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Alzahrani AA, Almajidi YQ, Jasim SA, Hjazi A, Olegovich BD, Alkhafaji AT, Abdulridui HA, Ahmed BA, Alawadi A, Alsalamy A. Getting to know ovarian cancer: Focusing on the effect of LncRNAs in this cancer and the effective signaling pathways. Pathol Res Pract 2024; 254:155084. [PMID: 38244434 DOI: 10.1016/j.prp.2023.155084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 01/22/2024]
Abstract
This article undertakes a comprehensive investigation of ovarian cancer, examining the complex nature of this challenging disease. The main focus is on understanding the role of long non-coding RNAs (lncRNAs) in the context of ovarian cancer (OC), and their regulatory functions in disease progression. Through extensive research, the article identifies specific lncRNAs that play significant roles in the intricate molecular processes of OC. Furthermore, the study examines the signaling pathways involved in the development of OC, providing a detailed comprehension of the underlying molecular mechanisms. By connecting lncRNA dynamics with signaling pathways, this exploration not only advances our understanding of ovarian cancer but also reveals potential targets for therapeutic interventions. The findings open up opportunities for targeted treatments, highlighting the importance of personalized approaches in addressing this complex disease and driving progress in ovarian cancer research and treatment strategies.
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Affiliation(s)
| | | | | | - Ahmed Hjazi
- Department of Medical Laboratory Sciences, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Saudi Arabia
| | - Bokov Dmitry Olegovich
- Institute of Pharmacy, Moscow Medical University, Moscow, Russian Federation; Laboratory of Food Chemistry, Federal Research Center of Nutrition, Biotechnology and Food Safety, Moscow, Russian Federation
| | | | | | - Batool Ali Ahmed
- Department of Medical Engineering, Al-Nisour University College, Baghdad, Iraq
| | - Ahmed Alawadi
- College of technical engineering, the Islamic University, Najaf, Iraq; College of technical engineering, the Islamic University of Al Diwaniyah, Iraq; College of technical engineering, the Islamic University of Babylon, Iraq
| | - Ali Alsalamy
- College of technical engineering, Imam Ja'afar Al-Sadiq University, Iraq
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2
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Tian J, Zhao J, Zheng C. Clustering of cancer data based on Stiefel manifold for multiple views. BMC Bioinformatics 2021; 22:268. [PMID: 34034643 PMCID: PMC8152349 DOI: 10.1186/s12859-021-04195-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 05/12/2021] [Indexed: 12/23/2022] Open
Abstract
Background In recent years, various sequencing techniques have been used to collect biomedical omics datasets. It is usually possible to obtain multiple types of omics data from a single patient sample. Clustering of omics data plays an indispensable role in biological and medical research, and it is helpful to reveal data structures from multiple collections. Nevertheless, clustering of omics data consists of many challenges. The primary challenges in omics data analysis come from high dimension of data and small size of sample. Therefore, it is difficult to find a suitable integration method for structural analysis of multiple datasets. Results In this paper, a multi-view clustering based on Stiefel manifold method (MCSM) is proposed. The MCSM method comprises three core steps. Firstly, we established a binary optimization model for the simultaneous clustering problem. Secondly, we solved the optimization problem by linear search algorithm based on Stiefel manifold. Finally, we integrated the clustering results obtained from three omics by using k-nearest neighbor method. We applied this approach to four cancer datasets on TCGA. The result shows that our method is superior to several state-of-art methods, which depends on the hypothesis that the underlying omics cluster class is the same. Conclusion Particularly, our approach has better performance than compared approaches when the underlying clusters are inconsistent. For patients with different subtypes, both consistent and differential clusters can be identified at the same time.
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Affiliation(s)
- Jing Tian
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China
| | - Jianping Zhao
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.
| | - Chunhou Zheng
- College of Mathematics and System Sciences, Xinjiang University, Urumqi, China.,School of Computer Science and Technology, Anhui University, Hefei, China
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3
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Zhang D, Bin Y. DriverSubNet: A Novel Algorithm for Identifying Cancer Driver Genes by Subnetwork Enrichment Analysis. Front Genet 2021; 11:607798. [PMID: 33679866 PMCID: PMC7933651 DOI: 10.3389/fgene.2020.607798] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/30/2020] [Indexed: 01/07/2023] Open
Abstract
Identification of driver genes from mass non-functional passenger genes in cancers is still a critical challenge. Here, an effective and no parameter algorithm, named DriverSubNet, is presented for detecting driver genes by effectively mining the mutation and gene expression information based on subnetwork enrichment analysis. Compared with the existing classic methods, DriverSubNet can rank driver genes and filter out passenger genes more efficiently in terms of precision, recall, and F1 score, as indicated by the analysis of four cancer datasets. The method recovered about 50% more known cancer driver genes in the top 100 detected genes than those found in other algorithms. Intriguingly, DriverSubNet was able to find these unknown cancer driver genes which could act as potential therapeutic targets and useful prognostic biomarkers for cancer patients. Therefore, DriverSubNet may act as a useful tool for the identification of driver genes by subnetwork enrichment analysis.
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Affiliation(s)
- Di Zhang
- College of Information Engineering, Shaoguan University, Shaoguan, China
| | - Yannan Bin
- Institutes of Physical Science and Information Technology, Anhui University, Hefei, China
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4
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Zheng M, Mullikin H, Hester A, Czogalla B, Heidegger H, Vilsmaier T, Vattai A, Chelariu-Raicu A, Jeschke U, Trillsch F, Mahner S, Kaltofen T. Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile. Int J Mol Sci 2020; 21:E9169. [PMID: 33271935 PMCID: PMC7731240 DOI: 10.3390/ijms21239169] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 11/06/2020] [Accepted: 11/27/2020] [Indexed: 02/06/2023] Open
Abstract
(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.
