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Kordshouli SO, Tahmasebi A, Moghadam A, Ramezani A, Niazi A. A comprehensive meta-analysis of transcriptome data to identify signature genes associated with pancreatic ductal adenocarcinoma. PLoS One 2024; 19:e0289561. [PMID: 38324544 PMCID: PMC10849254 DOI: 10.1371/journal.pone.0289561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 07/20/2023] [Indexed: 02/09/2024] Open
Abstract
PURPOSE Pancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of less than 5%. Absence of symptoms at primary tumor stages, as well as high aggressiveness of the tumor can lead to high mortality in cancer patients. Most patients are recognized at the advanced or metastatic stage without surgical symptom, because of the lack of reliable early diagnostic biomarkers. The objective of this work was to identify potential cancer biomarkers by integrating transcriptome data. METHODS Several transcriptomic datasets comprising of 11 microarrays were retrieved from the GEO database. After pre-processing, a meta-analysis was applied to identify differentially expressed genes (DEGs) between tumor and nontumor samples for datasets. Next, co-expression analysis, functional enrichment and survival analyses were used to determine the functional properties of DEGs and identify potential prognostic biomarkers. In addition, some regulatory factors involved in PDAC including transcription factors (TFs), protein kinases (PKs), and miRNAs were identified. RESULTS After applying meta-analysis, 1074 DEGs including 539 down- and 535 up-regulated genes were identified. Pathway enrichment analyzes using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that DEGs were significantly enriched in the HIF-1 signaling pathway and focal adhesion. The results also showed that some of the DEGs were assigned to TFs that belonged to 23 conserved families. Sixty-four PKs were identified among the DEGs that showed the CAMK family was the most abundant group. Moreover, investigation of corresponding upstream regions of DEGs identified 11 conserved sequence motifs. Furthermore, weighted gene co-expression network analysis (WGCNA) identified 8 modules, more of them were significantly enriched in Ras signaling, p53 signaling, MAPK signaling pathways. In addition, several hubs in modules were identified, including EMP1, EVL, ELP5, DEF8, MTERF4, GLUP1, CAPN1, IGF1R, HSD17B14, TOM1L2 and RAB11FIP3. According to survival analysis, it was identified that the expression levels of two genes, EMP1 and RAB11FIP3 are related to prognosis. CONCLUSION We identified several genes critical for PDAC based on meta-analysis and system biology approach. These genes may serve as potential targets for the treatment and prognosis of PDAC.
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Affiliation(s)
| | | | - Ali Moghadam
- Institute of Biotechnology, Shiraz University, Shiraz, Iran
| | - Amin Ramezani
- Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
- Shiraz Institute for Cancer Research, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Ali Niazi
- Institute of Biotechnology, Shiraz University, Shiraz, Iran
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2
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Pérez-Díez I, Andreu Z, Hidalgo MR, Perpiñá-Clérigues C, Fantín L, Fernandez-Serra A, de la Iglesia-Vaya M, Lopez-Guerrero JA, García-García F. A Comprehensive Transcriptional Signature in Pancreatic Ductal Adenocarcinoma Reveals New Insights into the Immune and Desmoplastic Microenvironments. Cancers (Basel) 2023; 15:cancers15112887. [PMID: 37296850 DOI: 10.3390/cancers15112887] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/11/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) prognoses and treatment responses remain devastatingly poor due partly to the highly heterogeneous, aggressive, and immunosuppressive nature of this tumor type. The intricate relationship between the stroma, inflammation, and immunity remains vaguely understood in the PDAC microenvironment. Here, we performed a meta-analysis of stroma-, and immune-related gene expression in the PDAC microenvironment to improve disease prognosis and therapeutic development. We selected 21 PDAC studies from the Gene Expression Omnibus and ArrayExpress databases, including 922 samples (320 controls and 602 cases). Differential gene enrichment analysis identified 1153 significant dysregulated genes in PDAC patients that contribute to a desmoplastic stroma and an immunosuppressive environment (the hallmarks of PDAC tumors). The results highlighted two gene signatures related to the immune and stromal environments that cluster PDAC patients into high- and low-risk groups, impacting patients' stratification and therapeutic decision making. Moreover, HCP5, SLFN13, IRF9, IFIT2, and IFI35 immune genes are related to the prognosis of PDAC patients for the first time.
