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Liao X, Ozcan M, Shi M, Kim W, Jin H, Li X, Turkez H, Achour A, Uhlén M, Mardinoglu A, Zhang C. Open MoA: revealing the mechanism of action (MoA) based on network topology and hierarchy. Bioinformatics 2023; 39:btad666. [PMID: 37930015 PMCID: PMC10637856 DOI: 10.1093/bioinformatics/btad666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/19/2023] [Accepted: 10/30/2023] [Indexed: 11/07/2023] Open
Abstract
MOTIVATION Many approaches in systems biology have been applied in drug repositioning due to the increased availability of the omics data and computational biology tools. Using a multi-omics integrated network, which contains information of various biological interactions, could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA). RESULTS We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA). Our pipeline computes confidence scores to edges that represent connections between genes/proteins in the integrated network. The interactions showing the highest confidence score could indicate potential drug targets and infer the underlying molecular MoAs. Open MoA was also validated by testing some well-established targets. Additionally, we applied Open MoA to reveal the MoA of a repositioned drug (JNK-IN-5A) that modulates the PKLR expression in HepG2 cells and found STAT1 is the key transcription factor. Overall, Open MoA represents a first-generation tool that could be utilized for predicting the potential MoA of repurposed drugs and dissecting de novo targets for developing effective treatments. AVAILABILITY AND IMPLEMENTATION Source code is available at https://github.com/XinmengLiao/Open_MoA.
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Affiliation(s)
- Xinmeng Liao
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, 17121 Stockholm, Sweden
| | - Mehmet Ozcan
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, 17121 Stockholm, Sweden
- Department of Medical Biochemistry, Faculty of Medicine, Zonguldak Bulent Ecevit University, 67630 Zonguldak, Turkey
| | - Mengnan Shi
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, 17121 Stockholm, Sweden
| | - Woonghee Kim
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, 17121 Stockholm, Sweden
| | - Han Jin
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, 17121 Stockholm, Sweden
| | - Xiangyu Li
- Guangzhou National Laboratory, Guangzhou, Guangdong Province 510005, China
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum 25240, Turkey
| | - Adnane Achour
- Science for Life Laboratory, Department of Medicine, Solna, Karolinska Institute, 17176 Stockholm, Sweden
| | - Mathias Uhlén
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, 17121 Stockholm, Sweden
| | - Adil Mardinoglu
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, 17121 Stockholm, Sweden
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, United Kingdom
| | - Cheng Zhang
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, 17121 Stockholm, Sweden
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Doghish AS, Moustafa HAM, Elballal MS, Sarhan OM, Darwish SF, Elkalla WS, Mohammed OA, Atta AM, Abdelmaksoud NM, El-Mahdy HA, Ismail A, Abdel Mageed SS, Elrebehy MA, Abdelfatah AM, Abulsoud AI. miRNAs as potential game-changers in retinoblastoma: Future clinical and medicinal uses. Pathol Res Pract 2023; 247:154537. [PMID: 37216745 DOI: 10.1016/j.prp.2023.154537] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 05/10/2023] [Accepted: 05/16/2023] [Indexed: 05/24/2023]
Abstract
Retinoblastoma (RB) is a rare tumor in children, but it is the most common primitive intraocular malignancy in childhood age, especially those below three years old. The RB gene (RB1) undergoes mutations in individuals with RB. Although mortality rates remain high in developing countries, the survival rate for this type of cancer is greater than 95-98% in industrialized countries. However, it is lethal if left untreated, so early diagnosis is essential. As a non-coding RNA, miRNA significantly impacts RB development and treatment resistance because it can control various cellular functions. In this review, we illustrate the recent advances in the role of miRNAs in RB. That includes the clinical importance of miRNAs in RB diagnosis, prognosis, and treatment. Moreover, the regulatory mechanisms of miRNAs in RB and therapeutic interventions are discussed.
