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Quinney SK, Bies RR, Grannis SJ, Bartlett CW, Mendonca E, Rogerson CM, Backes CH, Shah DK, Tillman EM, Costantine MM, Aruldhas BW, Allam R, Grant A, Abbasi MY, Kandasamy M, Zang Y, Wang L, Shendre A, Li L. The MPRINT Hub Data, Model, Knowledge and Research Coordination Center: Bridging the gap in maternal-pediatric therapeutics research through data integration and pharmacometrics. Pharmacotherapy 2023; 43:391-402. [PMID: 36625779 PMCID: PMC10192201 DOI: 10.1002/phar.2765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 11/13/2022] [Accepted: 12/08/2022] [Indexed: 01/11/2023]
Abstract
Maternal and pediatric populations have historically been considered "therapeutic orphans" due to their limited inclusion in clinical trials. Physiologic changes during pregnancy and lactation and growth and maturation of children alter pharmacokinetics (PK) and pharmacodynamics (PD) of drugs. Precision therapy in these populations requires knowledge of these effects. Efforts to enhance maternal and pediatric participation in clinical studies have increased over the past few decades. However, studies supporting precision therapeutics in these populations are often small and, in isolation, may have limited impact. Integration of data from various studies, for example through physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling or bioinformatics approaches, can augment the value of data from these studies, and help identify gaps in understanding. To catalyze research in maternal and pediatric precision therapeutics, the Obstetric and Pediatric Pharmacology and Therapeutics Branch of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) established the Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub. Herein, we provide an overview of the status of maternal-pediatric therapeutics research and introduce the Indiana University-Ohio State University MPRINT Hub Data, Model, Knowledge and Research Coordination Center (DMKRCC), which aims to facilitate research in maternal and pediatric precision therapeutics through the integration and assessment of existing knowledge, supporting pharmacometrics and clinical trials design, development of new real-world evidence resources, educational initiatives, and building collaborations among public and private partners, including other NICHD-funded networks. By fostering use of existing data and resources, the DMKRCC will identify critical gaps in knowledge and support efforts to overcome these gaps to enhance maternal-pediatric precision therapeutics.
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Affiliation(s)
- Sara K Quinney
- Department of Obstetrics and Gynecology, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Robert R Bies
- Department of Pharmaceutical Sciences, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
- Institute for Computational and Data Sciences, University at Buffalo, State University of New York at Buffalo, Buffalo, New York, USA
| | - Shaun J Grannis
- Department of Family Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Center for Biomedical Informatics, Regenstrief Institute, Indianapolis, Indiana, USA
- Richard M. Fairbanks School of Public Health, Indiana University, Indianapolis, Indiana, USA
| | - Christopher W Bartlett
- The Steve & Cindy Rasmussen Institute for Genomic Medicine, Battelle Center for Computational Biology, Abigail Wexner Research Institute at Nationwide Children’s Hospital, Columbus, Ohio, USA
- Department of Pediatrics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Eneida Mendonca
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA
| | - Colin M Rogerson
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Carl H Backes
- Division of Neonatology, Nationwide Children’s Hospital; Departments of Pediatrics and Obstetrics and Gynecology, The Ohio State University College of Medicine; Center for Perinatal Research and The Ohio Perinatal Research Network, The Abigail Wexner Research Institute at Nationwide Children’s Hospital, USA; The Heart Center at Nationwide Children’s Hospital, Columbus, Ohio, USA
| | - Dhaval K Shah
- Department of Pharmaceutical Sciences, University at Buffalo School of Pharmacy and Pharmaceutical Sciences, State University of New York at Buffalo, Buffalo, New York, USA
| | - Emma M Tillman
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Maged M Costantine
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, The Ohio State University, Columbus, Ohio, USA
| | - Blessed W Aruldhas
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
- Department of Pharmacology and Clinical Pharmacology, Christian Medical College, Vellore, India
| | - Reva Allam
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Amelia Grant
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Mohammed Yaseen Abbasi
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Murugesh Kandasamy
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Yong Zang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lei Wang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Aditi Shendre
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
| | - Lang Li
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, Ohio, USA