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Affiliation(s)
- Irene Pérez-Díez
- Bioinformatics and Biostatistics Unit, Principe Felipe Research Center (CIPF), 46012 Valencia, Spain
- Biomedical Imaging Unit FISABIO-CIPF, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana, 46012 Valencia, Spain
- IVO-CIPF Joint Research Unit of Cancer, Príncipe Felipe Research Center (CIPF), 46012 Valencia, Spain
| | - Zoraida Andreu
- IVO-CIPF Joint Research Unit of Cancer, Príncipe Felipe Research Center (CIPF), 46012 Valencia, Spain
| | - Marta R Hidalgo
- Bioinformatics and Biostatistics Unit, Principe Felipe Research Center (CIPF), 46012 Valencia, Spain
- IVO-CIPF Joint Research Unit of Cancer, Príncipe Felipe Research Center (CIPF), 46012 Valencia, Spain
| | - Carla Perpiñá-Clérigues
- Bioinformatics and Biostatistics Unit, Principe Felipe Research Center (CIPF), 46012 Valencia, Spain
- IVO-CIPF Joint Research Unit of Cancer, Príncipe Felipe Research Center (CIPF), 46012 Valencia, Spain
- Department of Physiology, School of Medicine and Dentistry, University of Valencia, 46010 Valencia, Spain
| | - Lucía Fantín
- Bioinformatics and Biostatistics Unit, Principe Felipe Research Center (CIPF), 46012 Valencia, Spain
| | - Antonio Fernandez-Serra
- IVO-CIPF Joint Research Unit of Cancer, Príncipe Felipe Research Center (CIPF), 46012 Valencia, Spain
- Laboratory of Molecular Biology, Fundación Instituto Valenciano de Oncología, 46009 Valencia, Spain
| | - María de la Iglesia-Vaya
- Biomedical Imaging Unit FISABIO-CIPF, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunidad Valenciana, 46012 Valencia, Spain
- IVO-CIPF Joint Research Unit of Cancer, Príncipe Felipe Research Center (CIPF), 46012 Valencia, Spain
| | - José A Lopez-Guerrero
- IVO-CIPF Joint Research Unit of Cancer, Príncipe Felipe Research Center (CIPF), 46012 Valencia, Spain
- Laboratory of Molecular Biology, Fundación Instituto Valenciano de Oncología, 46009 Valencia, Spain
- Department of Pathology, Medical School, Catholic University of Valencia, 46001 Valencia, Spain
| | - Francisco García-García
- Bioinformatics and Biostatistics Unit, Principe Felipe Research Center (CIPF), 46012 Valencia, Spain
- IVO-CIPF Joint Research Unit of Cancer, Príncipe Felipe Research Center (CIPF), 46012 Valencia, Spain
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3
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Silvestri M, Nghia Vu T, Nichetti F, Niger M, Di Cosimo S, De Braud F, Pruneri G, Pawitan Y, Calza S, Cappelletti V. Comprehensive transcriptomic analysis to identify biological and clinical differences in cholangiocarcinoma. Cancer Med 2023; 12:10156-10168. [PMID: 36938752 PMCID: PMC10166943 DOI: 10.1002/cam4.5719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND Cholangiocarcinoma (CC) is a rare and aggressive disease with limited therapeutic options and a poor prognosis. All available public records of cohorts reporting transcriptomic data on intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC) were collected with the aim to provide a comprehensive gene expression-based classification with clinical relevance. METHODS A total of 543 patients with primary tumor tissues profiled by RNAseq and microarray platforms from seven public datasets were used as a discovery set to identify distinct biological subgroups. Group predictors developed on the discovery sets were applied to a single cohort of 131 patients profiled with RNAseq for validation and assessment of clinical relevance leveraging machine learning techniques. RESULTS By unsupervised clustering analysis of gene expression data we identified both in the ICC and ECC discovery datasets four subgroups characterized by a distinct type of immune infiltrate and signaling pathways. We next developed class predictors using short gene list signatures and identified in an independent dataset subgroups of ICC tumors at different prognosis. CONCLUSIONS The developed class-predictor allows identification of CC subgroups with specific biological features and clinical behavior at single-sample level. Such results represent the starting point for a complete molecular characterization of CC, including integration of genomics data to develop in clinical practice.
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Affiliation(s)
- Marco Silvestri
- Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.,Unit of Biostatistics, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Trung Nghia Vu
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Federico Nichetti
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy.,Computational Oncology Group, Molecular Precision Oncology Program, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Monica Niger
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Serena Di Cosimo
- Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Filippo De Braud
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Giancarlo Pruneri
- Department Pathology and Laboratory Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Yudi Pawitan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Stefano Calza
- Unit of Biostatistics, Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Vera Cappelletti
- Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
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Gholizadeh M, Mazlooman SR, Hadizadeh M, Drozdzik M, Eslami S. Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis. MethodsX 2023; 10:102021. [PMID: 36713306 PMCID: PMC9879787 DOI: 10.1016/j.mex.2023.102021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 01/15/2023] [Indexed: 01/19/2023] Open
Abstract
One methodology extensively used to develop biomarkers is the precise detection of highly responsive genes that can distinguish cancer samples from healthy samples. The purpose of this study was to screen for potential hepatocellular carcinoma (HCC) biomarkers based on non-fusion integrative multi-platform meta-analysis method. The gene expression profiles of liver tissue samples from two microarray platforms were initially analyzed using a meta-analysis based on an empirical Bayesian method to robust discover differentially expressed genes in HCC and non-tumor tissues. Then, using the bioinformatics technique of weighted correlation network analysis, the highly associated prioritized Differentially Expressed Genes (DEGs) were clustered. Co-expression network and topological analysis were utilized to identify sub-clusters and confirm candidate genes. Next, a diagnostic model was developed and validated using a machine learning algorithm. To construct a prognostic model, the Cox proportional hazard regression analysis was applied and validated. We identified three genes as specific biomarkers for the diagnosis of HCC based on accuracy and feasibility. The diagnostic model's area under the curve was 0.931 with confidence interval of 0.923-0.952.•Non-fusion integrative multi-platform meta-analysis method.•Classification methods and biomarkers recognition via machine learning method.•Biomarker validation models.