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Affiliation(s)
- Mingjun Zheng
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Heather Mullikin
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Anna Hester
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Bastian Czogalla
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Helene Heidegger
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Theresa Vilsmaier
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Aurelia Vattai
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Anca Chelariu-Raicu
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Udo Jeschke
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
- Department of Obstetrics and Gynecology, University Hospital Augsburg, Stenglinstrasse 2, 86156 Augsburg, Germany
| | - Fabian Trillsch
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Sven Mahner
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Till Kaltofen
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
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5
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LncRNAs in Ovarian Cancer Progression, Metastasis, and Main Pathways: ceRNA and Alternative Mechanisms. Int J Mol Sci 2020; 21:ijms21228855. [PMID: 33238475 PMCID: PMC7700431 DOI: 10.3390/ijms21228855] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 11/16/2020] [Accepted: 11/18/2020] [Indexed: 12/12/2022] Open
Abstract
Ovarian cancer (OvCa) develops asymptomatically until it reaches the advanced stages with metastasis, chemoresistance, and poor prognosis. Our review focuses on the analysis of regulatory long non-coding RNAs (lncRNAs) competing with protein-coding mRNAs for binding to miRNAs according to the model of competitive endogenous RNA (ceRNA) in OvCa. Analysis of publications showed that most lncRNAs acting as ceRNAs participate in OvCa progression: migration, invasion, epithelial-mesenchymal transition (EMT), and metastasis. More than 30 lncRNAs turned out to be predictors of survival and/or response to therapy in patients with OvCa. For a number of oncogenic (CCAT1, HOTAIR, NEAT1, and TUG1 among others) and some suppressive lncRNAs, several lncRNA/miRNA/mRNA axes were identified, which revealed various functions for each of them. Our review also considers examples of alternative mechanisms of actions for lncRNAs besides being ceRNAs, including binding directly to mRNA or protein, and some of them (DANCR, GAS5, MALAT1, and UCA1 among others) act by both mechanisms depending on the target protein. A systematic analysis based on the data from literature and Panther or KEGG (Kyoto Encyclopedia of Genes and Genomes) databases showed that a significant part of lncRNAs affects the key pathways involved in OvCa metastasis, EMT, and chemoresistance.
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6
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Ravel JM, Monraz Gomez LC, Sompairac N, Calzone L, Zhivotovsky B, Kroemer G, Barillot E, Zinovyev A, Kuperstein I. Comprehensive Map of the Regulated Cell Death Signaling Network: A Powerful Analytical Tool for Studying Diseases. Cancers (Basel) 2020; 12:E990. [PMID: 32316560 PMCID: PMC7226067 DOI: 10.3390/cancers12040990] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 03/10/2020] [Indexed: 12/25/2022] Open
Abstract
The processes leading to, or avoiding cell death are widely studied, because of their frequent perturbation in various diseases. Cell death occurs in three highly interconnected steps: Initiation, signaling and execution. We used a systems biology approach to gather information about all known modes of regulated cell death (RCD). Based on the experimental data retrieved from literature by manual curation, we graphically depicted the biological processes involved in RCD in the form of a seamless comprehensive signaling network map. The molecular mechanisms of each RCD mode are represented in detail. The RCD network map is divided into 26 functional modules that can be visualized contextually in the whole seamless network, as well as in individual diagrams. The resource is freely available and accessible via several web platforms for map navigation, data integration, and analysis. The RCD network map was employed for interpreting the functional differences in cell death regulation between Alzheimer's disease and non-small cell lung cancer based on gene expression data that allowed emphasizing the molecular mechanisms underlying the inverse comorbidity between the two pathologies. In addition, the map was used for the analysis of genomic and transcriptomic data from ovarian cancer patients that provided RCD map-based signatures of four distinct tumor subtypes and highlighted the difference in regulations of cell death molecular mechanisms.
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Affiliation(s)
- Jean-Marie Ravel
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
- Laboratoire de génétique médicale, CHRU-Nancy, F-54000 Nancy, France
- Inserm, NGERE, Université de Lorraine, F-54000 Nancy, France
| | - L. Cristobal Monraz Gomez
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
- Centre de Recherches Interdisciplinaires, Université Paris Descartes, 75006 Paris, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Boris Zhivotovsky
- Faculty of Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia;
- Division of Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Box 210, 17177 Stockholm, Sweden
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université de Paris, Sorbonne Université, Inserm U1138, Institut Universitaire de France, 75006 Paris, France;
- Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, 94805 Villejuif, France
- Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France
- Suzhou Institute for Systems Medicine, Chinese Academy of Medical Sciences, Suzhou 215163, China
- Karolinska Institute, Department of Women’s and Children’s Health, Karolinska University Hospital, 171 77 Stockholm, Sweden
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Inna Kuperstein
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
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7
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Di Nanni N, Bersanelli M, Milanesi L, Mosca E. Network Diffusion Promotes the Integrative Analysis of Multiple Omics. Front Genet 2020; 11:106. [PMID: 32180795 PMCID: PMC7057719 DOI: 10.3389/fgene.2020.00106] [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: 07/31/2019] [Accepted: 01/29/2020] [Indexed: 02/01/2023] Open
Abstract
The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion—also referred to as network propagation—has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity. Indeed, network diffusion provides a quantitative estimation of network proximity between genes associated with one or more different data types, from simple binary vectors to real vectors. Therefore, this powerful data transformation method has also been increasingly used in integrative analyses of multiple collections of biological scores and/or one or more interaction networks. We present an overview of the state of the art of bioinformatics pipelines that use network diffusion processes for the integrative analysis of omics data. We discuss the fundamental ways in which network diffusion is exploited, open issues and potential developments in the field. Current trends suggest that network diffusion is a tool of broad utility in omics data analysis. It is reasonable to think that it will continue to be used and further refined as new data types arise (e.g. single cell datasets) and the identification of system-level patterns will be considered more and more important in omics data analysis.