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Affiliation(s)
- Ahmed S Doghish
- Department of Biochemistry, Faculty of Pharmacy, Badr University in Cairo (BUC), Badr, Cairo 11829, Egypt; Biochemistry and Molecular Biology Department, Faculty of Pharmacy (Boys), Al-Azhar University, Nasr, Cairo 11231, Egypt.
| | - Hebatallah Ahmed Mohamed Moustafa
- Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmacy, Badr University in Cairo (BUC), Badr, Cairo 11829, Egypt
| | - Mohammed S Elballal
- Department of Biochemistry, Faculty of Pharmacy, Badr University in Cairo (BUC), Badr, Cairo 11829, Egypt
| | - Omnia M Sarhan
- Department of Pharmaceutics, Faculty of Pharmacy, Badr University in Cairo (BUC), Badr, Cairo 11829, Egypt
| | - Samar F Darwish
- Pharmacology & Toxicology Department, Faculty of Pharmacy, Badr University in Cairo (BUC), Badr, Cairo 11829, Egypt
| | - Wagiha S Elkalla
- Microbiology and Immunology Department, Faculty of Pharmacy, Badr University in Cairo (BUC), Badr, Cairo 11829, Egypt
| | - Osama A Mohammed
- Department of Clinical Pharmacology, Faculty of Medicine, Ain Shams University, Cairo 11566, Egypt; Department of Clinical Pharmacology, Faculty of Medicine, Bisha University, Bisha 61922, Saudi Arabia
| | - Asmaa M Atta
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Badr University in Cairo (BUC), Badr, Cairo 11829, Egypt
| | | | - Hesham A El-Mahdy
- Biochemistry and Molecular Biology Department, Faculty of Pharmacy (Boys), Al-Azhar University, Nasr, Cairo 11231, Egypt.
| | - Ahmed Ismail
- Biochemistry and Molecular Biology Department, Faculty of Pharmacy (Boys), Al-Azhar University, Nasr, Cairo 11231, Egypt
| | - Sherif S Abdel Mageed
- Pharmacology & Toxicology Department, Faculty of Pharmacy, Badr University in Cairo (BUC), Badr, Cairo 11829, Egypt
| | - Mahmoud A Elrebehy
- Department of Biochemistry, Faculty of Pharmacy, Badr University in Cairo (BUC), Badr, Cairo 11829, Egypt
| | - Amr M Abdelfatah
- Department of Pharmaceutical Analytical Chemistry, Faculty of Pharmacy, Badr University in Cairo, Badr, Cairo 11829, Egypt
| | - Ahmed I Abulsoud
- Biochemistry and Molecular Biology Department, Faculty of Pharmacy (Boys), Al-Azhar University, Nasr, Cairo 11231, Egypt; Biochemistry Department, Faculty of Pharmacy, Heliopolis University, Cairo 11785, Egypt
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Wang C, Liao S, Wang Y, Hu X, Xu J. Computational Identification of Guillain-Barré Syndrome-Related Genes by an mRNA Gene Expression Profile and a Protein–Protein Interaction Network. Front Mol Neurosci 2022; 15:850209. [PMID: 35370550 PMCID: PMC8968047 DOI: 10.3389/fnmol.2022.850209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 02/24/2022] [Indexed: 11/22/2022] Open
Abstract
Background In the present study, we used a computational method to identify Guillain–Barré syndrome (GBS) related genes based on (i) a gene expression profile, and (ii) the shortest path analysis in a protein–protein interaction (PPI) network. Materials and Methods mRNA Microarray analyses were performed on the peripheral blood mononuclear cells (PBMCs) of four GBS patients and four age- and gender-matched healthy controls. Results Totally 30 GBS-related genes were screened out, in which 20 were retrieved from PPI analysis of upregulated expressed genes and 23 were from downregulated expressed genes (13 overlap genes). Gene ontology (GO) enrichment and KEGG enrichment analysis were performed, respectively. Results showed that there were some overlap GO terms and KEGG pathway terms in both upregulated and downregulated analysis, including positive regulation of macromolecule metabolic process, intracellular signaling cascade, cell surface receptor linked signal transduction, intracellular non-membrane-bounded organelle, non-membrane-bounded organelle, plasma membrane, ErbB signaling pathway, focal adhesion, neurotrophin signaling pathway and Wnt signaling pathway, which indicated these terms may play a critical role during GBS process. Discussion These results provided basic information about the genetic and molecular pathogenesis of GBS disease, which may improve the development of effective genetic strategies for GBS treatment in the future.