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Affiliation(s)
- Maryam Gholizadeh
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91388-13944, Iran
| | - Seyed Reza Mazlooman
- Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1477893780, Iran
| | - Morteza Hadizadeh
- Physiology Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman 7616913555, Iran
| | - Marek Drozdzik
- Department of Experimental and Clinical Pharmacology, Pomeranian Medical University, Szczecin 70-111, Poland,Corresponding authors.
| | - Saeid Eslami
- Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad 91388-13944, Iran,Corresponding authors.
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Mukherjee A, Acharya PB, Singh A, Mukunthan KS. Identification of therapeutic
miRNAs
from the Arsenic induced gene expression profile of hepatocellular carcinoma. Chem Biol Drug Des 2022; 101:1027-1041. [PMID: 36052834 DOI: 10.1111/cbdd.14132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 08/01/2022] [Accepted: 08/14/2022] [Indexed: 11/03/2022]
Abstract
Hepatocellular carcinoma (HCC) is the most common primary liver cancer, with a rising worldwide burden due to a lack of efficient treatment techniques and diagnosis after it has metastasized. Therefore, small non-coding RNA (miRNAs) as protein translation inhibitors are gaining attention that degrades or suppress specific gene transcripts, making it a prime strategy for oncogenes or tumor suppression. Systematic research with miRNAs in combination with Arsenic, which has been employed as a drug to treat several diseases, including cancer, was focused on cellular responses through interacting with multiple biological targets. The differential gene expression of the DNA microarray dataset (GSE48441) revealed the association of sterol, cholesterol, and lipid metabolic processes. With the aid of the network pharmacology approach, hsa-mir-335-5p was uncovered to negatively regulate the important nodes driving the transport and utilization of essential compounds for the rapid growth and proliferation of cancer cells. The binding energies of the duplexes were validated by the minimal free energies of the mRNAs for hsa-mir-335-5p, indicating energetically desirable binding association. The molecular interactions between hsa-mir-335-5p, which interacts with the Argonaute protein in the RNA induced silencing complex, and the target-specific genes were also investigated, revealing its susceptibility to be employed in in vitro studies.
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Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology Manipal Academy of Higher Education Manipal India
| | | | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology Manipal Academy of Higher Education Manipal India
| | - K. S. Mukunthan
- Department of Biotechnology, Manipal Institute of Technology Manipal Academy of Higher Education Manipal India
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6
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Maleknia S, Tavassolifar MJ, Mottaghitalab F, Zali MR, Meyfour A. Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis. Mol Med 2022; 28:86. [PMID: 35922752 PMCID: PMC9347150 DOI: 10.1186/s10020-022-00513-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 06/27/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Regardless of improvements in controlling the COVID-19 pandemic, the lack of comprehensive insight into SARS-COV-2 pathogenesis is still a sophisticated challenge. In order to deal with this challenge, we utilized advanced bioinformatics and machine learning algorithms to reveal more characteristics of SARS-COV-2 pathogenesis and introduce novel host response-based diagnostic biomarker panels. METHODS In the present study, eight published RNA-Seq datasets related to whole-blood (WB) and nasopharyngeal (NP) swab samples of patients with COVID-19, other viral and non-viral acute respiratory illnesses (ARIs), and healthy controls (HCs) were integrated. To define COVID-19 meta-signatures, Gene Ontology and pathway enrichment analyses were applied to compare COVID-19 with other similar diseases. Additionally, CIBERSORTx was executed in WB samples to detect the immune cell landscape. Furthermore, the optimum WB- and NP-based diagnostic biomarkers were identified via all the combinations of 3 to 9 selected features and the 2-phases machine learning (ML) method which implemented k-fold cross validation and independent test set validation. RESULTS The host gene meta-signatures obtained for SARS-COV-2 infection were different in the WB and NP samples. The gene ontology and enrichment results of the WB dataset represented the enhancement in inflammatory host response, cell cycle, and interferon signature in COVID-19 patients. Furthermore, NP samples of COVID-19 in comparison with HC and non-viral ARIs showed the significant upregulation of genes associated with cytokine production and defense response to the virus. In contrast, these pathways in COVID-19 compared to other viral ARIs were strikingly attenuated. Notably, immune cell proportions of WB samples altered in COVID-19 versus HC. Moreover, the optimum WB- and NP-based diagnostic panels after two phases of ML-based validation included 6 and 8 markers with an accuracy of 97% and 88%, respectively. CONCLUSIONS Based on the distinct gene expression profiles of WB and NP, our results indicated that SARS-COV-2 function is body-site-specific, although according to the common signature in WB and NP COVID-19 samples versus controls, this virus also induces a global and systematic host response to some extent. We also introduced and validated WB- and NP-based diagnostic biomarkers using ML methods which can be applied as a complementary tool to diagnose the COVID-19 infection from non-COVID cases.