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Ou-Yang L, Cai D, Zhang XF, Yan H. WDNE: an integrative graphical model for inferring differential networks from multi-platform gene expression data with missing values. Brief Bioinform 2021; 22:6272792. [PMID: 33975339 DOI: 10.1093/bib/bbab086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 02/14/2021] [Accepted: 02/23/2021] [Indexed: 11/14/2022] Open
Abstract
The mechanisms controlling biological process, such as the development of disease or cell differentiation, can be investigated by examining changes in the networks of gene dependencies between states in the process. High-throughput experimental methods, like microarray and RNA sequencing, have been widely used to gather gene expression data, which paves the way to infer gene dependencies based on computational methods. However, most differential network analysis methods are designed to deal with fully observed data, but missing values, such as the dropout events in single-cell RNA-sequencing data, are frequent. New methods are needed to take account of these missing values. Moreover, since the changes of gene dependencies may be driven by certain perturbed genes, considering the changes in gene expression levels may promote the identification of gene network rewiring. In this study, a novel weighted differential network estimation (WDNE) model is proposed to handle multi-platform gene expression data with missing values and take account of changes in gene expression levels. Simulation studies demonstrate that WDNE outperforms state-of-the-art differential network estimation methods. When applied WDNE to infer differential gene networks associated with drug resistance in ovarian tumors, cell differentiation and breast tumor heterogeneity, the hub genes in the estimated differential gene networks can provide important insights into the underlying mechanisms. Furthermore, a Matlab toolbox, differential network analysis toolbox, was developed to implement the WDNE model and visualize the estimated differential networks.
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Affiliation(s)
- Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Media Security, and Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ), College of Electronics and Information Engineering, Shenzhen University, Shenzhen, 518060, China
| | - Dehan Cai
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, 999077, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China
| | - Hong Yan
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong, 999077, China
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Wang H, Liu W, Yu B, Yu X, Chen B. Identification of Key Modules and Hub Genes of Annulus Fibrosus in Intervertebral Disc Degeneration. Front Genet 2021; 11:596174. [PMID: 33584795 PMCID: PMC7875098 DOI: 10.3389/fgene.2020.596174] [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: 08/18/2020] [Accepted: 12/17/2020] [Indexed: 11/24/2022] Open
Abstract
Background: Intervertebral disc degeneration impairs the quality of patients lives. Even though there has been development of many therapeutic strategies, most of them remain unsatisfactory due to the limited understanding of the mechanisms that underlie the intervertebral disc degeneration. Questions/purposes: This study is meant to identify the key modules and hub genes related to the annulus fibrosus in intervertebral disc degeneration (IDD) through: (1) constructing a weighted gene co-expression network; (2) identifying key modules and hub genes; (3) verifying the relationships of key modules and hub genes with IDD; and (4) confirming the expression pattern of hub genes in clinical samples. Methods: The Gene Expression Omnibus provided 24 sets of annulus fibrosus microarray data. Differentially expressed genes between the annulus fibrosus of degenerative and non-degenerative intervertebral disc samples have gone through the Gene Ontology (GO) and pathway analysis. The construction of a gene network and classification of genes into different modules were conducted through performing Weighted Gene Co-expression Network Analysis. The identification of modules and hub genes that were most related to intervertebral disc degeneration was proceeded. In order to verify the relationships of the module and hub genes with intervertebral disc degeneration, Ingenuity Pathway Analysis was operated. Clinical samples were adopted to help verify the hub gene expression profile. Results: One thousand one hundred ninety differentially expressed genes were identified. Terms and pathways associated with intervertebral disc degeneration were presented by GO and pathway analysis. The construction of a Weighted Gene Coexpression Network was completed and clustering differentially expressed genes into four modules was also achieved. The module with the lowest P-value and the highest absolute correlation coefficient was selected and its relationship with intervertebral disc degeneration was confirmed by Ingenuity Pathway Analysis. The identification of hub genes and the confirmation of their expression profile were also realized. Conclusions: This study generated a comprehensive overview of the gene networks underlying annulus fibrosus in intervertebral disc degeneration. Clinical Relevance: Modules and hub genes identified in this study are highly associated with intervertebral disc degeneration, and may serve as potential therapeutic targets for intervertebral disc degeneration.