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Affiliation(s)
- Noemi Di Nanni
- Institute of Biomedical Technologies, National Research Council, Milan, Italy.,Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy
| | - Matteo Bersanelli
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy.,National Institute of Nuclear Physics (INFN), Bologna, Italy
| | - Luciano Milanesi
- Institute of Biomedical Technologies, National Research Council, Milan, Italy
| | - Ettore Mosca
- Institute of Biomedical Technologies, National Research Council, Milan, Italy
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8
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Zheng M, Hu Y, Gou R, Liu O, Nie X, Li X, Liu Q, Hao Y, Liu J, Lin B. Identification of immune-enhanced molecular subtype associated with BRCA1 mutations, immune checkpoints and clinical outcome in ovarian carcinoma. J Cell Mol Med 2020; 24:2819-2831. [PMID: 31995855 PMCID: PMC7077593 DOI: 10.1111/jcmm.14830] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/26/2019] [Accepted: 11/06/2019] [Indexed: 12/15/2022] Open
Abstract
Ovarian carcinoma has the highest mortality among the malignant tumours in gynaecology, and new treatment strategies are urgently needed to improve the clinical status of ovarian carcinoma patients. The Cancer Genome Atlas (TCGA) cohort were performed to explore the immune function of the internal environment of tumours and its clinical correlation with ovarian carcinoma. Finally, four molecular subtypes were obtained based on the global immune‐related genes. The correlation analysis and clinical characteristics showed that four subtypes were all significantly related to clinical stage; the immune scoring results indicated that most immune signatures were upregulated in C3 subtype, and the majority of tumour‐infiltrating immune cells were upregulated in both C3 and C4 subtypes. Compared with other subtypes, C3 subtype had a higher BRCA1 mutation, higher expression of immune checkpoints, and optimal survival prognosis. These findings of the immunological microenvironment in tumours may provide new ideas for developing immunotherapeutic strategies for ovarian carcinoma.
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Affiliation(s)
- Mingjun Zheng
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China.,Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Munich, Germany
| | - Yuexin Hu
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Rui Gou
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Ouxuan Liu
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Xin Nie
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Xiao Li
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Qing Liu
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Yingying Hao
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Juanjuan Liu
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
| | - Bei Lin
- Department of Gynaecology and Obstetrics, Shengjing Hospital Affiliated to China Medical University, Shenyang, China.,Key Laboratory Of Maternal-Fetal Medicine of Liaoning Province, Key Laboratory of Obstetrics and Gynecology of Higher Education of Liaoning Province, Shenyang, China
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9
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Malik MZ, Chirom K, Ali S, Ishrat R, Somvanshi P, Singh RKB. Methodology of predicting novel key regulators in ovarian cancer network: a network theoretical approach. BMC Cancer 2019; 19:1129. [PMID: 31752757 PMCID: PMC6869253 DOI: 10.1186/s12885-019-6309-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Accepted: 10/30/2019] [Indexed: 02/08/2023] Open
Abstract
Background Identification of key regulator/s in ovarian cancer (OC) network is important for potential drug target and prevention from this cancer. This study proposes a method to identify the key regulators of this network and their importance. Methods The protein-protein interaction (PPI) network of ovarian cancer (OC) is constructed from curated 6 hundred genes from standard six important ovarian cancer databases (some of the genes are experimentally verified). We proposed a method to identify key regulators (KRs) from the complex ovarian cancer network based on the tracing of backbone hubs, which participate at all levels of organization, characterized by Newmann-Grivan community finding method. Knockout experiment, constant Potts model and survival analysis are done to characterize the importance of the key regulators in regulating the network. Results The PPI network of ovarian cancer is found to obey hierarchical scale free features organized by topology of heterogeneous modules coordinated by diverse leading hubs. The network and modular structures are devised by fractal rules with the absence of centrality-lethality rule, to enhance the efficiency of signal processing in the network and constituting loosely connected modules. Within the framework of network theory, we device a method to identify few key regulators (KRs) from a huge number of leading hubs, that are deeply rooted in the network, serve as backbones of it and key regulators from grassroots level to complete network structure. Using this method we could able to identify five key regulators, namely, AKT1, KRAS, EPCAM, CD44 and MCAM, out of which AKT1 plays central role in two ways, first it serves as main regulator of ovarian cancer network and second serves as key cross-talk agent of other key regulators, but exhibits disassortive property. The regulating capability of AKT1 is found to be highest and that of MCAM is lowest. Conclusions The popularities of these key hubs change in an unpredictable way at different levels of organization and absence of these hubs cause massive amount of wiring energy/rewiring energy that propagate over all the network. The network compactness is found to increase as one goes from top level to bottom level of the network organization.
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Affiliation(s)
- Md Zubbair Malik
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India
| | - Keilash Chirom
- Department of Biotechnology, TERI University, New Delhi, 110070, India
| | - Shahnawaz Ali
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Romana Ishrat
- Centre for Interdisciplinary Research in Basic Sciences, Jamia Millia Islamia, New Delhi, 110025, India
| | - Pallavi Somvanshi
- Department of Biotechnology, TERI University, New Delhi, 110070, India
| | - R K Brojen Singh
- School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, 110067, India.
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10
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Xie H, Xu H, Hou Y, Cai Y, Rong Z, Song W, Wang W, Li K. Integrative prognostic subtype discovery in high-grade serous ovarian cancer. J Cell Biochem 2019; 120:18659-18666. [PMID: 31347734 DOI: 10.1002/jcb.29049] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 04/30/2019] [Indexed: 01/19/2023]
Abstract
OBJECTIVE We sought to identify novel molecular subtypes of high-grade serous ovarian cancer (HGSC) by the integration of gene expression and proteomics data and to find the underlying biological characteristics of ovarian cancer to improve the clinical outcome. METHODS The iCluster method was utilized to analysis 131 common HGSC samples between TCGA and Clinical Proteomic Tumor Analysis Consortium databases. Kaplan-Meier survival curves were used to estimate the overall survival of patients, and the differences in survival curves were assessed using the log-rank test. RESULTS Two novel ovarian cancer subtypes with different overall survival (P = .00114) and different platinum status (P = .0061) were identified. Eighteen messenger RNAs and 38 proteins were selected as differential molecules between subtypes. Pathway analysis demonstrated arrhythmogenic right ventricular cardiomyopathy pathway played a critical role in the discrimination of these two subtypes and desmosomal cadherin DSG2, DSP, JUP, and PKP2 in this pathway were overexpression in subtype I compared with subtype II. CONCLUSION Our study extended the underlying prognosis-related biological characteristics of high-grade serous ovarian cancer. Enrichment of desmosomal cadherin increased the risk for HGSC prognosis among platinum-sensitive patients, the results guided the revision of the treatment options for platinum-sensitive ovarian cancer patients to improve outcomes.
