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García-Abadillo J, Barba P, Carvalho T, Sosa-Zuñiga V, Lozano R, Carvalho HF, Garcia-Rojas M, Salazar E, y Sánchez JI. Dissecting the complex genetic basis of pre- and post-harvest traits in Vitis vinifera L. using genome-wide association studies. HORTICULTURE RESEARCH 2024; 11:uhad283. [PMID: 38487297 PMCID: PMC10939405 DOI: 10.1093/hr/uhad283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 12/17/2023] [Indexed: 03/17/2024]
Abstract
Addressing the pressing challenges in agriculture necessitates swift advancements in breeding programs, particularly for perennial crops like grapevines. Moving beyond the traditional biparental quantitative trait loci (QTL) mapping, we conducted a genome-wide association study (GWAS) encompassing 588 Vitis vinifera L. cultivars from a Chilean breeding program, spanning three seasons and testing 13 key yield-related traits. A strong candidate gene, Vitvi11g000454, located on chromosome 11 and related to plant response to biotic and abiotic stresses through jasmonic acid signaling, was associated with berry width and holds potential for enhancing berry size in grape breeding. We also mapped novel QTL associated with post-harvest traits across chromosomes 2, 4, 9, 11, 15, 18, and 19, broadening our grasp on the genetic intricacies dictating fruit post-harvest behavior, including decay, shriveling, and weight loss. Leveraging gene ontology annotations, we drew parallels between traits and scrutinized candidate genes, laying a robust groundwork for future trait-feature identification endeavors in plant breeding. We also highlighted the importance of carefully considering the choice of the response variable in GWAS analyses, as the use of best linear unbiased estimators (BLUEs) corrections in our study may have led to the suppression of some common QTL in grapevine traits. Our results underscore the imperative of pioneering non-destructive evaluation techniques for long-term conservation traits, offering grape breeders and cultivators insights to improve post-harvest table grape quality and minimize waste.
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Affiliation(s)
- Julian García-Abadillo
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo - Pozuelo de Alarcón, 28223, Madrid, Spain
| | - Paola Barba
- Genetic Resources Unit and Germplasm Bank, La Platina, Instituto de Investigaciones Agropecuarias, Av Santa Rosa 11610, La pintana, Santiago, Chile
- Sun World International, 28994 Gromer Av, Wasco, 93280, California, USA
| | | | - Viviana Sosa-Zuñiga
- Instituto de Ciencias Químicas y Aplicadas (ICQA), Universidad Autónoma de Chile, El Llano Subercaseaux 2801, Santiago, Chile
| | | | - Humberto Fanelli Carvalho
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo - Pozuelo de Alarcón, 28223, Madrid, Spain
| | - Miguel Garcia-Rojas
- Genetic Resources Unit and Germplasm Bank, La Platina, Instituto de Investigaciones Agropecuarias, Av Santa Rosa 11610, La pintana, Santiago, Chile
| | - Erika Salazar
- Genetic Resources Unit and Germplasm Bank, La Platina, Instituto de Investigaciones Agropecuarias, Av Santa Rosa 11610, La pintana, Santiago, Chile
| | - Julio Isidro y Sánchez
- Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo - Pozuelo de Alarcón, 28223, Madrid, Spain
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Chen X, Xia Z, Wan Y, Huang P. Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis. Medicine (Baltimore) 2021; 100:e27117. [PMID: 34596112 PMCID: PMC8483840 DOI: 10.1097/md.0000000000027117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Accepted: 08/14/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven genes and potential drugs in HCC. METHODS Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein-protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC. RESULTS A total of 197 DEGs were identified. The protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC. CONCLUSION Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.