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Affiliation(s)
- Samaneh Maleknia
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Javad Tavassolifar
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Faezeh Mottaghitalab
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Anna Meyfour
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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7
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Gholizadeh M, Hadizadeh M, Mazlooman SR, Eslami S, Raoufi S, Farsimadan M, Rashidifar M, Drozdzik M, Mehrabani M. Integrative multi-platform meta-analysis of hepatocellular carcinoma gene expression profiles for identifying prognostic and diagnostic biomarkers. Genes Dis 2022. [DOI: 10.1016/j.gendis.2022.07.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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8
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Jin G, Ruan Q, Shangguan F, Lan L. RUNX2 and LAMC2: promising pancreatic cancer biomarkers identified by an integrative data mining of pancreatic adenocarcinoma tissues. Aging (Albany NY) 2021; 13:22963-22984. [PMID: 34606473 PMCID: PMC8544338 DOI: 10.18632/aging.203589] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 09/18/2021] [Indexed: 01/25/2023]
Abstract
Pancreatic carcinoma (PC) is a severe disease associated with high mortality. Although strategies for cancer therapy have made great progress, outcomes of pancreatic carcinoma patients remain extremely poor. Therefore, it is urgent to find novel biomarkers and therapeutic targets. To identify biomarkers for early diagnosis and therapy, three mRNA microarray datasets and two miRNA datasets were selected, and combinative analysis was performed by GEO2R. Functional and pathway enrichment analysis were performed using DAVID database. MiRTarBase, miRWalk and Diana Tools were used to get key genes. TCGA, HPA and western blotting were used to verify diagnostic and prognostic value of key genes. By integrating mRNA and miRNA expression profiles, we identified 114 differentially expressed genes and 114 differentially expressed miRNAs, respectively. Then, three overlapping key genes, RUNX2, LAMC2 and FBXO32, were found. Their protein levels in pancreatic tissue from PC patients and normal people were analyzed by immunohistochemical staining and western blotting. RUNX2 showed a potential property to identify PC. Aberrant over-expression of LAMC2 was associated with poor prognosis of PC patients, tumor status and subtypes. In summary, our current study identified that RUNX2 and LAMC2 may be promising targets for early diagnosis and therapy of PC patients.
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Affiliation(s)
- Guihua Jin
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Qingqing Ruan
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Fugen Shangguan
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
| | - Linhua Lan
- Key Laboratory of Diagnosis and Treatment of Severe Hepato-Pancreatic Diseases of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China
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Gene expression analysis of combined RNA-seq experiments using a receiver operating characteristic calibrated procedure. Comput Biol Chem 2021; 93:107515. [PMID: 34044204 DOI: 10.1016/j.compbiolchem.2021.107515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 05/12/2021] [Indexed: 10/21/2022]
Abstract
Because of rapid advancements in sequencing technology, the experimental platforms of RNA-seq are updated frequently. It is quite common to combine data sets from several experimental platforms for analysis in order to increase the sample size and achieve more powerful tests for detecting the presence of differential gene expression. The data sets combined from different experimental platforms will have a complex data distribution, which causes a major problem in statistical modeling as well as in multiple testing. Although plenty of research have studied this problem by modeling the batch effects, there are no general and robust data-driven procedures for RNA-seq analysis. In this paper we propose a new robust procedure which combines the use of popular methods (packages) with a data-driven simulation (DDS). We construct the average receiver operating characteristic curve through the DDS to provide the calibrated levels of significance for multiple testing. Instead of further modifying the adjusted p-values, we calibrated the levels of significance for each specific method and mean effect model. The procedure was demonstrated with several popular RNA-seq analysis methods (edgeR, DEseq2, limma+voom). The proposed procedure relaxes the stringent assumptions of data distributions for RNA-seq analysis methods and is illustrated using colorectal cancer studies from The Cancer Genome Atlas database.