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Affiliation(s)
- Hongyu Xie
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Huan Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Yan Hou
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Yuqing Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Zhiwei Rong
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Wei Song
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Wenjie Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Kang Li
- Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
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11
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da Costa AABA, do Canto LM, Larsen SJ, Ribeiro ARG, Stecca CE, Petersen AH, Aagaard MM, de Brot L, Baumbach J, Baiocchi G, Achatz MI, Rogatto SR. Genomic profiling in ovarian cancer retreated with platinum based chemotherapy presented homologous recombination deficiency and copy number imbalances of CCNE1 and RB1 genes. BMC Cancer 2019; 19:422. [PMID: 31060523 PMCID: PMC6503431 DOI: 10.1186/s12885-019-5622-4] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 04/18/2019] [Indexed: 02/01/2023] Open
Abstract
Background Ovarian carcinomas presenting homologous recombination deficiency (HRD), which is observed in about 50% of cases, are more sensitive to platinum and PARP inhibitor therapies. Although platinum resistant disease has a low chance to be responsive to platinum-based chemotherapy, a set of patients is retreated with platinum and some of them are responsive. In this study, we evaluated copy number alterations, HR gene mutations and HR deficiency scores in ovarian cancer patients with prolonged platinum sensitivity. Methods In this retrospective study (2005 to 2014), we selected 31 patients with platinum resistant ovarian cancer retreated with platinum therapy. Copy number alterations and HR scores were evaluated using the OncoScan® FFPE platform in 15 cases. The mutational profile of 24 genes was investigated by targeted-NGS. Results The median values of the four HRD scores were higher in responders (LOH = 15, LST = 28, tAI = 33, CS = 84) compared with non-responders (LOH = 7.5, LST = 17.5, tAI = 23, CS = 47). Patients with high LOH, LST, tAI and CS scores had better response rates, although these differences were not statistically significant. Response rate to platinum retreatment was 22% in patients with CCNE1 gains and 83.5% in patients with no CCNE1 gains (p = 0.041). Furthermore, response rate was 54.5% in patients with RB1 loss and 25% in patients without RB1 loss (p = 0.569). Patients with CCNE1 gains showed a worse progression free survival (PFS = 11.1 months vs 3.7 months; p = 0.008) and a shorter overall survival (OS = 39.3 months vs 7.1 months; p = 0.007) in comparison with patients with no CCNE1 gains. Patients with RB1 loss had better PFS (9.0 months vs 2.6 months; p = 0.093) and OS (27.4 months vs 3.6 months; p = 0.025) compared with cases with no RB1 loss. Four tumor samples were BRCA mutated and tumor mutations were not associated with response to treatment. Conclusions HR deficiency was found in 60% of our cases and HRD medium values were higher in responders than in non-responders. Despite the small number of patients tested, CCNE1 gain and RB1 loss discriminate patients with tumors extremely sensitive to platinum retreatment. Electronic supplementary material The online version of this article (10.1186/s12885-019-5622-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Alexandre A B A da Costa
- Department of Medical Oncology, AC Camargo Cancer Center, Rua Professor Antonio Prudente 211, São Paulo, CEP: 01509-010, Brazil.
| | - Luisa M do Canto
- CIPE - AC Camargo Cancer Center, São Paulo, Brazil.,Dept of Clinical Genetics, Vejle Hospital, Institute of Regional Health Research, University of Southern Denmark, Vejle, DK, Denmark
| | - Simon Jonas Larsen
- Dept of Mathematics and Computer Science, University of Southern Denmark, Odense, DK, Denmark
| | | | - Carlos Eduardo Stecca
- Department of Medical Oncology, AC Camargo Cancer Center, Rua Professor Antonio Prudente 211, São Paulo, CEP: 01509-010, Brazil
| | - Annabeth Høgh Petersen
- Dept of Mathematics and Computer Science, University of Southern Denmark, Odense, DK, Denmark
| | - Mads M Aagaard
- Dept of Mathematics and Computer Science, University of Southern Denmark, Odense, DK, Denmark
| | - Louise de Brot
- Dept of Pathology, AC Camargo Cancer Center, São Paulo, Brazil
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan Technical University of Munich, Munich, Germany
| | - Glauco Baiocchi
- Dept of Gynecologic Oncology, AC Camargo Cancer Center, São Paulo, Brazil
| | | | - Silvia Regina Rogatto
- Dept of Clinical Genetics, Vejle Hospital, Institute of Regional Health Research, University of Southern Denmark, Vejle, DK, Denmark
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12
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da Costa AABA, do Canto LM, Larsen SJ, Ribeiro ARG, Stecca CE, Petersen AH, Aagaard MM, de Brot L, Baumbach J, Baiocchi G, Achatz MI, Rogatto SR. Genomic profiling in ovarian cancer retreated with platinum based chemotherapy presented homologous recombination deficiency and copy number imbalances of CCNE1 and RB1 genes. BMC Cancer 2019. [PMID: 31060523 DOI: 10.1186/s12885-019-5622-4]+[] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Ovarian carcinomas presenting homologous recombination deficiency (HRD), which is observed in about 50% of cases, are more sensitive to platinum and PARP inhibitor therapies. Although platinum resistant disease has a low chance to be responsive to platinum-based chemotherapy, a set of patients is retreated with platinum and some of them are responsive. In this study, we evaluated copy number alterations, HR gene mutations and HR deficiency scores in