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Affiliation(s)
- Xiaolong Chen
- National Key Clinical Department, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhixiong Xia
- Department of Pathology, The Center Hospital of Wuhan, Hubei, China
| | - Yafeng Wan
- Department of Hepatobiliary Surgery, Daping Hospital, Army Medical University, Chongqing, China
| | - Ping Huang
- National Key Clinical Department, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Guo XY, Zhong S, Wang ZN, Xie T, Duan H, Zhang JY, Zhang GH, Liang L, Cui R, Hu HR, Lu J, Wu Y, Dong JJ, He ZQ, Mou YG. Immunogenomic Profiling Demonstrate AC003092.1 as an Immune-Related eRNA in Glioblastoma Multiforme. Front Genet 2021; 12:633812. [PMID: 33815468 PMCID: PMC8012670 DOI: 10.3389/fgene.2021.633812] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 02/16/2021] [Indexed: 12/20/2022] Open
Abstract
Enhancer RNAs, a type of long non-coding RNAs (lncRNAs), play a critical role in the occurrence and development of glioma. RNA-seq data from 161 glioblastoma multiforme (GBM) samples were acquired from The Cancer Genome Atlas database. Then, 70 eRNAs were identified as prognosis-related genes, which had significant relations with overall survival (log-rank test, p < 0.05). AC003092.1 was demonstrated as an immune-related eRNA by functional enrichment analysis. We divided samples into two groups based on AC003092.1 expression: AC003092.1 High (AC003092.1_H) and AC003092.1 Low (AC003092.1_L) and systematically analyzed the influence of AC003092.1 on the immune microenvironment by single-sample gene-set enrichment analysis and CIBERSORTx. We quantified AC003092.1 and TFPI2 levels in 11 high-grade gliomas, 5 low-grade gliomas, and 7 GBM cell lines. Our study indicates that AC003092.1 is related to glioma-immunosuppressive microenvironment, and these results offer innovative sights into GBM immune therapy.
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Affiliation(s)
- Xiao-Yu Guo
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
| | - Sheng Zhong
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
| | - Zhen-Ning Wang
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
| | - Tian Xie
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
| | - Hao Duan
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
| | - Jia-Yu Zhang
- The First Clinical Medical College of Yunnan University of Traditional Chinese Medicine, Kunming, China
| | - Guan-Hua Zhang
- Department of Cerebrovascular Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Lun Liang
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
| | - Run Cui
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
| | - Hong-Rong Hu
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
| | - Jie Lu
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
| | - Yi Wu
- Department of Neurosurgy, Jiangmen Central Hospital, Jiangmen, China
| | - Jia-Jun Dong
- Department of Neurosurgy, Jiangmen Central Hospital, Jiangmen, China
| | - Zhen-Qiang He
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
| | - Yong-Gao Mou
- Department of Neurosurgery/Neuro-oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer, Guangzhou, China
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Guo XY, Zhang GH, Wang ZN, Duan H, Xie T, Liang L, Cui R, Hu HR, Wu Y, Dong JJ, He ZQ, Mou YG. A novel Foxp3-related immune prognostic signature for glioblastoma multiforme based on immunogenomic profiling. Aging (Albany NY) 2021; 13:3501-3517. [PMID: 33429364 PMCID: PMC7906197 DOI: 10.18632/aging.202282] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 10/31/2020] [Indexed: 01/01/2023]
Abstract
Foxp3+ regulatory T cells (Treg) play an important part in the glioma immunosuppressive microenvironment. This study analyzed the effect of Foxsp3 on the immune microenvironment and constructed a Foxp3-related immune prognostic signature (IPS)for predicting prognosis in glioblastoma multiforme (GBM). Immunohistochemistry (IHC) staining for Foxp3 was performed in 72 high-grade glioma specimens. RNA-seq data from 152 GBM samples were obtained from The Cancer Genome Atlas database (TCGA) and divided into two groups, Foxp3 High (Foxp3_H) and Foxp3 Low (Foxp3_L), based on Foxp3 expression. We systematically analyzed the influence of Foxp3 on the immune microenvironment. Least Absolute Shrinkage and Selection Operator (LASSO) Cox analysis was conducted for immune-related genes that were differentially expressed between Foxp3_H and Foxp3_L GBM patients. We found a differential expression of Foxp3 in high-grade glioma tissues. The presence of Foxp3 was significantly associated with poor OS. From the four-gene IPS developed, GBM patients were stratified into low-risk and high-risk groups in both the training set and validation sets. Furthermore, we developed a novel nomogram to evaluate the overall survival in GBM patients. This study offers innovative insights into the GBM immune microenvironment and these findings contribute to individualized treatment and improvement in the prognosis for GBM patients.