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Affiliation(s)
- Chunyang Wang
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Shiwei Liao
- Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Department of Neurorehabilitation and Neurology, Tianjin Huanhu Hospital, Tianjin Neurosurgical Institute, Tianjin, China
| | - Yiyi Wang
- Department of Neurology, Tianjin Haihe Hospital, Tianjin, China
| | - Xiaowei Hu
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
| | - Jing Xu
- Department of Neurology, Tianjin Medical University General Hospital, Tianjin, China
- *Correspondence: Jing Xu,
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Zhang ZQ, Wu WW, Chen JD, Zhang GY, Lin JY, Wu YK, Zhang Y, Su YA, Li JT, Si TM. Weighted Gene Coexpression Network Analysis Reveals Essential Genes and Pathways in Bipolar Disorder. Front Psychiatry 2021; 12:553305. [PMID: 33815158 PMCID: PMC8010671 DOI: 10.3389/fpsyt.2021.553305] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 02/24/2021] [Indexed: 11/13/2022] Open
Abstract
Bipolar disorder (BD) is a major and highly heritable mental illness with severe psychosocial impairment, but its etiology and pathogenesis remains unclear. This study aimed to identify the essential pathways and genes involved in BD using weighted gene coexpression network analysis (WGCNA), a bioinformatic method studying the relationships between genes and phenotypes. Using two available BD gene expression datasets (GSE5388, GSE5389), we constructed a gene coexpression network and identified modules related to BD. The analyses of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways were performed to explore functional enrichment of the candidate modules. A protein-protein interaction (PPI) network was further constructed to identify the potential hub genes. Ten coexpression modules were identified from the top 5,000 genes in 77 samples and three modules were significantly associated with BD, which were involved in several biological processes (e.g., the actin filament-based process) and pathways (e.g., MAPK signaling). Four genes (NOTCH1, POMC, NGF, and DRD2) were identified as candidate hub genes by PPI analysis and CytoHubba. Finally, we carried out validation analyses in a separate dataset, GSE12649, and verified NOTCH1 as a hub gene and the involvement of several biological processes such as actin filament-based process and axon development. Taken together, our findings revealed several candidate pathways and genes (NOTCH1) in the pathogenesis of BD and call for further investigation for their potential research values in BD diagnosis and treatment.
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Affiliation(s)
- Zhen-Qing Zhang
- Xiamen Xianyue Hospital, Xiamen, China.,Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | | | | | - Guang-Yin Zhang
- Department of Psychosomatic Medicine, First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Jing-Yu Lin
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Yan-Kun Wu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Yu Zhang
- Institute of Mental Health, Hebei North University, Hebei, China
| | - Yun-Ai Su
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Ji-Tao Li
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
| | - Tian-Mei Si
- Peking University Sixth Hospital, Peking University Institute of Mental Health, Peking University, Beijing, China
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Identification of genes of four malignant tumors and a novel prediction model development based on PPI data and support vector machines. Cancer Gene Ther 2019; 27:715-725. [PMID: 31645679 DOI: 10.1038/s41417-019-0143-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 10/01/2019] [Accepted: 10/04/2019] [Indexed: 11/09/2022]
Abstract
Triple-negative breast cancer (TNBC), colon adenocarcinoma (COAD), ovarian cancer (OV), and glioblastoma multiforme (GBM) are common malignant tumors, in which significant challenges are still faced in early diagnosis, treatment, and prognosis. Therefore, further identification of genes related to those malignant tumors is of great significance for the improvement of management of the diseases. The database of the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) repository was used as the data source of gene expression profiles in this study. Malignant tumors genes were selected using a feature selection algorithm of maximal relevance and minimal redundancy (mRMR) and the protein-protein interaction (PPI) network. And finally selected 20 genes as potential related genes. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the potential related genes, and different tumor-specific genes and similarities and differences between network modules and pathways were analyzed. Further, using the potential cancer-related genes found above in this study as features, a support vector machine (SVM) model was developed to predict high-risk malignant tumors. As a result, the prediction accuracy reached more than 85%, indicating that such a model can effectively predict the four types of malignant tumors. It is demonstrated that such genes found above in this study indeed play important roles in the differentiation of the four types of malignant tumors, providing basis for future experimental biological validation and shedding some light on the understanding of new molecular mechanisms related to the four types of tumors.