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Affiliation(s)
- Hantao Wang
- Department of Spine Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
- Department of Orthopedics, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wenhui Liu
- Plastic & Reconstructive Surgery of the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bo Yu
- Department of Medicine, Lincoln Medical Center, Bronx, NY, United States
| | - Xiaosheng Yu
- Department of Spine Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Bin Chen
- Department of Spine Surgery, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, Shanghai, China
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Drake J, McMichael GO, Vornholt ES, Cresswell K, Williamson V, Chatzinakos C, Mamdani M, Hariharan S, Kendler KS, Kalsi G, Riley BP, Dozmorov M, Miles MF, Bacanu S, Vladimirov VI. Assessing the Role of Long Noncoding RNA in Nucleus Accumbens in Subjects With Alcohol Dependence. Alcohol Clin Exp Res 2020; 44:2468-2480. [PMID: 33067813 PMCID: PMC7756309 DOI: 10.1111/acer.14479] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 10/01/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Long noncoding RNA (lncRNA) have been implicated in the etiology of alcohol use. Since lncRNA provide another layer of complexity to the transcriptome, assessing their expression in the brain is the first critical step toward understanding lncRNA functions in alcohol use and addiction. Thus, we sought to profile lncRNA expression in the nucleus accumbens (NAc) in a large postmortem alcohol brain sample. METHODS LncRNA and protein-coding gene (PCG) expressions in the NAc from 41 subjects with alcohol dependence (AD) and 41 controls were assessed via a regression model. Weighted gene coexpression network analysis was used to identify lncRNA and PCG networks (i.e., modules) significantly correlated with AD. Within the significant modules, key network genes (i.e., hubs) were also identified. The lncRNA and PCG hubs were correlated via Pearson correlations to elucidate the potential biological functions of lncRNA. The lncRNA and PCG hubs were further integrated with GWAS data to identify expression quantitative trait loci (eQTL). RESULTS At Bonferroni adj. p-value ≤ 0.05, we identified 19 lncRNA and 5 PCG significant modules, which were enriched for neuronal and immune-related processes. In these modules, we further identified 86 and 315 PCG and lncRNA hubs, respectively. At false discovery rate (FDR) of 10%, the correlation analyses between the lncRNA and PCG hubs revealed 3,125 positive and 1,860 negative correlations. Integration of hubs with genotype data identified 243 eQTLs affecting the expression of 39 and 204 PCG and lncRNA hubs, respectively. CONCLUSIONS Our study identified lncRNA and gene networks significantly associated with AD in the NAc, coordinated lncRNA and mRNA coexpression changes, highlighting potentially regulatory functions for the lncRNA, and our genetic (cis-eQTL) analysis provides novel insights into the etiological mechanisms of AD.