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10
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Arora I, Tollefsbol TO. Computational methods and next-generation sequencing approaches to analyze epigenetics data: Profiling of methods and applications. Methods 2021; 187:92-103. [PMID: 32941995 PMCID: PMC7914156 DOI: 10.1016/j.ymeth.2020.09.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/08/2020] [Accepted: 09/10/2020] [Indexed: 12/20/2022] Open
Abstract
Epigenetics is mainly comprised of features that regulate genomic interactions thereby playing a crucial role in a vast array of biological processes. Epigenetic mechanisms such as DNA methylation and histone modifications influence gene expression by modulating the packaging of DNA in the nucleus. A plethora of studies have emphasized the importance of analyzing epigenetics data through genome-wide studies and high-throughput approaches, thereby providing key insights towards epigenetics-based diseases such as cancer. Recent advancements have been made towards translating epigenetics research into a high throughput approach such as genome-scale profiling. Amongst all, bioinformatics plays a pivotal role in achieving epigenetics-related computational studies. Despite significant advancements towards epigenomic profiling, it is challenging to understand how various epigenetic modifications such as chromatin modifications and DNA methylation regulate gene expression. Next-generation sequencing (NGS) provides accurate and parallel sequencing thereby allowing researchers to comprehend epigenomic profiling. In this review, we summarize different computational methods such as machine learning and other bioinformatics tools, publicly available databases and resources to identify key modifications associated with epigenetic machinery. Additionally, the review also focuses on understanding recent methodologies related to epigenome profiling using NGS methods ranging from library preparation, different sequencing platforms and analytical techniques to evaluate various epigenetic modifications such as DNA methylation and histone modifications. We also provide detailed information on bioinformatics tools and computational strategies responsible for analyzing large scale data in epigenetics.
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Affiliation(s)
- Itika Arora
- Department of Biology, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA.
| | - Trygve O Tollefsbol
- Department of Biology, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA; Comprehensive Center for Healthy Aging, University of Alabama Birmingham, 1530 3rd Avenue South, Birmingham, AL 35294, USA; Comprehensive Cancer Center, University of Alabama Birmingham, 1802 6th Avenue South, Birmingham, AL 35294, USA; Nutrition Obesity Research Center, University of Alabama Birmingham, 1675 University Boulevard, Birmingham, AL 35294, USA; Comprehensive Diabetes Center, University of Alabama Birmingham, 1825 University Boulevard, Birmingham, AL 35294, USA.
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11
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Jeon YS, Shivakumar M, Kim D, Kim CS, Lee JS. Reliability of microarray analysis for studying periodontitis: low consistency in 2 periodontitis cohort data sets from different platforms and an integrative meta-analysis. J Periodontal Implant Sci 2021; 51:18-29. [PMID: 33634612 PMCID: PMC7920837 DOI: 10.5051/jpis.2002120106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/14/2020] [Accepted: 09/24/2020] [Indexed: 11/08/2022] Open
Abstract
PURPOSE The aim of this study was to compare the characteristic expression patterns of advanced periodontitis in 2 cohort data sets analyzed using different microarray platforms, and to identify differentially expressed genes (DEGs) through a meta-analysis of both data sets. METHODS Twenty-two patients for cohort 1 and 40 patients for cohort 2 were recruited with the same inclusion criteria. The 2 cohort groups were analyzed using different platforms: Illumina and Agilent. A meta-analysis was performed to increase reliability by removing statistical differences between platforms. An integrative meta-analysis based on an empirical Bayesian methodology (ComBat) was conducted. DEGs for the integrated data sets were identified using the limma package to adjust for age, sex, and platform and compared with the results for cohorts 1 and 2. Clustering and pathway analyses were also performed. RESULTS This study detected 557 and 246 DEGs in cohorts 1 and 2, respectively, with 146 and 42 significantly enriched gene ontology (GO) terms. Overlapping between cohorts 1 and 2 was present in 59 DEGs and 18 GO terms. However, only 6 genes from the top 30 enriched DEGs overlapped, and there were no overlapping GO terms in the top 30 enriched pathways. The integrative meta-analysis detected 34 DEGs, of which 10 overlapped in all the integrated data sets of cohorts 1 and 2. CONCLUSIONS The characteristic expression pattern differed between periodontitis and the healthy periodontium, but the consistency between the data sets from different cohorts and metadata was too low to suggest specific biomarkers for identifying periodontitis.
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Affiliation(s)
- Yoon Seon Jeon
- Department of Periodontology, Research Institute for Periodontal Regeneration, Yonsei University College of Dentistry, Seoul, Korea
| | - Manu Shivakumar
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chang Sung Kim
- Department of Periodontology, Research Institute for Periodontal Regeneration, Yonsei University College of Dentistry, Seoul, Korea
| | - Jung Seok Lee
- Department of Periodontology, Research Institute for Periodontal Regeneration, Yonsei University College of Dentistry, Seoul, Korea.