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Yang CW, Cao HH, Guo Y, Feng YM, Zhang N. Identification of Novel Breast Cancer Genes based on Gene Expression Profiles and PPI Data. CURR PROTEOMICS 2019. [DOI: 10.2174/1570164616666190126111354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:Breast cancer is one of the most common malignancies, and a threat to female health all over the world. However, the molecular mechanism of breast cancer has not been fully discovered yet.Objective:It is crucial to identify breast cancer-related genes, which could provide new biomarker for breast cancer diagnosis as well as potential treatment targets.Methods:Here we used the minimum redundancy-maximum relevance (mRMR) method to select significant genes, then mapped the transcripts of the genes on the Protein-Protein Interaction (PPI) network and traced the shortest path between each pair of two proteins.Results:As a result, we identified 24 breast cancer-related genes whose betweenness were over 700. The GO enrichment analysis indicated that the transcription and oxygen level are very important in breast cancer. And the pathway analysis indicated that most of these 24 genes are enriched in prostate cancer, endocrine resistance, and pathways in cancer.Conclusion:We hope these 24 genes might be useful for diagnosis, prognosis and treatment for breast cancer.
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Affiliation(s)
- Cheng-Wen Yang
- Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Huan-Huan Cao
- Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Yu Guo
- Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Yuan-Ming Feng
- Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, China
| | - Ning Zhang
- Tianjin Key Lab of BME Measurement, Department of Biomedical Engineering, Tianjin University, Tianjin, China
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7
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Li M, Guo Y, Feng YM, Zhang N. Identification of Triple-Negative Breast Cancer Genes and a Novel High-Risk Breast Cancer Prediction Model Development Based on PPI Data and Support Vector Machines. Front Genet 2019; 10:180. [PMID: 30930932 PMCID: PMC6428707 DOI: 10.3389/fgene.2019.00180] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Accepted: 02/19/2019] [Indexed: 12/20/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is a special subtype of breast cancer that is difficult to treat. It is crucial to identify breast cancer-related genes that could provide new biomarkers for breast cancer diagnosis and potential treatment goals. In the development of our new high-risk breast cancer prediction model, seven raw gene expression datasets from the NCBI gene expression omnibus (GEO) database (GSE31519, GSE9574, GSE20194, GSE20271, GSE32646, GSE45255, and GSE15852) were used. Using the maximum relevance minimum redundancy (mRMR) method, we selected significant genes. Then, we mapped transcripts of the genes on the protein-protein interaction (PPI) network from the Search Tool for the Retrieval of Interacting Genes (STRING) database, as well as traced the shortest path between each pair of proteins. Genes with higher betweenness values were selected from the shortest path proteins. In order to ensure validity and precision, a permutation test was performed. We randomly selected 248 proteins from the PPI network for shortest path tracing and repeated the procedure 100 times. We also removed genes that appeared more frequently in randomized results. As a result, 54 genes were selected as potential TNBC-related genes. Using 14 out the 54 genes, which are potential TNBC associated genes, as input features into a support vector machine (SVM), a novel model was trained to predict high-risk breast cancer. The prediction accuracy of normal tissues and TNBC tissues reached 95.394%, and the predictions of Stage II and Stage III TNBC reached 86.598%, indicating that such genes play important roles in distinguishing breast cancers, and that the method could be promising in practical use. According to reports, some of the 54 genes we identified from the PPI network are associated with breast cancer in the literature. Several other genes have not yet been reported but have functional resemblance with known cancer genes. These may be novel breast cancer-related genes and need further experimental validation. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to appraise the 54 genes. It was indicated that cellular response to organic cyclic compounds has an influence in breast cancer, and most genes may be related with viral carcinogenesis.