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Affiliation(s)
- John Drake
- From the Center for Integrative Life Sciences Education (JD)Virginia Commonwealth UniversityRichmondVirginia
| | - Gowon O. McMichael
- Virginia Institute for Psychiatric and Behavioral Genetics(GOM, ESV, CC, MM, KSK, BPR, MFM, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
| | - Eric Sean Vornholt
- Virginia Institute for Psychiatric and Behavioral Genetics(GOM, ESV, CC, MM, KSK, BPR, MFM, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
| | - Kellen Cresswell
- Department of Biostatistics(KC, MD)Virginia Commonwealth UniversityRichmondVirginia
| | - Vernell Williamson
- Department of Pathology(VW)Virginia Commonwealth UniversityRichmondVirginia
| | - Chris Chatzinakos
- Virginia Institute for Psychiatric and Behavioral Genetics(GOM, ESV, CC, MM, KSK, BPR, MFM, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
| | - Mohammed Mamdani
- Virginia Institute for Psychiatric and Behavioral Genetics(GOM, ESV, CC, MM, KSK, BPR, MFM, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
| | - Siddharth Hariharan
- Summer Research Fellowship(SH)School of MedicineVirginia Commonwealth UniversityRichmondVirginia
| | - Kenneth S. Kendler
- Virginia Institute for Psychiatric and Behavioral Genetics(GOM, ESV, CC, MM, KSK, BPR, MFM, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
- Department of Psychiatry(KSK, BPR, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
- Department of Human and Molecular Genetics(KSK, BPR)Virginia Commonwealth UniversityRichmondVirginia
| | - Gursharan Kalsi
- Department of Social, Genetic and Developmental Psychiatry(GK)Institute of PsychiatryLondonUK
| | - Brien P. Riley
- Virginia Institute for Psychiatric and Behavioral Genetics(GOM, ESV, CC, MM, KSK, BPR, MFM, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
- Department of Psychiatry(KSK, BPR, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
- Department of Human and Molecular Genetics(KSK, BPR)Virginia Commonwealth UniversityRichmondVirginia
| | - Mikhail Dozmorov
- Department of Biostatistics(KC, MD)Virginia Commonwealth UniversityRichmondVirginia
| | - Michael F. Miles
- Virginia Institute for Psychiatric and Behavioral Genetics(GOM, ESV, CC, MM, KSK, BPR, MFM, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
- Department of Pharmacology and Toxicology(MFM)Virginia Commonwealth UniversityRichmondVirginia
| | - Silviu‐Alin Bacanu
- Virginia Institute for Psychiatric and Behavioral Genetics(GOM, ESV, CC, MM, KSK, BPR, MFM, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
- Department of Psychiatry(KSK, BPR, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
| | - Vladimir I. Vladimirov
- Virginia Institute for Psychiatric and Behavioral Genetics(GOM, ESV, CC, MM, KSK, BPR, MFM, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
- Department of Psychiatry(KSK, BPR, S‐AB, VIV)Virginia Commonwealth UniversityRichmondVirginia
- Center for Biomarker Research and Personalized Medicine(VIV)Virginia Commonwealth UniversityRichmondVirginia
- Lieber Institute for Brain Development(VIV)Johns Hopkins UniversityBaltimoreMaryland
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Xing J, Shi Q, Zhao J, Yu Z. Identifying drug candidates for hepatocellular carcinoma based on differentially expressed genes. Am J Transl Res 2020; 12:2664-2674. [PMID: 32655798 PMCID: PMC7344051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Accepted: 05/23/2020] [Indexed: 06/11/2023]
Abstract
The prognosis for patients with advanced hepatocellular carcinoma (HCC) is extremely poor, mainly due to rapid progression and a paucity of effective drugs. Genome-wide analysis allows for potential drugs to be explored based on differentially expressed genes (DEGs). However, drug candidates and DEGs in HCC are largely unknown. In this study, we investigated DEGs and prognostication using The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), the Gene Expression Omnibus (GEO), and immunohistochemical staining. Protein-protein interaction networks between DEGs were also analyzed to clarify 12 hub genes and query online databases for potential HCC therapeutic drugs. We found that 885 of 3219 DEGs from a TCGA dataset were associated with prognosis. We clarified 12 hub genes that were overexpressed in tumor samples and significantly associated with poor overall survival (OS) in HCC patients. These findings were validated using GEO and ICGC cohorts. Moreover, promising drug candidates targeted against HCC were predicted using online databases. Collectively, the upregulation of 12 hub genes was associated with poor prognosis for patients with HCC, and focusing on their expression may advance efforts towards targeted HCC therapies.