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12
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Junet V, Farrés J, Mas JM, Daura X. CuBlock: a cross-platform normalization method for gene-expression microarrays. Bioinformatics 2021; 37:2365-2373. [PMID: 33609102 PMCID: PMC8388031 DOI: 10.1093/bioinformatics/btab105] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 02/04/2021] [Accepted: 02/16/2021] [Indexed: 12/28/2022] Open
Abstract
Motivation Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups. Results We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct datasets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these datasets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log2 transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study. Availability and implementation CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Valentin Junet
- Anaxomics Biotech SL, Barcelona, 08008, Spain.,Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, 08193, Spain
| | | | - José M Mas
- Anaxomics Biotech SL, Barcelona, 08008, Spain
| | - Xavier Daura
- Institute of Biotechnology and Biomedicine, Universitat Autònoma de Barcelona, 08193, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, 08010, Spain
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Atay S. Integrated transcriptome meta-analysis of pancreatic ductal adenocarcinoma and matched adjacent pancreatic tissues. PeerJ 2020; 8:e10141. [PMID: 33194391 PMCID: PMC7597628 DOI: 10.7717/peerj.10141] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 09/19/2020] [Indexed: 12/17/2022] Open
Abstract
A comprehensive meta-analysis of publicly available gene expression microarray data obtained from human-derived pancreatic ductal adenocarcinoma (PDAC) tissues and their histologically matched adjacent tissue samples was performed to provide diagnostic and prognostic biomarkers, and molecular targets for PDAC. An integrative meta-analysis of four submissions (GSE62452, GSE15471, GSE62165, and GSE56560) containing 105 eligible tumor-adjacent tissue pairs revealed 344 differentially over-expressed and 168 repressed genes in PDAC compared to the adjacent-to-tumor samples. The validation analysis using TCGA combined GTEx data confirmed 98.24% of the identified up-regulated and 73.88% of the down-regulated protein-coding genes in PDAC. Pathway enrichment analysis showed that “ECM-receptor interaction”, “PI3K-Akt signaling pathway”, and “focal adhesion” are the most enriched KEGG pathways in PDAC. Protein-protein interaction analysis identified FN1, TIMP1, and MSLN as the most highly ranked hub genes among the DEGs. Transcription factor enrichment analysis revealed that TCF7, CTNNB1, SMAD3, and JUN are significantly activated in PDAC, while SMAD7 is inhibited. The prognostic significance of the identified and validated differentially expressed genes in PDAC was evaluated via survival analysis of TCGA Pan-Cancer pancreatic ductal adenocarcinoma data. The identified candidate prognostic biomarkers were then validated in four external validation datasets (GSE21501, GSE50827, GSE57495, and GSE71729) to further improve reliability. A total of 28 up-regulated genes were found to be significantly correlated with worse overall survival in patients with PDAC. Twenty-one of the identified prognostic genes (ITGB6, LAMC2, KRT7, SERPINB5, IGF2BP3, IL1RN, MPZL2, SFTA2, MET, LAMA3, ARNTL2, SLC2A1, LAMB3, COL17A1, EPSTI1, IL1RAP, AK4, ANXA2, S100A16, KRT19, and GPRC5A) were also found to be significantly correlated with the pathological stages of the disease. The results of this study provided promising prognostic biomarkers that have the potential to differentiate PDAC from both healthy and adjacent-to-tumor pancreatic tissues. Several novel dysregulated genes merit further study as potentially promising candidates for the development of more effective treatment strategies for PDAC.
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Affiliation(s)
- Sevcan Atay
- Department of Medical Biochemistry, Ege University Faculty of Medicine, Izmir, Turkey
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Maleknia S, Salehi Z, Rezaei Tabar V, Sharifi-Zarchi A, Kavousi K. An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus. Arthritis Res Ther 2020; 22:156. [PMID: 32576231 PMCID: PMC7310461 DOI: 10.1186/s13075-020-02239-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 06/05/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND A comprehensive intuition of the systemic lupus erythematosus (SLE), as a complex and multifactorial disease, is a biological challenge. Dealing with this challenge needs employing sophisticated bioinformatics algorithms to discover the unknown aspects. This study aimed to underscore key molecular characteristics of SLE pathogenesis, which may serve as effective targets for therapeutic intervention. METHODS In the present study, the human peripheral blood mononuclear cell (PBMC) microarray datasets (n = 6), generated by three platforms, which included SLE patients (n = 220) and healthy control samples (n = 135) were collected. Across each platform, we integrated the datasets by cross-platform normalization (CPN). Subsequently, through BNrich method, the structures of Bayesian networks (BNs) were extracted from KEGG-indexed SLE, TCR, and BCR signaling pathways; the values of the node (gene) and edge (intergenic relationships) parameters were estimated within each integrated datasets. Parameters with the FDR < 0.05 were considered significant. Finally, a mixture model was performed to decipher the signaling pathway alterations in the SLE patients compared to healthy controls. RESULTS In the SLE signaling pathway, we identified the dysregulation of several nodes involved in the (1) clearance mechanism (SSB, MACROH2A2, TRIM21, H2AX, and C1Q gene family), (2) autoantigen presentation by MHCII (HLA gene family, CD80, IL10, TNF, and CD86), and (3) end-organ damage (FCGR1A, ELANE, and FCGR2A). As a remarkable finding, we demonstrated significant perturbation in CD80 and CD86 to CD28, CD40LG to CD40, C1QA and C1R to C2, and C1S to C4A edges. Moreover, we not only replicated previous studies regarding alterations of subnetworks involved in TCR and BCR signaling pathways (PI3K/AKT, MAPK, VAV gene family, AP-1 transcription factor) but also distinguished several significant edges between genes (PPP3 to NFATC gene families). Our findings unprecedentedly showed that different parameter values assign to the same node based on the pathway topology (the PIK3CB parameter values were 1.7 in TCR vs - 0.5 in BCR signaling pathway). CONCLUSIONS Applying the BNrich as a hybridized network construction method, we highlight under-appreciated systemic alterations of SLE, TCR, and BCR signaling pathways in SLE. Consequently, having such a systems biology approach opens new insights into the context of multifactorial disorders.