ovarian cancer patients with prolonged platinum sensitivity. METHODS In this retrospective study (2005 to 2014), we selected 31 patients with platinum resistant ovarian cancer retreated with platinum therapy. Copy number alterations and HR scores were evaluated using the OncoScan® FFPE platform in 15 cases. The mutational profile of 24 genes was investigated by targeted-NGS. RESULTS The median values of the four HRD scores were higher in responders (LOH = 15, LST = 28, tAI = 33, CS = 84) compared with non-responders (LOH = 7.5, LST = 17.5, tAI = 23, CS = 47). Patients with high LOH, LST, tAI and CS scores had better response rates, although these differences were not statistically significant. Response rate to platinum retreatment was 22% in patients with CCNE1 gains and 83.5% in patients with no CCNE1 gains (p = 0.041). Furthermore, response rate was 54.5% in patients with RB1 loss and 25% in patients without RB1 loss (p = 0.569). Patients with CCNE1 gains showed a worse progression free survival (PFS = 11.1 months vs 3.7 months; p = 0.008) and a shorter overall survival (OS = 39.3 months vs 7.1 months; p = 0.007) in comparison with patients with no CCNE1 gains. Patients with RB1 loss had better PFS (9.0 months vs 2.6 months; p = 0.093) and OS (27.4 months vs 3.6 months; p = 0.025) compared with cases with no RB1 loss. Four tumor samples were BRCA mutated and tumor mutations were not associated with response to treatment. CONCLUSIONS HR deficiency was found in 60% of our cases and HRD medium values were higher in responders than in non-responders. Despite the small number of patients tested, CCNE1 gain and RB1 loss discriminate patients with tumors extremely sensitive to platinum retreatment.
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Affiliation(s)
- Alexandre A B A da Costa
- Department of Medical Oncology, AC Camargo Cancer Center, Rua Professor Antonio Prudente 211, São Paulo, CEP: 01509-010, Brazil.
| | - Luisa M do Canto
- CIPE - AC Camargo Cancer Center, São Paulo, Brazil.,Dept of Clinical Genetics, Vejle Hospital, Institute of Regional Health Research, University of Southern Denmark, Vejle, DK, Denmark
| | - Simon Jonas Larsen
- Dept of Mathematics and Computer Science, University of Southern Denmark, Odense, DK, Denmark
| | | | - Carlos Eduardo Stecca
- Department of Medical Oncology, AC Camargo Cancer Center, Rua Professor Antonio Prudente 211, São Paulo, CEP: 01509-010, Brazil
| | - Annabeth Høgh Petersen
- Dept of Mathematics and Computer Science, University of Southern Denmark, Odense, DK, Denmark
| | - Mads M Aagaard
- Dept of Mathematics and Computer Science, University of Southern Denmark, Odense, DK, Denmark
| | - Louise de Brot
- Dept of Pathology, AC Camargo Cancer Center, São Paulo, Brazil
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan Technical University of Munich, Munich, Germany
| | - Glauco Baiocchi
- Dept of Gynecologic Oncology, AC Camargo Cancer Center, São Paulo, Brazil
| | | | - Silvia Regina Rogatto
- Dept of Clinical Genetics, Vejle Hospital, Institute of Regional Health Research, University of Southern Denmark, Vejle, DK, Denmark
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13
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da Costa AABA, do Canto LM, Larsen SJ, Ribeiro ARG, Stecca CE, Petersen AH, Aagaard MM, de Brot L, Baumbach J, Baiocchi G, Achatz MI, Rogatto SR. Genomic profiling in ovarian cancer retreated with platinum based chemotherapy presented homologous recombination deficiency and copy number imbalances of CCNE1 and RB1 genes. BMC Cancer 2019. [PMID: 31060523 DOI: 10.1186/s12885-019-5622-4] [] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Ovarian carcinomas presenting homologous recombination deficiency (HRD), which is observed in about 50% of cases, are more sensitive to platinum and PARP inhibitor therapies. Although platinum resistant disease has a low chance to be responsive to platinum-based chemotherapy, a set of patients is retreated with platinum and some of them are responsive. In this study, we evaluated copy number alterations, HR gene mutations and HR deficiency scores in ovarian cancer patients with prolonged platinum sensitivity. METHODS In this retrospective study (2005 to 2014), we selected 31 patients with platinum resistant ovarian cancer retreated with platinum therapy. Copy number alterations and HR scores were evaluated using the OncoScan® FFPE platform in 15 cases. The mutational profile of 24 genes was investigated by targeted-NGS. RESULTS The median values of the four HRD scores were higher in responders (LOH = 15, LST = 28, tAI = 33, CS = 84) compared with non-responders (LOH = 7.5, LST = 17.5, tAI = 23, CS = 47). Patients with high LOH, LST, tAI and CS scores had better response rates, although these differences were not statistically significant. Response rate to platinum retreatment was 22% in patients with CCNE1 gains and 83.5% in patients with no CCNE1 gains (p = 0.041). Furthermore, response rate was 54.5% in patients with RB1 loss and 25% in patients without RB1 loss (p = 0.569). Patients with CCNE1 gains showed a worse progression free survival (PFS = 11.1 months vs 3.7 months; p = 0.008) and a shorter overall survival (OS = 39.3 months vs 7.1 months; p = 0.007) in comparison with patients with no CCNE1 gains. Patients with RB1 loss had better PFS (9.0 months vs 2.6 months; p = 0.093) and OS (27.4 months vs 3.6 months; p = 0.025) compared with cases with no RB1 loss. Four tumor samples were BRCA mutated and tumor mutations were not associated with response to treatment. CONCLUSIONS HR deficiency was found in 60% of our cases and HRD medium values were higher in responders than in non-responders. Despite the small number of patients tested, CCNE1 gain and RB1 loss discriminate patients with tumors extremely sensitive to platinum retreatment.