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Affiliation(s)
- Ming Li
- Department of Biomedical Engineering, Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin, China
| | - Yu Guo
- Department of Biomedical Engineering, Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin, China
| | - Yuan-Ming Feng
- Department of Biomedical Engineering, Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin, China
- Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ning Zhang
- Department of Biomedical Engineering, Tianjin Key Lab of BME Measurement, Tianjin University, Tianjin, China
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8
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Marvin HJP, Janssen EM, Bouzembrak Y, Hendriksen PJM, Staats M. Big data in food safety: An overview. Crit Rev Food Sci Nutr 2016; 57:2286-2295. [DOI: 10.1080/10408398.2016.1257481] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Hans J. P. Marvin
- RIKILT Wageningen University & Research, Wageningen, The Netherlands
| | - Esmée M. Janssen
- RIKILT Wageningen University & Research, Wageningen, The Netherlands
| | - Yamine Bouzembrak
- RIKILT Wageningen University & Research, Wageningen, The Netherlands
| | | | - Martijn Staats
- RIKILT Wageningen University & Research, Wageningen, The Netherlands
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Hydrolytic Enzyme Profiling of Bacillus Subtilis COM6B and Its Application in the Bioremediation of Groundnut Oil Mill Effluent. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/978-3-319-27228-3_17] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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10
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Abstract
Epigenetics is currently one of the most promising areas of study in the field of biomedical research. Scientists have dedicated their efforts to studying epigenetic mechanisms in cancer for centuries. Additionally, the field has expanded from simply studying DNA methylation to other areas, such as histone modification, non-coding RNA, histone variation, nucleosome location, and chromosome remodeling. In ocular tumors, a large amount of epigenetic exploration has expanded from single genes to the genome-wide level. Most importantly, because epigenetic changes are reversible, several epigenetic drugs have been developed for the treatment of cancer. Herein, we review the current understanding of epigenetic mechanisms in ocular tumors, including but not limited to retinoblastoma and uveal melanoma. Furthermore, the development of new pharmacological strategies is summarized.
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Affiliation(s)
- Xuyang Wen
- Department of Ophthalmology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Linna Lu
- Department of Ophthalmology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Zhang He
- Department of Ophthalmology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
| | - Xianqun Fan
- Department of Ophthalmology, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China
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Mining for Candidate Genes Related to Pancreatic Cancer Using Protein-Protein Interactions and a Shortest Path Approach. BIOMED RESEARCH INTERNATIONAL 2015; 2015:623121. [PMID: 26613085 PMCID: PMC4647023 DOI: 10.1155/2015/623121] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2015] [Accepted: 10/15/2015] [Indexed: 12/13/2022]
Abstract
Pancreatic cancer (PC) is a highly malignant tumor derived from pancreas tissue and is one of the leading causes of death from cancer. Its molecular mechanism has been partially revealed by validating its oncogenes and tumor suppressor genes; however, the available data remain insufficient for medical workers to design effective treatments. Large-scale identification of PC-related genes can promote studies on PC. In this study, we propose a computational method for mining new candidate PC-related genes. A large network was constructed using protein-protein interaction information, and a shortest path approach was applied to mine new candidate genes based on validated PC-related genes. In addition, a permutation test was adopted to further select key candidate genes. Finally, for all discovered candidate genes, the likelihood that the genes are novel PC-related genes is discussed based on their currently known functions.
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12
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Gui T, Dong X, Li R, Li Y, Wang Z. Identification of hepatocellular carcinoma-related genes with a machine learning and network analysis. J Comput Biol 2015; 22:63-71. [PMID: 25247452 DOI: 10.1089/cmb.2014.0122] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Liver cancer is one of the leading causes of cancer mortality worldwide. Hepatocellular carcinoma (HCC) is the main type of liver cancer. We applied a machine learning approach with maximum-relevance-minimum-redundancy (mRMR) algorithm followed by incremental feature selection (IFS) to a set of microarray data generated from 43 tumor and 52 nontumor samples. With the machine learning approach, we identified 117 gene probes that could optimally separate tumor and nontumor samples. These genes not only include known HCC-relevant genes such as MT1X, BMI1, and CAP2, but also include cancer genes that were not found previously to be closely related to HCC, such as TACSTD2. Then, we constructed a molecular interaction network based on the protein-protein interaction (PPI) data from the STRING database and identified 187 genes on the shortest paths among the genes identified with the machine learning approach. Network analysis reveals new potential roles of ubiquitin C in the pathogenesis of HCC. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, we showed that the identified subnetwork is significantly enriched in biological processes related to cell death. These results bring new insights of understanding the process of HCC.
