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Bonni S, Brindley DN, Chamberlain MD, Daneshvar-Baghbadorani N, Freywald A, Hemmings DG, Hombach-Klonisch S, Klonisch T, Raouf A, Shemanko CS, Topolnitska D, Visser K, Vizeacoumar FJ, Wang E, Gibson SB. Breast Tumor Metastasis and Its Microenvironment: It Takes Both Seed and Soil to Grow a Tumor and Target It for Treatment. Cancers (Basel) 2024; 16:911. [PMID: 38473273 DOI: 10.3390/cancers16050911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/12/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024] Open
Abstract
Metastasis remains a major challenge in treating breast cancer. Breast tumors metastasize to organ-specific locations such as the brain, lungs, and bone, but why some organs are favored over others remains unclear. Breast tumors also show heterogeneity, plasticity, and distinct microenvironments. This contributes to treatment failure and relapse. The interaction of breast cancer cells with their metastatic microenvironment has led to the concept that primary breast cancer cells act as seeds, whereas the metastatic tissue microenvironment (TME) is the soil. Improving our understanding of this interaction could lead to better treatment strategies for metastatic breast cancer. Targeted treatments for different subtypes of breast cancers have improved overall patient survival, even with metastasis. However, these targeted treatments are based upon the biology of the primary tumor and often these patients' relapse, after therapy, with metastatic tumors. The advent of immunotherapy allowed the immune system to target metastatic tumors. Unfortunately, immunotherapy has not been as effective in metastatic breast cancer relative to other cancers with metastases, such as melanoma. This review will describe the heterogeneic nature of breast cancer cells and their microenvironments. The distinct properties of metastatic breast cancer cells and their microenvironments that allow interactions, especially in bone and brain metastasis, will also be described. Finally, we will review immunotherapy approaches to treat metastatic breast tumors and discuss future therapeutic approaches to improve treatments for metastatic breast cancer.
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Affiliation(s)
- Shirin Bonni
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB T2N 4N1, Canada
- The Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - David N Brindley
- Department of Biochemistry, University of Alberta, Edmonton, AB T6G 2H7, Canada
- Cancer Research Institute of Northern Alberta, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - M Dean Chamberlain
- Division of Oncology, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 0W8, Canada
- Saskatchewan Cancer Agency, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Nima Daneshvar-Baghbadorani
- Division of Oncology, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 0W8, Canada
- Saskatchewan Cancer Agency, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Andrew Freywald
- Department of Pathology, Laboratory Medicine, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada
| | - Denise G Hemmings
- Cancer Research Institute of Northern Alberta, University of Alberta, Edmonton, AB T6G 2E1, Canada
- Department of Obstetrics and Gynecology, University of Alberta, Edmonton, AB T6G 2S2, Canada
- Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB T6G 2E1, Canada
- Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - Sabine Hombach-Klonisch
- Department of Human Anatomy and Cell Science, Faculty of Health Sciences, College of Medicine, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Thomas Klonisch
- Department of Human Anatomy and Cell Science, Faculty of Health Sciences, College of Medicine, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Afshin Raouf
- Department of Immunology, Faculty of Medicine, University of Manitoba, Winnipeg, MB R3E OT5, Canada
- Cancer Care Manitoba Research Institute, Cancer Care Manitoba, Winnipeg, MB R3E OV9, Canada
| | - Carrie Simone Shemanko
- The Arnie Charbonneau Cancer Institute, University of Calgary, Calgary, AB T2N 4N1, Canada
- Department of Biological Sciences, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada
| | - Diana Topolnitska
- Department of Immunology, Faculty of Medicine, University of Manitoba, Winnipeg, MB R3E OT5, Canada
- Cancer Care Manitoba Research Institute, Cancer Care Manitoba, Winnipeg, MB R3E OV9, Canada
| | - Kaitlyn Visser
- Cancer Research Institute of Northern Alberta, University of Alberta, Edmonton, AB T6G 2E1, Canada
- Department of Obstetrics and Gynecology, University of Alberta, Edmonton, AB T6G 2S2, Canada
- Department of Medical Microbiology and Immunology, University of Alberta, Edmonton, AB T6G 2E1, Canada
- Li Ka Shing Institute of Virology, University of Alberta, Edmonton, AB T6G 2E1, Canada
| | - Franco J Vizeacoumar
- Division of Oncology, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 0W8, Canada
- Saskatchewan Cancer Agency, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada
| | - Edwin Wang
- Department of Biochemistry and Molecular Biology, Medical Genetics, and Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
| | - Spencer B Gibson
- Department of Oncology, University of Alberta, Edmonton, AB T6G 2R3, Canada
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Pagadala M, Sears TJ, Wu VH, Pérez-Guijarro E, Kim H, Castro A, Talwar JV, Gonzalez-Colin C, Cao S, Schmiedel BJ, Goudarzi S, Kirani D, Au J, Zhang T, Landi T, Salem RM, Morris GP, Harismendy O, Patel SP, Alexandrov LB, Mesirov JP, Zanetti M, Day CP, Fan CC, Thompson WK, Merlino G, Gutkind JS, Vijayanand P, Carter H. Germline modifiers of the tumor immune microenvironment implicate drivers of cancer risk and immunotherapy response. Nat Commun 2023; 14:2744. [PMID: 37173324 PMCID: PMC10182072 DOI: 10.1038/s41467-023-38271-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 04/24/2023] [Indexed: 05/15/2023] Open
Abstract
With the continued promise of immunotherapy for treating cancer, understanding how host genetics contributes to the tumor immune microenvironment (TIME) is essential to tailoring cancer screening and treatment strategies. Here, we study 1084 eQTLs affecting the TIME found through analysis of The Cancer Genome Atlas and literature curation. These TIME eQTLs are enriched in areas of active transcription, and associate with gene expression in specific immune cell subsets, such as macrophages and dendritic cells. Polygenic score models built with TIME eQTLs reproducibly stratify cancer risk, survival and immune checkpoint blockade (ICB) response across independent cohorts. To assess whether an eQTL-informed approach could reveal potential cancer immunotherapy targets, we inhibit CTSS, a gene implicated by cancer risk and ICB response-associated polygenic models; CTSS inhibition results in slowed tumor growth and extended survival in vivo. These results validate the potential of integrating germline variation and TIME characteristics for uncovering potential targets for immunotherapy.