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Affiliation(s)
- Xiao-Yu Guo
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China
| | - Guan-Hua Zhang
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China.,Department of Cerebrovascular Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510000, China
| | - Zhen-Ning Wang
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China
| | - Hao Duan
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China
| | - Tian Xie
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China
| | - Lun Liang
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China
| | - Rui Cui
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China
| | - Hong-Rong Hu
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China
| | - Yi Wu
- Department of Neurosurgery, Jiangmen Central Hospital, Jiangmen 529030, China
| | - Jia-Jun Dong
- Department of Neurosurgery, Jiangmen Central Hospital, Jiangmen 529030, China
| | - Zhen-Qiang He
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China
| | - Yong-Gao Mou
- Department of Neurosurgery/Neuro-oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510000, China
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Chen Y, Wu X, Hu D, Wang W. Importance of Mitochondrial-Related Genes in Dilated Cardiomyopathy Based on Bioinformatics Analysis. CARDIOVASCULAR INNOVATIONS AND APPLICATIONS 2020; 5. [DOI: 10.15212/cvia.2019.0588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
We designed this study to identify potential key protein interaction networks, genes, and correlated pathways in dilated cardiomyopathy (DCM) via bioinformatics methods. We selected the GSE3586 microarray dataset, consisting of 15 dilated cardiomyopathic heart biopsy samples and 13 nonfailing heart biopsy samples. Initially, the GSE3586 dataset was downloaded and was analyzed with the limma package to identify differentially expressed genes (DEGs). A total of 172 DEGs consisting of 162 upregulated genes and ten downregulated genes in DCM were selected by the criterion of adjusted Pvalues less than 0.01 and the log2-fold change of 0.6 or greater. Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to view the biological processes, cellular components, molecular function, and KEGG pathways of the DEGs. Next, protein-protein interactions were constructed, and the hub protein modules were identified. Then we selected the key genes DLD, UQCRC2, DLAT, SUCLA2, ATP5A1, PRDX3, FH, SDHD, and NDUFV1, which are involved in a wide range of biological activities, such as the citrate cycle, oxidation-reduction processes and cellular respiration, and energy derivation by oxidation of organic compounds in mitochondria. Finally, we found that currently there are no related gene-targeting drugs after exploring the predicted interactions between key genes and drugs, and transcription factors. In conclusion, our study provides greater understanding of the pathogenesis and underlying molecular mechanisms in DCM. This contributes to the exploration of potential gene therapy targets.
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Affiliation(s)
- Yukuan Chen
- Shantou University Medical College, Shantou, 515041 Guangdong, People’s Republic of China
- Department of Cardiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong, People’s Republic of China
| | - Xiaohui Wu
- Shantou University Medical College, Shantou, 515041 Guangdong, People’s Republic of China
- Department of Cardiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong, People’s Republic of China
| | - Danchun Hu
- Shantou University Medical College, Shantou, 515041 Guangdong, People’s Republic of China
- Department of Cardiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong, People’s Republic of China
| | - Wei Wang
- Department of Cardiology, Second Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong, People’s Republic of China
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Zhu W, Li LL, Songyang Y, Shi Z, Li D. Identification and validation of HELLS (Helicase, Lymphoid-Specific) and ICAM1 (Intercellular adhesion molecule 1) as potential diagnostic biomarkers of lung cancer. PeerJ 2020; 8:e8731. [PMID: 32195055 PMCID: PMC7067188 DOI: 10.7717/peerj.8731] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 02/11/2020] [Indexed: 12/25/2022] Open
Abstract
Although lung cancer is one of the greatest threats to human health, its signaling pathway and related genes are still unknown. This study integrates data from three groups of people to study potential key candidate genes and pathways related to lung cancer. Expression profiles (GSE18842, GSE19188 and GSE27262), including 162 tumor tissue and 135 adjacent normal lung tissue samples, were integrated and analyzed. Differentially expressed genes (DEGs) and candidate genes were identified, their expression pathways were analyzed, and the diethylene glycol-related protein–protein interaction (PPI) network was analyzed. We identified 232 shared DEGs (40 upregulated and 192 down-regulated) from the three GSE datasets. The DEGs were clustered according to function and signaling pathway for significant enrichment analysis. In total, 129 nodes/DEGs were identified from the DEG PPI network complex. An improved prognosis was associated with increased Helicase, Lymphoid-Specific (HELLS) and decreased Intercellular adhesion molecule 1 (ICAM1) mRNA expression in lung cancer patients. In conclusion, we used integrated bioinformatics analysis to identify candidate genes and pathways in lung cancer to show that HELLS and ICAM1 might be the key genes related to tumorigenesis or tumor progression in lung cancer. Additional studies are needed to further explore the involved functional mechanisms.