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Affiliation(s)
- Jiyuan Xing
- Gene Hospital of Henan Province, Precision Medicine Center, The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052, Henan, China
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052, Henan, China
| | - Qingmiao Shi
- Gene Hospital of Henan Province, Precision Medicine Center, The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052, Henan, China
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052, Henan, China
| | - Junjie Zhao
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052, Henan, China
| | - Zujiang Yu
- Gene Hospital of Henan Province, Precision Medicine Center, The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052, Henan, China
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052, Henan, China
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Gene expression network analysis of lymph node involvement in colon cancer identifies AHSA2, CDK10, and CWC22 as possible prognostic markers. Sci Rep 2020; 10:7170. [PMID: 32345988 PMCID: PMC7189385 DOI: 10.1038/s41598-020-63806-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2019] [Accepted: 03/24/2020] [Indexed: 01/28/2023] Open
Abstract
Colon cancer has been well studied using a variety of molecular techniques, including whole genome sequencing. However, genetic markers that could be used to predict lymph node (LN) involvement, which is the most important prognostic factor for colon cancer, have not been identified. In the present study, we compared LN(+) and LN(−) colon cancer patients using differential gene expression and network analysis. Colon cancer gene expression data were obtained from the Cancer Genome Atlas and divided into two groups, LN(+) and LN(−). Gene expression networks were constructed using LASSO (Least Absolute Shrinkage and Selection Operator) regression. We identified hub genes, such as APBB1, AHSA2, ZNF767, and JAK2, that were highly differentially expressed. Survival analysis using selected hub genes, such as AHSA2, CDK10, and CWC22, showed that their expression levels were significantly associated with the survival rate of colon cancer patients, which indicates their possible use as prognostic markers. In addition, protein-protein interaction network, GO enrichment, and KEGG pathway analysis were performed with selected hub genes from each group to investigate the regulatory relationships between hub genes and LN involvement in colon cancer; these analyses revealed differences between the LN(−) and LN(+) groups. Our network analysis may help narrow down the search for novel candidate genes for the treatment of colon cancer, in addition to improving our understanding of the biological processes underlying LN involvement. All R implementation codes are available at journal website as Supplementary Materials.
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Shah SD, Braun R. GeneSurrounder: network-based identification of disease genes in expression data. BMC Bioinformatics 2019; 20:229. [PMID: 31060502 PMCID: PMC6503437 DOI: 10.1186/s12859-019-2829-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 04/17/2019] [Indexed: 11/24/2022] Open
Abstract
Background A key challenge of identifying disease–associated genes is analyzing transcriptomic data in the context of regulatory networks that control cellular processes in order to capture multi-gene interactions and yield mechanistically interpretable results. One existing category of analysis techniques identifies groups of related genes using interaction networks, but these gene sets often comprise tens or hundreds of genes, making experimental follow-up challenging. A more recent category of methods identifies precise gene targets while incorporating systems-level information, but these techniques do not determine whether a gene is a driving source of changes in its network, an important characteristic when looking for potential drug targets. Results We introduce GeneSurrounder, an analysis method that integrates expression data and network information in a novel procedure to detect genes that are sources of dysregulation on the network. The key idea of our method is to score genes based on the evidence that they influence the dysregulation of their neighbors on the network in a manner that impacts cell function. Applying GeneSurrounder to real expression data, we show that our method is able to identify biologically relevant genes, integrate pathway and expression data, and yield more reproducible results across multiple studies of the same phenotype than competing methods. Conclusions Together these findings suggest that GeneSurrounder provides a new avenue for identifying individual genes that can be targeted therapeutically. The key innovation of GeneSurrounder is the combination of pathway network information with gene expression data to determine the degree to which a gene is a source of dysregulation on the network. By prioritizing genes in this way, our method provides insights into disease mechanisms and suggests diagnostic and therapeutic targets. Our method can be used to help biologists select among tens or hundreds of genes for further validation. The implementation in R is available at github.com/sahildshah1/gene-surrounder. Electronic supplementary material The online version of this article (10.1186/s12859-019-2829-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sahil D Shah
- Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, USA
| | - Rosemary Braun
- Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, USA. .,Biostatistics, Feinberg School of Medicine, Chicago, USA. .,Northwestern Institute on Complex Systems, Northwestern University, Evanston, USA.
