- Key Laboratory of Functional
Protein Research of Guangdong Higher Education Institutes, Institute
of Life and Health Engineering, College of Life Science and Technology, Jinan University, 601 Huangpu Avenue West, Guangzhou 510632, China
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11
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Büchler R, Wendler S, Muckova P, Großkreutz J, Rhode H. The intricacy of biomarker complexity-the identification of a genuine proteomic biomarker is more complicated than believed. Proteomics Clin Appl 2016; 10:1073-1076. [PMID: 27377180 DOI: 10.1002/prca.201600067] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 06/22/2016] [Accepted: 06/29/2016] [Indexed: 11/10/2022]
Abstract
Several reasons have been put forward to explain the irreproducibility of proteomic biomarker search. However, these reasons pertain to almost every part of biomarker search across the entire analytical workflow but are entirely experimental or methodological. However, in this article we point out that there is a further cause of such irreproducibility. This is not an additional methodological or experimental cause but arises directly from the biology of protein expression. It arises from the fact that disease changes the diversity within protein families. This cause of irreproducibility has been very little studied in relation to proteomic biomarker search. Gene expression is highly variable even in healthy people. Therefore, multiple proteoforms are also to be expected when gene expression is disrupted by disease, proteoforms that may be differently altered by pathology. In consequence, it is illogical to expect that the whole protein family produces a reliably usable biomarker. It is more reasonable to expect that a specific proteoform fulfills this role. Appropriate sample pre-fractionation methods and data analyses could help to identify this version, carrying the modification or the epitope required.
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Affiliation(s)
- Rita Büchler
- Institute of Biochemistry I, University Hospital Jena, Jena, Germany
| | - Sindy Wendler
- Institute of Biochemistry I, University Hospital Jena, Jena, Germany
| | - Petra Muckova
- Institute of Biochemistry I, University Hospital Jena, Jena, Germany.,Clinic of Neurology, University Hospital Jena, Jena, Germany
| | | | - Heidrun Rhode
- Institute of Biochemistry I, University Hospital Jena, Jena, Germany
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12
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Paik YK, Omenn GS, Overall CM, Deutsch EW, Hancock WS. Recent Advances in the Chromosome-Centric Human Proteome Project: Missing Proteins in the Spot Light. J Proteome Res 2016; 14:3409-14. [PMID: 26337862 DOI: 10.1021/acs.jproteome.5b00785] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Young-Ki Paik
- Yonsei Proteome Research Center, Yonsei University , Seoul 120-749, Korea
| | - Gilbert S Omenn
- Center for Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, Michigan 48109, United States.,Yonsei Proteome Research Center, Yonsei University , Seoul 120-749, Korea
| | - Christopher M Overall
- Department of Biochemistry and Molecular Biology, University of British Columbia , Vancouver, British Columbia V6T 1Z3, Canada.,Yonsei Proteome Research Center, Yonsei University , Seoul 120-749, Korea
| | - Eric W Deutsch
- Institute for Systems Biology , Seattle, Washington 98109, United States.,Yonsei Proteome Research Center, Yonsei University , Seoul 120-749, Korea
| | - William S Hancock
- Department of Chemical Biology, Northeastern University , Boston, Massachusetts 02115, United States.,Yonsei Proteome Research Center, Yonsei University , Seoul 120-749, Korea
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Panwar B, Menon R, Eksi R, Li HD, Omenn GS, Guan Y. Genome-Wide Functional Annotation of Human Protein-Coding Splice Variants Using Multiple Instance Learning. J Proteome Res 2016; 15:1747-53. [PMID: 27142340 DOI: 10.1021/acs.jproteome.5b00883] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
The vast majority of human multiexon genes undergo alternative splicing and produce a variety of splice variant transcripts and proteins, which can perform different functions. These protein-coding splice variants (PCSVs) greatly increase the functional diversity of proteins. Most functional annotation algorithms have been developed at the gene level; the lack of isoform-level gold standards is an important intellectual limitation for currently available machine learning algorithms. The accumulation of a large amount of RNA-seq data in the public domain greatly increases our ability to examine the functional annotation of genes at isoform level. In the present study, we used a multiple instance learning (MIL)-based approach for predicting the function of PCSVs. We used transcript-level expression values and gene-level functional associations from the Gene Ontology database. A support vector machine (SVM)-based 5-fold cross-validation technique was applied. Comparatively, genes with multiple PCSVs performed better than single PCSV genes, and performance also improved when more examples were available to train the models. We demonstrated our predictions using literature evidence of ADAM15, LMNA/C, and DMXL2 genes. All predictions have been implemented in a web resource called "IsoFunc", which is freely available for the global scientific community through http://guanlab.ccmb.med.umich.edu/isofunc .
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Affiliation(s)
- Bharat Panwar
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Rajasree Menon
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Ridvan Eksi
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Hong-Dong Li
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, ‡Department of Internal Medicine, §Department of Human Genetics and School of Public Health, and ∥Department of Electrical Engineering and Computer Science, University of Michigan , Ann Arbor, Michigan 48109, United States
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