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Affiliation(s)
- Wei Zhu
- Department of Occupational and Environmental Health, Wuhan University, School of Health Science, Wuhan, Hubei, China
| | - Lin Lin Li
- Department of Occupational and Environmental Health, Wuhan University, School of Health Science, Wuhan, Hubei, China
| | - Yiyan Songyang
- Department of Occupational and Environmental Health, Wuhan University, School of Health Science, Wuhan, Hubei, China
| | - Zhan Shi
- Human Biology Program, University of Toronto, Toronto, ON, Canada
| | - Dejia Li
- Department of Occupational and Environmental Health, Wuhan University, School of Health Science, Wuhan, Hubei, China
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Geng X, Wang W, Feng Z, Liu R, Cheng X, Shen W, Dong Z, Cai G, Chen X, Hong Q, Wu D. Identification of key genes and pathways in diabetic nephropathy by bioinformatics analysis. J Diabetes Investig 2019; 10:972-984. [PMID: 30536626 PMCID: PMC6626994 DOI: 10.1111/jdi.12986] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 11/26/2018] [Accepted: 12/04/2018] [Indexed: 01/15/2023] Open
Abstract
AIMS/INTRODUCTION The aim of the present study was to identify candidate differentially expressed genes (DEGs) and pathways using bioinformatics analysis, and to improve our understanding of the cause and potential molecular events of diabetic nephropathy. MATERIALS AND METHODS Two cohort profile datasets (GSE30528 and GSE33744) were integrated and used for deep analysis. We sorted DEGs and analyzed differential pathway enrichment. DEG-associated ingenuity pathway analysis was carried out. The screened gene expression feature was verified in the db/db mouse kidney cortex. Then, rat mesangial cells cultured with high-concentration glucose were used for verification. The target genes of transcriptional factor E26 transformation-specific-1 (ETS1) were predicted with online tools and validated using chromatin immunoprecipitation assay quantitative polymerase chain reaction. RESULTS The two GSE datasets identified 89 shared DEGs; 51 were upregulated; and 38 were downregulated. Most of the DEGs were significantly enriched in cell adhesion, the plasma membrane, the extracellular matrix and the extracellular region. Quantitative reverse transcription polymerase chain reaction analysis validated the upregulated expression of Itgb2, Cd44, Sell, Fn1, Tgfbi and Il7r, and the downregulated expression of Igfbp2 and Cd55 in the db/db mouse kidney cortex. Chromatin immunoprecipitation assay quantitative polymerase chain reaction showed that Itgb2 was the target gene of transcription factor Ets1. ETS1 knockdown in rat mesangial cells decreased integrin subunit beta 2 expression. CONCLUSION We found that EST1 functioned as an important transcription factor in diabetic nephropathy development through the promotion of integrin subunit beta 2 expression. EST1 might be a drug target for diabetic nephropathy treatment.
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Affiliation(s)
- Xiao‐dong Geng
- Department of NephrologyChinese PLA General HospitalChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesChinese PLA Medical SchoolBeijingChina
- Kidney Therapeutic Center of Traditional Chinese and Western MedicineBeidaihe Sanatorium of Beijing Military RegionQinhuangdaoChina
| | - Wei‐wei Wang
- Department of Thoracic SurgeryPeking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina
| | - Zhe Feng
- Department of NephrologyChinese PLA General HospitalChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesChinese PLA Medical SchoolBeijingChina
| | - Ran Liu
- Department of NephrologyChinese PLA General HospitalChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesChinese PLA Medical SchoolBeijingChina
| | - Xiao‐long Cheng
- Department of NephrologyChinese PLA General HospitalChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesChinese PLA Medical SchoolBeijingChina
| | - Wan‐jun Shen
- Department of NephrologyChinese PLA General HospitalChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesChinese PLA Medical SchoolBeijingChina
| | - Zhe‐yi Dong
- Department of NephrologyChinese PLA General HospitalChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesChinese PLA Medical SchoolBeijingChina
| | - Guang‐yan Cai
- Department of NephrologyChinese PLA General HospitalChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesChinese PLA Medical SchoolBeijingChina
| | - Xiang‐mei Chen
- Department of NephrologyChinese PLA General HospitalChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesChinese PLA Medical SchoolBeijingChina
| | - Quan Hong
- Department of NephrologyChinese PLA General HospitalChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesChinese PLA Medical SchoolBeijingChina
| | - Di Wu
- Department of NephrologyChinese PLA General HospitalChinese PLA Institute of NephrologyState Key Laboratory of Kidney DiseasesNational Clinical Research Center for Kidney DiseasesChinese PLA Medical SchoolBeijingChina