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8
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Mao Q, Zhang L, Zhang Y, Dong G, Yang Y, Xia W, Chen B, Ma W, Hu J, Jiang F, Xu L. A network-based signature to predict the survival of non-smoking lung adenocarcinoma. Cancer Manag Res 2018; 10:2683-2693. [PMID: 30147367 PMCID: PMC6101016 DOI: 10.2147/cmar.s163918] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Background A substantial increase in the number of non-smoking lung adenocarcinoma (LAC) patients has been drawing extensive attention in the past decade. However, effective biomarkers, which could guide the precise treatment, are still limited for identifying high-risk patients. Here, we provide a network-based signature to predict the survival of non-smoking LAC. Materials and methods Gene expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Significant gene co-expression networks and hub genes were identified by Weighted Gene Co-expression Network Analysis. Potential mechanisms and pathways of co-expression networks were analyzed by Gene Ontology. The predictive signature was constructed by penalized Cox regression analysis and tested in two independent datasets. Results Two distinct co-expression modules were significantly correlated with the non-smoking status across 4 Gene Expression Omnibus datasets. Gene Ontology revealed that nuclear division and cell cycle pathways were main mechanisms of the blue module and that genes in the turquoise module were involved in lymphocyte activation and cell adhesion pathways. Seventeen genes were selected from hub genes at an optimal lambda value and built the prognostic signature. The prognostic signature distinguished the survival of non-smoking LAC (training: hazard ratio [HR]=3.696, 95% CI: 2.025–6.748, P<0.001; testing: HR=2.9, 95% CI: 1.322–6.789, P=0.006; HR=2.78, 95% CI: 1.658–6.654, P=0.022) and had moderate predictive abilities in the training and validation datasets. Conclusion The prognostic signature is a promising predictor of non-smoking LAC patients, which might benefit clinical practice and precision therapeutic management.
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Affiliation(s)
- Qixing Mao
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China, , .,The Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing, China, , .,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Louqian Zhang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China, , .,The Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing, China, ,
| | - Yi Zhang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China, ,
| | - Gaochao Dong
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China, , .,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing, China, ,
| | - Yao Yang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wenjie Xia
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China, , .,The Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing, China, , .,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bing Chen
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China, , .,The Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing, China, ,
| | - Weidong Ma
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China, , .,The Fourth Clinical College of Nanjing Medical University, Nanjing, China.,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing, China, ,
| | - Jianzhong Hu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Feng Jiang
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China, , .,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing, China, ,
| | - Lin Xu
- Department of Thoracic Surgery, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Nanjing, China, , .,Jiangsu Key Laboratory of Molecular and Translational Cancer Research, Nanjing Medical University Affiliated Cancer Hospital, Cancer Institute of Jiangsu Province, Nanjing, China, ,
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He X, Zhang C, Shi C, Lu Q. Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers. Oncol Rep 2018; 39:929-938. [PMID: 29328493 PMCID: PMC5802042 DOI: 10.3892/or.2018.6197] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2017] [Accepted: 12/29/2017] [Indexed: 11/24/2022] Open
Abstract
Compared to other types of lung cancer, lung adenocarcinoma patients with a history of smoking have a poor prognosis during the treatment of lung cancer. How lung adenocarcinoma-related genes are differentially expressed between smoker and non-smoker patients has yet to be fully elucidated. We performed a meta-analysis of four publicly available microarray datasets related to lung adenocarcinoma tissue in patients with a history of smoking using R statistical software. The top 50 differentially expressed genes (DEGs) in smoking vs. non‑smoking patients are shown using heat maps. Additionally, we conducted KEGG and GO analyses. In addition, we performed a PPI network analysis for 8 genes that were selected during a previous analysis. We identified a total of 2,932 DEGs (1,806 upregulated, 1,126 downregulated) and five genes (CDC45, CDC20, ANAPC7, CDC6, ESPL1) that may link lung adenocarcinoma to smoking history. Our study may provide new insights into the complex mechanisms of lung adenocarcinoma in smoking patients, and our novel gene expression signatures will be useful for future clinical studies.