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Affiliation(s)
- Samaneh Maleknia
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran
| | - Zahra Salehi
- Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Vahid Rezaei Tabar
- Department of Statistics, Allameh Tabataba'i University, Tehran, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences, Tehran, Iran
| | - Ali Sharifi-Zarchi
- Department of Computer Engineering, Sharif University of Technology, Tehran, Iran
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, Tehran, Iran
| | - Kaveh Kavousi
- Laboratory of Complex Biological Systems and Bioinformatics (CBB), Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
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Almeida PP, Cardoso CP, de Freitas LM. PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression. BMC Cancer 2020; 20:82. [PMID: 32005189 PMCID: PMC6995241 DOI: 10.1186/s12885-020-6533-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 01/13/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment. METHODS We performed a gene expression microarray meta-analysis of the tumor against normal tissues in order to identify differentially expressed genes (DEG) shared among all datasets, named core-genes (CG). We confirmed the CG protein expression in pancreatic tissue through The Human Protein Atlas. It was selected five genes with the highest area under the curve (AUC) among these proteins with expression confirmed in the tumor group to train an artificial neural network (ANN) to classify samples. RESULTS This microarray included 461 tumor and 187 normal samples. We identified a CG composed of 40 genes, 39 upregulated, and one downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that fourteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our ANN, we selected the best genes (AHNAK2, KRT19, LAMB3, LAMC2, and S100P) to classify the samples based on AUC using mRNA expression. The network classified tumor samples with an f1-score of 0.83 for the normal samples and 0.88 for the PDAC samples, with an average of 0.86. The PDAC-ANN could classify the test samples with a sensitivity of 87.6 and specificity of 83.1. CONCLUSION The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on RNA expression. This software can be useful in the PDAC diagnosis.
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Affiliation(s)
- Palloma Porto Almeida
- Núcleo de Biointegração, Instituto Multidisciplinar em Saúde, Universidade Federal da Bahia, Vitória da Conquista, Brazil
| | - Cristina Padre Cardoso
- Núcleo de Biointegração, Instituto Multidisciplinar em Saúde, Universidade Federal da Bahia, Vitória da Conquista, Brazil
- Faculdade Santo Agostinho, Vitória da Conquista, Brazil
| | - Leandro Martins de Freitas
- Núcleo de Biointegração, Instituto Multidisciplinar em Saúde, Universidade Federal da Bahia, Vitória da Conquista, Brazil.
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Galvez JM, Castillo-Secilla D, Herrera LJ, Valenzuela O, Caba O, Prados JC, Ortuno FM, Rojas I. Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets. IEEE J Biomed Health Inform 2019; 24:2119-2130. [PMID: 31871000 DOI: 10.1109/jbhi.2019.2953978] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Many clinical studies have revealed the high biological similarities existing among different skin pathological states. These similarities create difficulties in the efficient diagnosis of skin cancer, and encourage to study and design new intelligent clinical decision support systems. In this sense, gene expression analysis can help find differentially expressed genes (DEGs) simultaneously discerning multiple skin pathological states in a single test. The integration of multiple heterogeneous transcriptomic datasets requires different pipeline stages to be properly designed: from suitable batch merging and efficient biomarker selection to automated classification assessment. This article presents a novel approach addressing all these technical issues, with the intention of providing new sights about skin cancer diagnosis. Although new future efforts will have to be made in the search for better biomarkers recognizing specific skin pathological states, our study found a panel of 8 highly relevant multiclass DEGs for discerning up to 10 skin pathological states: 2 healthy skin conditions a priori, 2 cataloged precancerous skin diseases and 6 cancerous skin states. Their power of diagnosis over new samples was widely tested by previously well-trained classification models. Robust performance metrics such as overall and mean multiclass F1-score outperformed recognition rates of 94% and 80%, respectively. Clinicians should give special attention to highlighted multiclass DEGs that have high gene expression changes present among them, and understand their biological relationship to different skin pathological states.