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Yang W, Zhao X, Han Y, Duan L, Lu X, Wang X, Zhang Y, Zhou W, Liu J, Zhang H, Zhao Q, Hong L, Fan D. Identification of hub genes and therapeutic drugs in esophageal squamous cell carcinoma based on integrated bioinformatics strategy. Cancer Cell Int 2019; 19:142. [PMID: 31139019 PMCID: PMC6530124 DOI: 10.1186/s12935-019-0854-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 05/10/2019] [Indexed: 12/13/2022] Open
Abstract
Background Esophageal squamous cell carcinoma (ESCC) is one of leading malignant cancers of gastrointestinal tract worldwide. Until now, the involved mechanisms during the development of ESCC are largely unknown. This study aims to explore the driven-genes and biological pathways in ESCC. Methods mRNA expression datasets of GSE29001, GSE20347, GSE100942, and GSE38129, containing 63 pairs of ESCC and non-tumor tissues data, were integrated and deeply analyzed. The bioinformatics approaches include identification of differentially expressed genes (DEGs) and hub genes, gene ontology (GO) terms analysis and biological pathway enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, and miRNA-gene network construction. Subsequently, GEPIA2 database and qPCR assay were utilized to validate the expression of hub genes. DGIdb database was performed to search the candidate drugs for ESCC. Results Finally, 120 upregulated and 26 downregulated DEGs were identified. The functional enrichment of DEGs in ESCC were mainly correlated with cell cycle, DNA replication, deleted in colorectal cancer (DCC) mediated attractive signaling pathway, and Netrin-1 signaling pathway. The PPI network was constructed using STRING software with 146 nodes and 2392 edges. The most significant three modules in PPI were filtered and analyzed. Totally ten genes were selected and considered as the hub genes and nuclear division cycle 80 (NDC80) was closely related to the survival of ESCC patients. DGIdb database predicted 33 small molecules as the possible drugs for treating ESCC. Conclusions In summary, the data may provide new insights into ESCC pathogenesis and treatments. The candidate drugs may improve the efficiency of personalized therapy in future.
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Affiliation(s)
- Wanli Yang
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Xinhui Zhao
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Yu Han
- 2Department of Otolaryngology, Xijing Hospital, Fourth Military Medical University, Xi'an, China
| | - Lili Duan
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Xin Lu
- 3The School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xiaoqian Wang
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Yujie Zhang
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Wei Zhou
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Jinqiang Liu
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Hongwei Zhang
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Qingchuan Zhao
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Liu Hong
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
| | - Daiming Fan
- 1State Key Laboratory of Cancer Biology, National Clinical Research Center for Digestive Diseases, and Xijing Hospital of Digestive Diseases, Fourth Military Medical University, Xi'an, China
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Zhao B, Baloch Z, Ma Y, Wan Z, Huo Y, Li F, Zhao Y. Identification of Potential Key Genes and Pathways in Early-Onset Colorectal Cancer Through Bioinformatics Analysis. Cancer Control 2019; 26:1073274819831260. [PMID: 30786729 PMCID: PMC6383095 DOI: 10.1177/1073274819831260] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Revised: 10/24/2018] [Accepted: 01/23/2019] [Indexed: 12/15/2022] Open
Abstract
This study was designed to identify the potential key protein interaction networks, genes, and correlated pathways in early-onset colorectal cancer (CRC) via bioinformatics methods. We selected microarray data GSE4107 consisting 12 patient's colonic mucosa and 10 healthy control mucosa; initially, the GSE4107 were downloaded and analyzed using limma package to identify differentially expressed genes (DEGs). A total of 131 DEGs consisting of 108 upregulated genes and 23 downregulated genes of patients in early-onset CRC were selected by the criteria of adjusted P values <.01 and |log2 fold change (FC)| ≥ 2. The gene ontology functional enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were accomplished to view the biological process, cellular components, molecular function, and the KEGG pathways of DEGs. Finally, protein-protein interactions (PPIs) were constructed, and the hub protein module was identified. Genes such as ACTA2, ACTG2, MYH11, CALD1, MYL9, TPM2, and LMOD1 were strongly implicated in CRC. In summary, in this study, we indicated that molecular mechanisms were involved in muscle contraction and vascular smooth muscle contraction signaling pathway, which improve our understanding of CRC and could be used as new therapeutic targets for CRC.