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Affiliation(s)
- Tuantuan Gui
- 1 Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences , Chinese Academy of Sciences, Shanghai, P.R. China
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Benavente CA, Dyer MA. Genetics and epigenetics of human retinoblastoma. ANNUAL REVIEW OF PATHOLOGY-MECHANISMS OF DISEASE 2015; 10:547-62. [PMID: 25621664 DOI: 10.1146/annurev-pathol-012414-040259] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Retinoblastoma is a pediatric tumor of the developing retina from which the genetic basis for cancer development was first described. Inactivation of both copies of the RB1 gene is the predominant initiating genetic lesion in retinoblastoma and is rate limiting for tumorigenesis. Recent whole-genome sequencing of retinoblastoma uncovered a tumor that had no coding-region mutations or focal chromosomal lesions other than in the RB1 gene, shifting the paradigm in the field. The retinoblastoma genome can be very stable; therefore, epigenetic deregulation of tumor-promoting pathways is required for tumorigenesis. This review highlights the genetic and epigenetic changes in retinoblastoma that have been reported, with special emphasis on recent whole-genome sequencing and epigenetic analyses that have identified novel candidate genes as potential therapeutic targets.
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Affiliation(s)
- Claudia A Benavente
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee 38105;
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14
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Li ZC, Lai YH, Chen LL, Xie Y, Dai Z, Zou XY. Identifying and prioritizing disease-related genes based on the network topological features. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2014; 1844:2214-21. [PMID: 25183318 DOI: 10.1016/j.bbapap.2014.08.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Revised: 07/22/2014] [Accepted: 08/14/2014] [Indexed: 11/26/2022]
Abstract
Identifying and prioritizing disease-related genes are the most important steps for understanding the pathogenesis and discovering the therapeutic targets. The experimental examination of these genes is very expensive and laborious, and usually has a higher false positive rate. Therefore, it is highly desirable to develop computational methods for the identification and prioritization of disease-related genes. In this study, we develop a powerful method to identify and prioritize candidate disease genes. The novel network topological features with local and global information are proposed and adopted to characterize genes. The performance of these novel features is verified based on the 10-fold cross-validation test and leave-one-out cross-validation test. The proposed features are compared with the published features, and fused strategy is investigated by combining the current features with the published features. And, these combination features are also utilized to identify and prioritize Parkinson's disease-related genes. The results indicate that identified genes are highly related to some molecular process and biological function, which provides new clues for researching pathogenesis of Parkinson's disease. The source code of Matlab is freely available on request from the authors.
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Affiliation(s)
- Zhan-Chao Li
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China.
| | - Yan-Hua Lai
- School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Li-Li Chen
- School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Yun Xie
- School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou 510006, People's Republic of China
| | - Zong Dai
- School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
| | - Xiao-Yong Zou
- School of Chemistry and Chemical Engineering, Sun Yat-Sen University, Guangzhou 510275, People's Republic of China.
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15
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Zhang N, Jiang M, Huang T, Cai YD. Identification of Influenza A/H7N9 virus infection-related human genes based on shortest paths in a virus-human protein interaction network. BIOMED RESEARCH INTERNATIONAL 2014; 2014:239462. [PMID: 24955349 PMCID: PMC4052153 DOI: 10.1155/2014/239462] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2014] [Revised: 04/18/2014] [Accepted: 04/21/2014] [Indexed: 12/15/2022]
Abstract
The recently emerging Influenza A/H7N9 virus is reported to be able to infect humans and cause mortality. However, viral and host factors associated with the infection are poorly understood. It is suggested by the "guilt by association" rule that interacting proteins share the same or similar functions and hence may be involved in the same pathway. In this study, we developed a computational method to identify Influenza A/H7N9 virus infection-related human genes based on this rule from the shortest paths in a virus-human protein interaction network. Finally, we screened out the most significant 20 human genes, which could be the potential infection related genes, providing guidelines for further experimental validation. Analysis of the 20 genes showed that they were enriched in protein binding, saccharide or polysaccharide metabolism related pathways and oxidative phosphorylation pathways. We also compared the results with those from human rhinovirus (HRV) and respiratory syncytial virus (RSV) by the same method. It was indicated that saccharide or polysaccharide metabolism related pathways might be especially associated with the H7N9 infection. These results could shed some light on the understanding of the virus infection mechanism, providing basis for future experimental biology studies and for the development of effective strategies for H7N9 clinical therapies.