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Affiliation(s)
- Alexandre A B A da Costa
- Department of Medical Oncology, AC Camargo Cancer Center, Rua Professor Antonio Prudente 211, São Paulo, CEP: 01509-010, Brazil.
| | - Luisa M do Canto
- CIPE - AC Camargo Cancer Center, São Paulo, Brazil.,Dept of Clinical Genetics, Vejle Hospital, Institute of Regional Health Research, University of Southern Denmark, Vejle, DK, Denmark
| | - Simon Jonas Larsen
- Dept of Mathematics and Computer Science, University of Southern Denmark, Odense, DK, Denmark
| | | | - Carlos Eduardo Stecca
- Department of Medical Oncology, AC Camargo Cancer Center, Rua Professor Antonio Prudente 211, São Paulo, CEP: 01509-010, Brazil
| | - Annabeth Høgh Petersen
- Dept of Mathematics and Computer Science, University of Southern Denmark, Odense, DK, Denmark
| | - Mads M Aagaard
- Dept of Mathematics and Computer Science, University of Southern Denmark, Odense, DK, Denmark
| | - Louise de Brot
- Dept of Pathology, AC Camargo Cancer Center, São Paulo, Brazil
| | - Jan Baumbach
- Chair of Experimental Bioinformatics, TUM School of Life Sciences Weihenstephan Technical University of Munich, Munich, Germany
| | - Glauco Baiocchi
- Dept of Gynecologic Oncology, AC Camargo Cancer Center, São Paulo, Brazil
| | | | - Silvia Regina Rogatto
- Dept of Clinical Genetics, Vejle Hospital, Institute of Regional Health Research, University of Southern Denmark, Vejle, DK, Denmark
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14
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Deng SP, Hu W, Calhoun VD, Wang YP. Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2018; 15:1480-1491. [PMID: 28880187 PMCID: PMC6207076 DOI: 10.1109/tcbb.2017.2748944] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
It's increasingly important but difficult to determine potential biomarkers of schizophrenia (SCZ) disease, owing to the complex pathophysiology of this disease. In this study, a network-fusion based framework was proposed to identify genetic biomarkers of the SCZ disease. A three-step feature selection was applied to single nucleotide polymorphisms (SNPs), DNA methylation, and functional magnetic resonance imaging (fMRI) data to select important features, which were then used to construct two gene networks in different states for the SNPs and DNA methylation data, respectively. Two health networks (one is for SNP data and the other is for DNA methylation data) were combined into one health network from which health minimum spanning trees (MSTs) were extracted. Two disease networks also followed the same procedures. Those genes with significant changes were determined as SCZ biomarkers by comparing MSTs in two different states and they were finally validated from five aspects. The effectiveness of the proposed discovery framework was also demonstrated by comparing with other network-based discovery methods. In summary, our approach provides a general framework for discovering gene biomarkers of the complex diseases by integrating imaging genomic data, which can be applied to the diagnosis of the complex diseases in the future.
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Affiliation(s)
- Su-Ping Deng
- Department of Biomedical Engineering, School of Science and Engineering, Tulane University, New Orleans, LA 70118, USA.,
| | - Wenxing Hu
- Department of Biomedical Engineering, School of Science and Engineering, Tulane University, New Orleans, LA 70118, USA.,
| | | | - Yu-Ping Wang
- Department of Biomedical Engineering, School of Science and Engineering, Tulane University, New Orleans, LA 70118, USA., , Telephone: (504)865-5867, Fax: (504)862-8779
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15
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Wu H, Liu M, Zhuang J. Identification of modules of hepatic encephalopathy based on protein-protein network and gene expression data. Exp Ther Med 2018; 15:4344-4348. [PMID: 29849776 PMCID: PMC5962850 DOI: 10.3892/etm.2018.5924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2017] [Accepted: 01/30/2018] [Indexed: 11/06/2022] Open
Abstract
Hepatic encephalopathy (HE) is regarded as a complication of liver cirrhosis, and 50–75% of patients who have been diagnosed with cirrhosis have HE syndrome. The aim of this study was to identify genes and pathways associated with HE alcoholics. Human protein-protein interactions were downloaded from the STRING database. Gene expression data were downloaded from EMBL-EBI. Combined score and Pearson's correlation coefficient were calculated to construct differential co-expression networks. Graph-theoretical measure was used to calculate the module connectivity dynamic score of multiple differential modules. In total, 11,134 genes were obtained after mapping between probes and genes. Then, 501,736 pairs and 16,496 genes were obtained to form background protein-protein interaction networks, 1,435 edges and 460 nodes were obtained constituting differential co-expression networks. Twenty-three seed genes and 10 significantly differential modules were identified. Four significantly differential modules which had larger connectivity alternation were observed. The identified seed genes and significantly differential modules offer novel understanding and molecular targets for the treatment of HE alcoholics.
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Affiliation(s)
- Hao Wu
- Department of Internal Medicine, Jining Psychiatric Hospital, Jining, Shandong 272051, P.R. China
| | - Miao Liu
- Department of Internal Medicine, Jining Psychiatric Hospital, Jining, Shandong 272051, P.R. China
| | - Jiajun Zhuang
- No. 1 Department of Neurology, Weifang People's Hospital, Weifang, Shandong 261000, P.R. China
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16
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de Vega WC, Erdman L, Vernon SD, Goldenberg A, McGowan PO. Integration of DNA methylation & health scores identifies subtypes in myalgic encephalomyelitis/chronic fatigue syndrome. Epigenomics 2018; 10:539-557. [PMID: 29692205 DOI: 10.2217/epi-2017-0150] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
AIM To identify subtypes in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) based on DNA methylation profiles and health scores. METHODS DNA methylome profiles in immune cells were integrated with symptomatology from 70 women with ME/CFS using similarity network fusion to identify subtypes. RESULTS We discovered four ME/CFS subtypes associated with DNA methylation modifications in 1939 CpG sites, three RAND-36 categories and five DePaul Symptom Questionnaire measures. Methylation patterns of immune response genes and differences in physical functioning and postexertional malaise differentiated the subtypes. CONCLUSION ME/CFS subtypes are associated with specific DNA methylation differences and health symptomatology and provide additional evidence of the potential relevance of metabolic and immune differences in ME/CFS with respect to specific symptoms.