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Affiliation(s)
- Xiaona He
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Cheng Zhang
- Center for Experimental Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Chao Shi
- Center for Experimental Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, P.R. China
| | - Quqin Lu
- Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, Jiangxi 330006, P.R. China
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Zhao R, Wang Y, Zhang M, Gu X, Wang W, Tan J, Wei X, Jin N. Screening of potential therapy targets for prostate cancer using integrated analysis of two gene expression profiles. Oncol Lett 2017; 14:5361-5369. [PMID: 29113170 PMCID: PMC5662906 DOI: 10.3892/ol.2017.6879] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 05/23/2017] [Indexed: 12/14/2022] Open
Abstract
The aim of the present study was to analyze potential therapy targets for prostate cancer using integrated analysis of two gene expression profiles. First, gene expression profiles GSE38241 and GSE3933 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between prostate cancer and normal control samples were identified using the Linear Models for Microarray Data package. Pathway enrichment analysis of DEGs was performed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Furthermore, protein-protein interaction (PPI) networks of DEGs were constructed, on the basis of the Search Tool for the Retrieval of Interacting Genes/Proteins database. The Molecular Complex Detection was utilized to perform module analysis of the PPI networks. In addition, transcriptional regulatory networks were constructed on the basis of the associations between transcription factors (TFs) and target genes. A total of 529 DEGs were identified, including 129 upregulated genes that were primarily associated with to the cell cycle. Additionally, 400 downregulated genes were identified, which were principally enriched in the pathways associated with vascular smooth muscle contraction and focal adhesion. Cell Division Cycle Associated 8, Cell Division Cycle 45, Ubiquitin Conjugating Enzyme E2 C and Thymidine Kinase 1 were identified as hub genes in the upregulated sub-network. Furthermore, the upregulated TF E2F, and the downregulated TF Early Growth Response 1, were identified to be critical in the transcriptional regulatory networks. The identified DEGs and TFs may have critical roles in the progression of prostate cancer, and may be used as target molecules for treating prostate cancer.
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Affiliation(s)
- Rui Zhao
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
| | - Yao Wang
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
| | - Muchun Zhang
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
| | - Xinquan Gu
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
| | - Weihua Wang
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
| | - Jiufeng Tan
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
| | - Xin Wei
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
| | - Ning Jin
- Department of Urology, China-Japan Union Hospital of Jilin University, Changchun, Jilin 130033, P.R. China
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Identification of Key Modules and Hub Genes of Keloids with Weighted Gene Coexpression Network Analysis. Plast Reconstr Surg 2017; 139:376-390. [PMID: 28121871 DOI: 10.1097/prs.0000000000003014] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
BACKGROUND Keloid scarring impairs patients' quality of life, and although many therapeutic strategies have been developed, most remain unsatisfactory because of limited understanding of the mechanisms underlying keloid development. METHODS A microarray gene expression data set from keloid tissue was acquired from the Gene Expression Omnibus. Differentially expressed genes in fibroblasts and keratinocytes underwent functional annotation and pathway analysis. Weighted gene coexpression network analysis was applied to identify the gene targets of keloid scars within differentially expressed genes. Modules and hub genes for keloids were identified. Enrichment analysis was undertaken to verify the modules' and hub genes' relationship with keloids. RESULTS Enrichment analysis and pathway analysis showed gene ontology terms and pathways related to keloids. Each cell type generated three modules in weighted gene coexpression network analysis, with one module most related to keloids. Enrichment analysis showed that the modules concerned are enriched with terms related to keloids. Three hub genes were selected for fibroblasts and keratinocytes, and their relationship to keloids was verified. Immunohistochemical staining verified expression change of some hub genes. CONCLUSIONS This is the first study to describe the gene networks underlying keloids. Modules and hub genes generated in the present study are highly related to keloids and may identify novel therapeutic targets for treatment of keloids. CLINICAL QUESTION/LEVEL OF EVIDENCE Therapeutic, V.