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Affiliation(s)
- Ning Zhang
- Department of Biomedical Engineering, Tianjin University, Tianjin Key Lab of BME Measurement, Tianjin 300072, China
| | - Min Jiang
- State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Jiaotong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, China
| | - Tao Huang
- Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York City, NY, USA
- Institute of Systems Biology, Shanghai University, 99 Shangda Road, Shanghai 200444, China
| | - Yu-Dong Cai
- Institute of Systems Biology, Shanghai University, 99 Shangda Road, Shanghai 200444, China
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16
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Prediction and analysis of retinoblastoma related genes through gene ontology and KEGG. BIOMED RESEARCH INTERNATIONAL 2013; 2013:304029. [PMID: 23998122 PMCID: PMC3755425 DOI: 10.1155/2013/304029] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2013] [Accepted: 07/16/2013] [Indexed: 11/18/2022]
Abstract
One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.
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17
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Identifying chemicals with potential therapy of HIV based on protein-protein and protein-chemical interaction network. PLoS One 2013; 8:e65207. [PMID: 23762317 PMCID: PMC3675210 DOI: 10.1371/journal.pone.0065207] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2013] [Accepted: 04/23/2013] [Indexed: 12/27/2022] Open
Abstract
Acquired immune deficiency syndrome (AIDS) is a severe infectious disease that causes a large number of deaths every year. Traditional anti-AIDS drugs directly targeting the HIV-1 encoded enzymes including reverse transcriptase (RT), protease (PR) and integrase (IN) usually suffer from drug resistance after a period of treatment and serious side effects. In recent years, the emergence of numerous useful information of protein-protein interactions (PPI) in the HIV life cycle and related inhibitors makes PPI a new way for antiviral drug intervention. In this study, we identified 26 core human proteins involved in PPI between HIV-1 and host, that have great potential for HIV therapy. In addition, 280 chemicals that interact with three HIV drugs targeting human proteins can also interact with these 26 core proteins. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying novel anti-HIV drugs.
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18
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Identification of lung-cancer-related genes with the shortest path approach in a protein-protein interaction network. BIOMED RESEARCH INTERNATIONAL 2013; 2013:267375. [PMID: 23762832 PMCID: PMC3674655 DOI: 10.1155/2013/267375] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2013] [Revised: 04/19/2013] [Accepted: 04/29/2013] [Indexed: 12/30/2022]
Abstract
Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In this work, a computational method was proposed for identifying lung-cancer-related genes with a shortest path approach in a protein-protein interaction (PPI) network. Based on the PPI data from STRING, a weighted PPI network was constructed. 54 NSCLC- and 84 SCLC-related genes were retrieved from associated KEGG pathways. Then the shortest paths between each pair of these 54 NSCLC genes and 84 SCLC genes were obtained with Dijkstra's algorithm. Finally, all the genes on the shortest paths were extracted, and 25 and 38 shortest genes with a permutation P value less than 0.05 for NSCLC and SCLC were selected for further analysis. Some of the shortest path genes have been reported to be related to lung cancer. Intriguingly, the candidate genes we identified from the PPI network contained more cancer genes than those identified from the gene expression profiles. Furthermore, these genes possessed more functional similarity with the known cancer genes than those identified from the gene expression profiles. This study proved the efficiency of the proposed method and showed promising results.
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19
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Thériault BL, Dimaras H, Gallie BL, Corson TW. The genomic landscape of retinoblastoma: a review. Clin Exp Ophthalmol 2013; 42:33-52. [PMID: 24433356 DOI: 10.1111/ceo.12132] [Citation(s) in RCA: 131] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 04/07/2013] [Indexed: 12/13/2022]
Abstract
Retinoblastoma is a paediatric ocular tumour that continues to reveal much about the genetic basis of cancer development. Study of genomic aberrations in retinoblastoma tumours has exposed important mechanisms of cancer development and identified oncogenes and tumour suppressors that offer potential points of therapeutic intervention. The recent development of next-generation genomic technologies has allowed further refinement of the genomic landscape of retinoblastoma at high resolution. In a relatively short period of time, a wealth of genetic and epigenetic data has emerged on a small number of tumour samples. These data highlight the inherent molecular complexity of this cancer despite the fact that most retinoblastomas are initiated by the inactivation of a single tumour suppressor gene. This review outlines the current understanding of the genomic, genetic and epigenetic changes in retinoblastoma, highlighting recent genome-wide analyses that have identified exciting candidate genes worthy of further validation as potential prognostic and therapeutic targets.