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Affiliation(s)
- Bin Zhao
- Medical College of Xiamen University, Xiamen, Fujian, China
| | - Zulqarnain Baloch
- College of Veterinary Medicine, South China Agricultural University,
Guangzhou, China
| | - Yunhan Ma
- Medical College of Xiamen University, Xiamen, Fujian, China
| | - Zheng Wan
- Medical College of Xiamen University, Xiamen, Fujian, China
| | - Yani Huo
- Medical College of Xiamen University, Xiamen, Fujian, China
| | - Fujun Li
- The Department of Anesthesiology, the First Affiliated Hospital of
Harbin Medical University, Harbin, Heilongjiang, China
| | - Yilin Zhao
- Medical College of Xiamen University, Xiamen, Fujian, China
- Department of Oncology and Vascular Interventional Radiology,
Zhongshan Hospital affiliated of Xiamen University, Xiamen, Fujian, China
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10
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Xing T, Yan T, Zhou Q. Identification of key candidate genes and pathways in hepatocellular carcinoma by integrated bioinformatical analysis. Exp Ther Med 2018; 15:4932-4942. [PMID: 29805517 PMCID: PMC5958738 DOI: 10.3892/etm.2018.6075] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Accepted: 03/13/2018] [Indexed: 12/11/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most common malignant neoplasms worldwide, however the underlying mechanisms and gene signatures of HCC are unknown. In the present study the profile datasets of four cohorts were integrated to elucidate the pathways and candidate genes of HCC. The expression profiles GSE25097, GSE45267, GSE57957 and GSE62232 were downloaded from the Gene Expression Omnibus database, including 436 HCC and 94 normal liver tissues. A total of 185 differentially expressed genes (DEGs) were identified in HCC, including 92 upregulated genes and 92 downregulated genes. Gene ontology (GO) was performed, which revealed that the upregulated DEGs were primarily enriched in cell division, mitotic nuclear division, mitotic cytokinesis and G1/S transition of the mitotic cell cycle. Pathway enrichment was analyzed based on the Kyoto Encyclopedia of Genes and Genomes database to assess the functional relevance of DEGs. The most significant module was selected from protein-protein interactions and 15 important hub genes were identified. The sub-networks of hub genes were involved in cell division, p53 signaling, and T lymphotropic virus type I infection signaling pathways. In conclusion, the present study revealed that the identified DEG candidate genes may promote the understanding of the cause and molecular mechanisms underlying the development of HCC and that these candidates and signal pathways may be potential targets of clinical therapy for HCC.
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Affiliation(s)
- Tonghai Xing
- Department of General Surgery, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, P.R. China
| | - Tingmang Yan
- Department of Urology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, P.R. China
| | - Qiang Zhou
- Department of General Surgery, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200080, P.R. China
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11
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Identification of Key Candidate Genes and Pathways in Colorectal Cancer by Integrated Bioinformatical Analysis. Int J Mol Sci 2017; 18:ijms18040722. [PMID: 28350360 PMCID: PMC5412308 DOI: 10.3390/ijms18040722] [Citation(s) in RCA: 111] [Impact Index Per Article: 15.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2017] [Revised: 03/24/2017] [Accepted: 03/24/2017] [Indexed: 12/27/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most common malignant diseases worldwide, but the involved signaling pathways and driven-genes are largely unclear. This study integrated four cohorts profile datasets to elucidate the potential key candidate genes and pathways in CRC. Expression profiles GSE28000, GSE21815, GSE44076 and GSE75970, including 319 CRC and 103 normal mucosa, were integrated and deeply analyzed. Differentially expressed genes (DEGs) were sorted and candidate genes and pathways enrichment were analyzed. DEGs-associated protein–protein interaction network (PPI) was performed. Firstly, 292 shared DEGs (165 up-regulated and 127 down-regulated) were identified from the four GSE datasets. Secondly, the DEGs were clustered based on functions and signaling pathways with significant enrichment analysis. Thirdly, 180 nodes/DEGs were identified from DEGs PPI network complex. Lastly, the most significant 2 modules were filtered from PPI, 31 central node genes were identified and most of the corresponding genes are involved in cell cycle process, chemokines and G protein-coupled receptor signaling pathways. Taken above, using integrated bioinformatical analysis, we have identified DEGs candidate genes and pathways in CRC, which could improve our understanding of the cause and underlying molecular events, and these candidate genes and pathways could be therapeutic targets for CRC.