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Lorenzon R, Mariotti-Ferrandiz E, Aheng C, Ribet C, Toumi F, Pitoiset F, Chaara W, Derian N, Johanet C, Drakos I, Harris S, Amselem S, Berenbaum F, Benveniste O, Bodaghi B, Cacoub P, Grateau G, Amouyal C, Hartemann A, Saadoun D, Sellam J, Seksik P, Sokol H, Salem JE, Vicaut E, Six A, Rosenzwajg M, Bernard C, Klatzmann D. Clinical and multi-omics cross-phenotyping of patients with autoimmune and autoinflammatory diseases: the observational TRANSIMMUNOM protocol. BMJ Open 2018; 8:e021037. [PMID: 30166293 PMCID: PMC6119447 DOI: 10.1136/bmjopen-2017-021037] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 07/02/2018] [Accepted: 07/17/2018] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Autoimmune and autoinflammatory diseases (AIDs) represent a socioeconomic burden as the second cause of chronic illness in Western countries. In this context, the TRANSIMMUNOM clinical protocol is designed to revisit the nosology of AIDs by combining basic, clinical and information sciences. Based on classical and systems biology analyses, it aims to uncover important phenotypes that cut across diagnostic groups so as to discover biomarkers and identify novel therapeutic targets. METHODS AND ANALYSIS TRANSIMMUNOM is an observational clinical protocol that aims to cross-phenotype a set of 19 AIDs, six related control diseases and healthy volunteers . We assembled a multidisciplinary cohort management team tasked with (1) selecting informative biological (routine and omics type) and clinical parameters to be captured, (2) standardising the sample collection and shipment circuit, (3) selecting omics technologies and benchmarking omics data providers, (4) designing and implementing a multidisease electronic case report form and an omics database and (5) implementing supervised and unsupervised data analyses. ETHICS AND DISSEMINATION The study was approved by the institutional review board of Pitié-Salpêtrière Hospital (ethics committee Ile-De-France 48-15) and done in accordance with the Declaration of Helsinki and good clinical practice. Written informed consent is obtained from all participants before enrolment in the study. TRANSIMMUNOM's project website provides information about the protocol (https://www.transimmunom.fr/en/) including experimental set-up and tool developments. Results will be disseminated during annual scientific committees appraising the project progresses and at national and international scientific conferences. DISCUSSION Systems biology approaches are increasingly implemented in human pathophysiology research. The TRANSIMMUNOM study applies such approach to the pathophysiology of AIDs. We believe that this translational systems immunology approach has the potential to provide breakthrough discoveries for better understanding and treatment of AIDs. TRIAL REGISTRATION NUMBER NCT02466217; Pre-results.
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Affiliation(s)
- Roberta Lorenzon
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | | | - Caroline Aheng
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Claire Ribet
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Ferial Toumi
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
| | - Fabien Pitoiset
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Wahiba Chaara
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Nicolas Derian
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Catherine Johanet
- Immunology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UFR 967, Sorbonne Université, Paris, France
| | - Iannis Drakos
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
| | - Sophie Harris
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
| | - Serge Amselem
- Laboratoire de génétique, UMR S933, Sorbonne Université, INSERM, Paris, France
| | - Francis Berenbaum
- Rheumatology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR S938, Sorbonne Université, INSERM, Paris, France
| | - Olivier Benveniste
- Internal Medicine and Clinical Immunology Department, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR 974, Sorbonne Université, Paris, France
| | - Bahram Bodaghi
- Département Hospitalo-Universitaire Vision and Handicaps ‘ViewMaintain’, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, Île-de-France, France
| | - Patrice Cacoub
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Internal Medicine and Clinical Immunology Department, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Gilles Grateau
- Internal Medicine Department, Tenon Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR S933, Sorbonne Université, INSERM, Paris, France
| | - Chloe Amouyal
- Diabetology Department, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, Île-de-France, France
| | - Agnes Hartemann
- Diabetology Department, Institute of Cardiometabolism and Nutrition (ICAN), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, Île-de-France, France
| | - David Saadoun
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Internal Medicine and Clinical Immunology Department, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Jeremie Sellam
- Rheumatology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- UMR S938, Sorbonne Université, INSERM, Paris, France
| | - Philippe Seksik
- Gastroenterology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- GRC-UPMC 03, Sorbonne Université, Paris, France
| | - Harry Sokol
- Gastroenterology Department, Saint-Antoine Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
- GRC-UPMC 03, Sorbonne Université, Paris, France
| | - Joe-Elie Salem
- CIC-1421, Pharmacology Department, INSERM, Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Eric Vicaut
- Unité de recherche clinique, UMR 942, Saint-Louis Lariboisière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Adrien Six
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Michelle Rosenzwajg
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Claude Bernard
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - David Klatzmann
- Immunology, Immunopathology, Immunotherapy (i3), Sorbonne Université, INSERM, Paris, France
- Biotherapy (CIC-BTi) and Inflammation, Immunopathology, Biotherapy Department (i2B), Pitié-Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France
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