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Affiliation(s)
- Brigitte L Thériault
- Campbell Family Cancer Research Institute, Ontario Cancer Institute, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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20
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Beta M, Venkatesan N, Vasudevan M, Vetrivel U, Khetan V, Krishnakumar S. Identification and Insilico Analysis of Retinoblastoma Serum microRNA Profile and Gene Targets Towards Prediction of Novel Serum Biomarkers. Bioinform Biol Insights 2013; 7:21-34. [PMID: 23400111 PMCID: PMC3547501 DOI: 10.4137/bbi.s10501] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Retinoblastoma (RB) is a malignant tumor of the retina seen in children, and potential non invasive biomarkers are in need for rapid diagnosis and for prognosticating the therapy. This study was undertaken to identify the differentially expressed miRNAs in the serum of children with RB in comparison with the normal age matched serum, to analyze its concurrence with the existing RB tumor miRNA profile, to identify its novel gene targets specific to RB, and to study the expression of a few of the identified oncogenic miRNAs in the advanced stage primary RB patient’s serum sample. MiRNA profiling was performed on 14 pooled serum from children with advanced RB and 14 normal age matched serum samples, wherein 21 miRNAs were found to be upregulated (fold change ≤ −2.0, P ≤ 0.05) and 24 to be downregulated (fold change ≥ +2.0, P ≤ 0.05). Furthermore, intersection of 59 significantly deregulated miRNAs identified from RB tumor profiles with that of miRNAs detected in serum profile revealed that 33 miRNAs had followed a similar deregulation pattern in RB serum. Later we validated a few of the miRNAs (miRNA 17-92) identified by microarray in the RB patient serum samples (n = 20) by using qRT-PCR. Expression of the oncogenic miRNAs, miR-17, miR-18a, and miR-20a by qRT-PCR was significant in the serum samples exploring the potential of serum miRNAs identification as noninvasive diagnosis. Moreover, from miRNA gene target prediction, key regulatory genes of cell proliferation, apoptosis, and positive and negative regulatory networks involved in RB progression were identified in the gene expression profile of RB tumors. Therefore, these identified miRNAs and their corresponding target genes could give insights on potential biomarkers and key events involved in the RB pathway.
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Affiliation(s)
- Madhu Beta
- L & T Ocular Pathology Department, Kamalnayan Bajaj Research Institute, Vision Research Foundation, Sankara Nethralaya, Tamil Nadu, India. ; Shanmugha Arts, Science, Technology & Research Academy (SASTRA University), Tirumalaisamudram, Thanjavur, Tamil Nadu, India
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21
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Reis AHO, Vargas FR, Lemos B. More epigenetic hits than meets the eye: microRNAs and genes associated with the tumorigenesis of retinoblastoma. Front Genet 2012; 3:284. [PMID: 23233862 PMCID: PMC3516829 DOI: 10.3389/fgene.2012.00284] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2012] [Accepted: 11/21/2012] [Indexed: 12/29/2022] Open
Abstract
Retinoblastoma (RB), a childhood neoplasia of the retinoblasts, can occur unilaterally or bilaterally, with one or multiple foci per eye. RB is associated with somatic loss of function of both alleles of the tumor suppressor gene RB1. Hereditary forms emerge due to germline loss of function mutations in RB1 alleles. RB has long been the prototypic “model” cancer ever since Knudson's “two-hit” hypothesis. However, a simple two-hit model for RB is challenged by an increasing number of studies documenting additional hits that contribute to RB development. Here we review the genetics and epigenetics of RB with a focus on the role of small non-coding RNAs (microRNAs) and on novel findings indicating the relevance of DNA methylation in the development and prognosis of this neoplasia. Studies point to an elaborated landscape of genetic and epigenetic complexity, in which a number of events and pahtways play crucial roles in the origin and prognosis of RB. These include roles for microRNAs, inprinted loci, and parent-of-origin contributions to RB1 regulation and RB progression. This complexity is also manifested in the structure of the RB1 locus itself: it includes numerous repetitive DNA segments and retrotransposon insertion elements, some of which are actively transcribed from the RB1 locus. Altogether, we conclude that RB1 loss of function represents the tip of an iceberg of events that determine RB development, progression, severity, and disease risk. Comprehensive assessment of personalized RB risk will require genetic and epigenetic evaluations beyond RB1 protein coding sequences.
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Affiliation(s)
- Adriana H O Reis
- Genetics Program, Instituto Nacional de Câncer Rio de Janeiro, Brazil
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