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12
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Incorporating prior knowledge to increase the power of genome-wide association studies. Methods Mol Biol 2014; 1019:519-41. [PMID: 23756909 DOI: 10.1007/978-1-62703-447-0_25] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Typical methods of analyzing genome-wide single nucleotide variant (SNV) data in cases and controls involve testing each variant's genotypes separately for phenotype association, and then using a substantial multiple-testing penalty to minimize the rate of false positives. This approach, however, can result in low power for modestly associated SNVs. Furthermore, simply looking at the most associated SNVs may not directly yield biological insights about disease etiology. SNVset methods attempt to address both limitations of the traditional approach by testing biologically meaningful sets of SNVs (e.g., genes or pathways). The number of tests run in a SNVset analysis is typically much lower (hundreds or thousands instead of millions) than in a traditional analysis, so the false-positive rate is lower. Additionally, by testing SNVsets that are biologically meaningful finding a significant set may more quickly yield insights into disease etiology.In this chapter we summarize the short history of SNVset testing and provide an overview of the many recently proposed methods. Furthermore, we provide detailed step-by-step instructions on how to perform a SNVset analysis, including a substantial number of practical tips and questions that researchers should consider before undertaking a SNVset analysis. Lastly, we describe a companion R package (snvset) that implements recently proposed SNVset methods. While SNVset testing is a new approach, with many new methods still being developed and many open questions, the promise of the approach is worth serious consideration when considering analytic methods for GWAS.
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Evangelou M, Dudbridge F, Wernisch L. Two novel pathway analysis methods based on a hierarchical model. ACTA ACUST UNITED AC 2013; 30:690-7. [PMID: 24123673 PMCID: PMC3933872 DOI: 10.1093/bioinformatics/btt583] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Motivation: Over the past few years several pathway analysis methods have been proposed for exploring and enhancing the analysis of genome-wide association data. Hierarchical models have been advocated as a way to integrate SNP and pathway effects in the same model, but their computational complexity has prevented them being applied on a genome-wide scale to date. Methods: We present two novel methods for identifying associated pathways. In the proposed hierarchical model, the SNP effects are analytically integrated out of the analysis, allowing computationally tractable model fitting to genome-wide data. The first method uses Bayes factors for calculating the effect of the pathways, whereas the second method uses a machine learning algorithm and adaptive lasso for finding a sparse solution of associated pathways. Results: The performance of the proposed methods was explored on both simulated and real data. The results of the simulation study showed that the methods outperformed some well-established association methods: the commonly used Fisher’s method for combining P-values and also the recently published BGSA. The methods were applied to two genome-wide association study datasets that aimed to find the genetic structure of platelet function and body mass index, respectively. The results of the analyses replicated the results of previously published pathway analysis of these phenotypes but also identified novel pathways that are potentially involved. Availability: An R package is under preparation. In the meantime, the scripts of the methods are available on request from the authors. Contact: marina.evangelou@cimr.cam.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Marina Evangelou
- Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, CB2 0SR, UK, JDRF/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke's Hospital, Cambridge, CB2 0XY, UK and Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK
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14
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Hua L, Zhou P, Liu H, Li L, Yang Z, Liu ZC. Mining susceptibility gene modules and disease risk genes from SNP data by combining network topological properties with support vector regression. J Theor Biol 2011; 289:225-36. [PMID: 21910999 DOI: 10.1016/j.jtbi.2011.08.040] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2011] [Revised: 08/29/2011] [Accepted: 08/29/2011] [Indexed: 01/04/2023]
Abstract
Genome-wide association study is a powerful approach to identify disease risk loci. However, the molecular regulatory mechanisms for most complex diseases are still not well understood. Therefore, further investigating the interplay between genetic factors and biological networks is important for elucidating the molecular mechanisms of complex diseases. Here, we proposed a novel framework to identify susceptibility gene modules and disease risk genes by combining network topological properties with support vector regression from single nucleotide polymorphism (SNP) level. We assigned risk SNPs to genes using the University of California at Santa Cruz (UCSC) genome database, and then mapped these genes to protein-protein interaction (PPI) networks. The gene modules implicated by hub genes were extracted using the PPI networks and the topological property was analyzed for these gene modules. For each gene module, risk feature genes were determined by topological property analysis and support vector regression. As a result, five shared risk feature genes, CD80, EGFR, FN1, GSK3B and TRAF6 were found and proven to be associated with rheumatoid arthritis by previous reports. Our approach showed a good performance in comparison with other approaches and can be used for prioritizing candidate genes associated with complex diseases.
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Affiliation(s)
- Lin Hua
- Biomedical Engineering Institute of Capital Medical University, Beijing 100069, China.
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15
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Wang K, Li M, Hakonarson H. Analysing biological pathways in genome-wide association studies. Nat Rev Genet 2010; 11:843-54. [PMID: 21085203 DOI: 10.1038/nrg2884] [Citation(s) in RCA: 581] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest. Here we review the development of pathway-based approaches for GWA studies, discuss their practical use and caveats, and suggest that pathway-based approaches may also be useful for future GWA studies with sequencing data.