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Affiliation(s)
- Wilfred C de Vega
- Department of Biological Sciences, University of Toronto, Scarborough, Toronto, Ontario, Canada.,Department of Cell & Systems Biology, University of Toronto, Toronto, Ontario, Canada
| | - Lauren Erdman
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Genetics & Genome Biology, SickKids Research Institute, Toronto, Ontario, Canada
| | - Suzanne D Vernon
- The Bateman Horne Center of Excellence, Salt Lake City, UT 84102, USA
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.,Genetics & Genome Biology, SickKids Research Institute, Toronto, Ontario, Canada
| | - Patrick O McGowan
- Department of Biological Sciences, University of Toronto, Scarborough, Toronto, Ontario, Canada.,Department of Cell & Systems Biology, University of Toronto, Toronto, Ontario, Canada.,Department of Psychology, University of Toronto, Toronto, Ontario, Canada.,Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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17
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Gao B, Li G, Liu J, Li Y, Huang X. Identification of driver modules in pan-cancer via coordinating coverage and exclusivity. Oncotarget 2018; 8:36115-36126. [PMID: 28415609 PMCID: PMC5482642 DOI: 10.18632/oncotarget.16433] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2017] [Accepted: 03/13/2017] [Indexed: 12/30/2022] Open
Abstract
It is widely accepted that cancer is driven by accumulated somatic mutations during the lifetime of an individual. Cancer mutations may target relatively small number of cell functional modules. The heterogeneity in different cancer patients makes it difficult to identify driver mutations or functional modules related to cancer. It is biologically desired to be capable of identifying cancer pathway modules through coordination between coverage and exclusivity. There have been a few approaches developed for this purpose, but they all have limitations in practice due to their computational complexity and prediction accuracy. We present a network based approach, CovEx, to predict the specific patient oriented modules by 1) discovering candidate modules for each considered gene, 2) extracting significant candidates by harmonizing coverage and exclusivity and, 3) further selecting the patient oriented modules based on a set cover model. Applying CovEx to pan-cancer datasets spanning 12 cancer types collecting from public database TCGA, it demonstrates significant superiority over the current leading competitors in performance. It is published under GNU GENERAL PUBLIC LICENSE and the source code is available at:https://sourceforge.net/projects/cancer-pathway/files/
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Affiliation(s)
- Bo Gao
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China.,Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72401, USA
| | - Guojun Li
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China.,Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72401, USA
| | - Juntao Liu
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Yang Li
- School of Mathematics, Shandong University, Jinan, Shandong, 250100, China
| | - Xiuzhen Huang
- Department of Computer Science, Arkansas State University, Jonesboro, Arkansas, 72401, USA.,Molecular Biosciences Program, Arkansas State University, Jonesboro, Arkansas, 72401, USA
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18
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Chang CM, Wang ML, Lu KH, Yang YP, Juang CM, Wang PH, Hsu RJ, Yu MH, Chang CC. Integrating the dysregulated inflammasome-based molecular functionome in the malignant transformation of endometriosis-associated ovarian carcinoma. Oncotarget 2017; 9:3704-3726. [PMID: 29423077 PMCID: PMC5790494 DOI: 10.18632/oncotarget.23364] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2017] [Accepted: 10/29/2017] [Indexed: 11/30/2022] Open
Abstract
The coexistence of endometriosis (ES) with ovarian clear cell carcinoma (CCC) or endometrioid carcinoma (EC) suggested that malignant transformation of ES leads to endometriosis associated ovarian carcinoma (EAOC). However, there is still lack of an integrating data analysis of the accumulated experimental data to provide the evidence supporting the hypothesis of EAOC transformation. Herein we used a function-based analytic model with the publicly available microarray datasets to investigate the expression profiling between ES, CCC, and EC. We analyzed the functional regularity pattern of the three type of samples and hierarchically clustered the gene sets to identify key mechanisms regulating the malignant transformation of EAOC. We identified a list of 18 genes (NLRP3, AIM2, PYCARD, NAIP, Caspase-4, Caspase-7, Caspase-8, TLR1, TLR7, TOLLIP, NFKBIA, TNF, TNFAIP3, INFGR2, P2RX7, IL-1B, IL1RL1, IL-18) closely related to inflammasome complex, indicating an important role of inflammation/immunity in EAOC transformation. We next explore the association between these target genes and patient survival using Gene Expression Omnibus (GEO), and found significant correlation between the expression levels of the target genes and the progression-free survival. Interestingly, high expression levels of AIM2 and NLRP3, initiating proteins of inflammasomes, were significantly correlated with poor progression-free survival. Immunohistochemistry staining confirmed a correlation between high AIM2 and high Ki-67 in clinical EAOC samples, supporting its role in disease progression. Collectively, we established a bioinformatic platform of gene-set integrative molecular functionome to dissect the pathogenic pathways of EAOC, and demonstrated a key role of dysregulated inflammasome in modulating the malignant transformation of EAOC.