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Chen J, Yang HT, Li Z, Xu N, Yu B, Xu JP, Zhao PG, Wang Y, Zhang XJ, Lin DJ. Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm. Oncol Lett 2016; 12:1792-1800. [PMID: 27588126 PMCID: PMC4998145 DOI: 10.3892/ol.2016.4822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 12/01/2015] [Indexed: 12/14/2022] Open
Abstract
Studies that only assess differentially-expressed (DE) genes do not contain the information required to investigate the mechanisms of diseases. A complete knowledge of all the direct and indirect interactions between proteins may act as a significant benchmark in the process of forming a comprehensive description of cellular mechanisms and functions. The results of protein interaction network studies are often inconsistent and are based on various methods. In the present study, a combined network was constructed using selected gene pairs, following the conversion and combination of the scores of gene pairs that were obtained across multiple approaches by a novel algorithm. Samples from patients with and without lung adenocarcinoma were compared, and the RankProd package was used to identify DE genes. The empirical Bayesian (EB) meta-analysis approach, the search tool for the retrieval of interacting genes/proteins database (STRING), the weighted gene coexpression network analysis (WGCNA) package and the differentially-coexpressed genes and links package (DCGL) were used for network construction. A combined network was also constructed with a novel rank-based algorithm using a combined score. The topological features of the 5 networks were analyzed and compared. A total of 941 DE genes were screened. The topological analysis indicated that the gene interaction network constructed using the WGCNA method was more likely to produce a small-world property, which has a small average shortest path length and a large clustering coefficient, whereas the combined network was confirmed to be a scale-free network. Gene pairs that were identified using the novel combined method were mostly enriched in the cell cycle and p53 signaling pathway. The present study provided a novel perspective to the network-based analysis. Each method has advantages and disadvantages. Compared with single methods, the combined algorithm used in the present study may provide a novel method to analyze gene interactions, with increased credibility.
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Affiliation(s)
- Juan Chen
- Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250014, P.R. China; Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Hai-Tao Yang
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Zhu Li
- Department of Hepatobiliary Surgery, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Ning Xu
- Department of Respiratory Medicine, Weihai Municipal Hospital, Weihai, Shandong 264200, P.R. China
| | - Bo Yu
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Jun-Ping Xu
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Pei-Ge Zhao
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Yan Wang
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Xiu-Juan Zhang
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Dian-Jie Lin
- Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250014, P.R. China
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Sedaghat N, Saegusa T, Randolph T, Shojaie A. Comparative study of computational methods for reconstructing genetic networks of cancer-related pathways. Cancer Inform 2014; 13:55-66. [PMID: 25288880 PMCID: PMC4179645 DOI: 10.4137/cin.s13781] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2014] [Revised: 05/08/2014] [Accepted: 05/10/2014] [Indexed: 12/16/2022] Open
Abstract
Network reconstruction is an important yet challenging task in systems biology. While many methods have been recently proposed for reconstructing biological networks from diverse data types, properties of estimated networks and differences between reconstruction methods are not well understood. In this paper, we conduct a comprehensive empirical evaluation of seven existing network reconstruction methods, by comparing the estimated networks with different sparsity levels for both normal and tumor samples. The results suggest substantial heterogeneity in networks reconstructed using different reconstruction methods. Our findings also provide evidence for significant differences between networks of normal and tumor samples, even after accounting for the considerable variability in structures of networks estimated using different reconstruction methods. These differences can offer new insight into changes in mechanisms of genetic interaction associated with cancer initiation and progression.
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Affiliation(s)
- Nafiseh Sedaghat
- Computer Engineering Department, Iran University of Science and Technology, Tehran, Iran
| | - Takumi Saegusa
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Timothy Randolph
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
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