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Affiliation(s)
- Kai Wang
- Center for Applied Genomics, The Childrens Hospital of Philadelphia, Pennsylvania 19104, USA
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16
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Zhang L, Li W, Song L, Chen L. A towards-multidimensional screening approach to predict candidate genes of rheumatoid arthritis based on SNP, structural and functional annotations. BMC Med Genomics 2010; 3:38. [PMID: 20727150 PMCID: PMC2939610 DOI: 10.1186/1755-8794-3-38] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2009] [Accepted: 08/20/2010] [Indexed: 11/20/2022] Open
Abstract
Background According to the Genetic Analysis Workshops (GAW), hundreds of thousands of SNPs have been tested for association with rheumatoid arthritis. Traditional genome-wide association studies (GWAS) have been developed to identify susceptibility genes using a "most significant SNPs/genes" model. However, many minor- or modest-risk genes are likely to be missed after adjustment of multiple testing. This screening process uses a strict selection of statistical thresholds that aim to identify susceptibility genes based only on statistical model, without considering multi-dimensional biological similarities in sequence arrangement, crystal structure, or functional categories/biological pathways between candidate and known disease genes. Methods Multidimensional screening approaches combined with traditional statistical genetics methods can consider multiple biological backgrounds of genetic mutation, structural, and functional annotations. Here we introduce a newly developed multidimensional screening approach for rheumatoid arthritis candidate genes that considers all SNPs with nominal evidence of Bayesian association (BFLn > 0), and structural and functional similarities of corresponding genes or proteins. Results Our multidimensional screening approach extracted all risk genes (BFLn > 0) by odd ratios of hypothesis H1 to H0, and determined whether a particular group of genes shared underlying biological similarities with known disease genes. Using this method, we found 6614 risk SNPs in our Bayesian screen result set. Finally, we identified 146 likely causal genes for rheumatoid arthritis, including CD4, FGFR1, and KDR, which have been reported as high risk factors by recent studies. We must denote that 790 (96.1%) of genes identified by GWAS could not easily be classified into related functional categories or biological processes associated with the disease, while our candidate genes shared underlying biological similarities (e.g. were in the same pathway or GO term) and contributed to disease etiology, but where common variations in each of these genes make modest contributions to disease risk. We also found 6141 risk SNPs that were too minor to be detected by conventional approaches, and associations between 58 candidate genes and rheumatoid arthritis were verified by literature retrieved from the NCBI PubMed module. Conclusions Our proposed approach to the analysis of GAW16 data for rheumatoid arthritis was based on an underlying biological similarities-based method applied to candidate and known disease genes. Application of our method could identify likely causal candidate disease genes of rheumatoid arthritis, and could yield biological insights that not detected when focusing only on genes that give the strongest evidence by multiple testing. We hope that our proposed method complements the "most significant SNPs/genes" model, and provides additional insights into the pathogenesis of rheumatoid arthritis and other diseases, when searching datasets for hundreds of genetic variances.
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Affiliation(s)
- Liangcai Zhang
- Department of Biophysics, College of Bioinformatics Science and Technology; Harbin Medical University, Harbin, Hei Longjiang Province, China
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Tintle N, Lantieri F, Lebrec J, Sohns M, Ballard D, Bickeböller H. Inclusion of a priori information in genome-wide association analysis. Genet Epidemiol 2010; 33 Suppl 1:S74-80. [PMID: 19924705 DOI: 10.1002/gepi.20476] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Genome-wide association studies (GWAS) continue to gain in popularity. To utilize the wealth of data created more effectively, a variety of methods have recently been proposed to include a priori information (e.g., biologically interpretable sets of genes, candidate gene information, or gene expression) in GWAS analysis. Six contributions to Genetic Analysis Workshop 16 Group 11 applied novel or recently proposed methods to GWAS of rheumatoid arthritis and heart disease related phenotypes. The results of these analyses were a variety of novel candidate genes and sets of genes, in addition to the validation of well-known genotype-phenotype associations. However, because many methods are relatively new, they would benefit from further methodological research to ensure that they maintain type I error rates while increasing power to find additional associations. When methods have been adapted from other study types (e.g., gene expression data analysis or linkage analysis), the lessons learned there should be used to guide implementation of techniques. Lastly, many open research questions exist concerning the logistic details of the origin of the a priori information and the way to incorporate it. Overall, our group has demonstrated a strong potential for identifying novel genotype-phenotype relationships by including a priori data in the analysis of GWAS, while also uncovering a series of questions requiring further research.
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Affiliation(s)
- Nathan Tintle
- Department of Mathematics, Hope College, Holland, Michigan 49423, USA.
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