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Affiliation(s)
- Chia-Ming Chang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Mong-Lien Wang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Kai-Hsi Lu
- Department of Medical Research and Education, Cheng-Hsin Hospital, Taipei, Taiwan
| | - Yi-Ping Yang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chi-Mou Juang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Peng-Hui Wang
- School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taipei, Taiwan.,Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Ren-Jun Hsu
- Graduate Institute of Life Sciences, National Defense Medical Center, Taipei, Taiwan.,Biobank Management Center of Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Mu-Hsien Yu
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Cheng-Chang Chang
- Department of Obstetrics and Gynecology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
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19
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Wu T, Wang Y, Jiang R, Lu X, Tian J. A pathways-based prediction model for classifying breast cancer subtypes. Oncotarget 2017; 8:58809-58822. [PMID: 28938599 PMCID: PMC5601695 DOI: 10.18632/oncotarget.18544] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Accepted: 05/01/2017] [Indexed: 11/25/2022] Open
Abstract
Breast cancer is highly heterogeneous and is classified into four subtypes characterized by specific biological traits, treatment responses, and clinical prognoses. We performed a systemic analysis of 698 breast cancer patient samples from The Cancer Genome Atlas project database. We identified 136 breast cancer genes differentially expressed among the four subtypes. Based on unsupervised clustering analysis, these 136 core genes efficiently categorized breast cancer patients into the appropriate subtypes. Functional enrichment based on Kyoto Encyclopedia of Genes and Genomes analysis identified six functional pathways regulated by these genes: JAK-STAT signaling, basal cell carcinoma, inflammatory mediator regulation of TRP channels, non-small cell lung cancer, glutamatergic synapse, and amyotrophic lateral sclerosis. Three support vector machine (SVM) classification models based on the identified pathways effectively classified different breast cancer subtypes, suggesting that breast cancer subtype-specific risk assessment based on disease pathways could be a potentially valuable approach. Our analysis not only provides insight into breast cancer subtype-specific mechanisms, but also may improve the accuracy of SVM classification models.
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Affiliation(s)
- Tong Wu
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang Province, China
| | - Yunfeng Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Heilongjiang Province, China
| | - Ronghui Jiang
- Department of Surgery, Yanbian No.2 People's Hospital, Jilin Province, China
| | - Xinliang Lu
- Institute of Immunology, Zhejiang University School of Medicine, Zhejiang Province, China
| | - Jiawei Tian
- Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Heilongjiang Province, China
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Staropoli N, Ciliberto D, Chiellino S, Caglioti F, Del Giudice T, Gualtieri S, Salvino A, Strangio A, Botta C, Pignata S, Tassone P, Tagliaferri P. Is ovarian cancer a targetable disease? A systematic review and meta-analysis and genomic data investigation. Oncotarget 2016; 7:82741-82756. [PMID: 27764790 PMCID: PMC5347729 DOI: 10.18632/oncotarget.12633] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Accepted: 09/25/2016] [Indexed: 12/16/2022] Open
Abstract
OBJECTIVES The current gold-standard for the first-line treatment in IIIb/IV stages of epithelial ovarian cancer (EOC) is the combination of carboplatin and paclitaxel plus bevacizumab in some countries. In the era of personalized medicine, there is still uncertainty on the impact of several molecularly targeted agents, which have been investigated for the management of this disease. To shed light on the actual role of targeted therapy in EOC, a systematic review and meta-analysis was performed. METHODS Clinical trials were selected by searching "Pubmed" database and abstracts from major cancer meetings within the time-frame of January 2004-June 2015. The endpoints were survival outcome and response rate (RR). Hazard ratios (HRs) of survival outcomes, with confidence intervals and odds-ratios (ORs) of RR, were extracted from retrieved studies and used for current analysis. Meta-analysis was carried out by random effect model. RESULTS 30 randomized trials for a total of 10,530 patients were selected and included in the final analysis. A benefit in terms of OS (pooled HR 0.915; 95%CI 0.840-0.997; p=0.043), particularly for anti-angiogenetic agents (HR 0.872; 95%CI 0.761-1.000; p=0.049), has been demonstrated for targeted therapy. Moreover, a significant advantage in platinum-resistant subgroup in term of PFS (HR 0.755; 95%CI 0.624-0.912; p=0.004) was found. CONCLUSIONS This systematic review and meta-analysis provide the first evidence that targeted therapy is potentially able to translate into improved survival of EOC patients, with a major role played by anti-angiogenetic drugs. The role of target therapy is underlined in the platinum-resistant setting that represents the "pain in the neck" in EOC management.
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Affiliation(s)
- Nicoletta Staropoli
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Domenico Ciliberto
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Silvia Chiellino
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Francesca Caglioti
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Teresa Del Giudice
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Simona Gualtieri
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Angela Salvino
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Alessandra Strangio
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Cirino Botta
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
| | - Sandro Pignata
- Department of Gynecologic and Urologic Oncology, Fondazione Pascale, National Cancer Institute of Naples, Naples, Italy
| | - Pierfrancesco Tassone
- Department of Experimental and Clinical Medicine, Magna Græcia University, Catanzaro, Italy
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Dauchel H, Lecroq T. Findings from the Section on Bioinformatics and Translational Informatics. Yearb Med Inform 2016:188-192. [PMID: 27830252 DOI: 10.15265/iy-2016-050] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES To summarize excellent current research and propose a selection of best papers published in 2015 in the field of Bioinformatics and Translational Informatics with application in the health domain and clinical care. METHOD We provide a synopsis of the articles selected for the IMIA Yearbook 2016, from which we attempt to derive a synthetic overview of current and future activities in the field. As last year, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor has evaluated separately the set of 1,566 articles and the evaluation results were merged for retaining 14 articles for peer-review. RESULTS The selection and evaluation process of this Yearbook's section on Bioinformatics and Translational Informatics yielded four excellent articles focusing this year on data management of large-scale datasets and genomic medicine that are mainly new method-based papers. Three articles explore the high potential of the re-analysis of previously collected data, here from The Cancer Genome Atlas project (TCGA) and one article presents an original analysis of genomic data from sub-Saharan Africa populations. CONCLUSIONS The current research activities in Bioinformatics and Translational Informatics with application in the health domain continues to explore new algorithms and statistical models to manage and interpret large-scale genomic datasets. From population wide genome sequencing for cataloging genomic variants to the comprehension of functional impact on pathways and molecular interactions regarding a given pathology, making sense of large genomic data requires a necessary effort to address the issue of clinical translation for precise diagnostic and personalized medicine.
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