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Affiliation(s)
- Meghana Pagadala
- Biomedical Sciences Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Timothy J Sears
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Victoria H Wu
- Department of Pharmacology, UCSD Moores Cancer Center, La Jolla, CA, 92093, USA
| | - Eva Pérez-Guijarro
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Hyo Kim
- Undergraduate Bioengineering Program, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Andrea Castro
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - James V Talwar
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | | | - Steven Cao
- Division of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, 92093, USA
| | | | | | - Divya Kirani
- Undergraduate Biology and Bioinformatics Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jessica Au
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
| | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Rany M Salem
- Division of Epidemiology, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, 92093, USA
| | - Gerald P Morris
- Department of Pathology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Olivier Harismendy
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, 92093, USA
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego School of Medicine, La Jolla, CA, 92093, USA
| | - Sandip Pravin Patel
- Center for Personalized Cancer Therapy, Division of Hematology and Oncology, UC San Diego Moores Cancer Center, San Diego, CA, 92037, USA
| | - Ludmil B Alexandrov
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, 92093, USA
| | - Jill P Mesirov
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA, 92093, USA
| | - Maurizio Zanetti
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA
- The Laboratory of Immunology and Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Chi-Ping Day
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - Chun Chieh Fan
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, 74136, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, 92093, USA
| | - Wesley K Thompson
- Division of Biostatistics, Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, 92093, USA
| | - Glenn Merlino
- Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health (NIH), Bethesda, MD, 20892, USA
| | - J Silvio Gutkind
- Department of Pharmacology, UCSD Moores Cancer Center, La Jolla, CA, 92093, USA
| | | | - Hannah Carter
- Moores Cancer Center, University of California San Diego, La Jolla, CA, 92093, USA.
- Department of Medicine, Division of Medical Genetics, University of California San Diego, La Jolla, CA, 92093, USA.
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Milanese JS, Marcotte R, Costain WJ, Kablar B, Drouin S. Roles of Skeletal Muscle in Development: A Bioinformatics and Systems Biology Overview. ADVANCES IN ANATOMY, EMBRYOLOGY, AND CELL BIOLOGY 2023; 236:21-55. [PMID: 37955770 DOI: 10.1007/978-3-031-38215-4_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
The ability to assess various cellular events consequent to perturbations, such as genetic mutations, disease states and therapies, has been recently revolutionized by technological advances in multiple "omics" fields. The resulting deluge of information has enabled and necessitated the development of tools required to both process and interpret the data. While of tremendous value to basic researchers, the amount and complexity of the data has made it extremely difficult to manually draw inference and identify factors key to the study objectives. The challenges of data reduction and interpretation are being met by the development of increasingly complex tools that integrate disparate knowledge bases and synthesize coherent models based on current biological understanding. This chapter presents an example of how genomics data can be integrated with biological network analyses to gain further insight into the developmental consequences of genetic perturbations. State of the art methods for conducting similar studies are discussed along with modern methods used to analyze and interpret the data.
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Affiliation(s)
| | - Richard Marcotte
- Human Health Therapeutics, National Research Council of Canada , Montreal, QC, Canada
| | - Willard J Costain
- Human Health Therapeutics, National Research Council of Canada, Ottawa, ON, Canada
| | - Boris Kablar
- Department of Medical Neuroscience, Anatomy and Pathology, Faculty of Medicine, Dalhousie University, Halifax, NS, Canada
| | - Simon Drouin
- Human Health Therapeutics, National Research Council of Canada , Montreal, QC, Canada.
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TGFBR1*6A as a modifier of breast cancer risk and progression: advances and future prospects. NPJ Breast Cancer 2022; 8:84. [PMID: 35853889 PMCID: PMC9296458 DOI: 10.1038/s41523-022-00446-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 06/13/2022] [Indexed: 11/18/2022] Open
Abstract
There is growing evidence that germline mutations in certain genes influence cancer susceptibility, tumor evolution, as well as clinical outcomes. Identification of a disease-causing genetic variant enables testing and diagnosis of at-risk individuals. For breast cancer, several genes such as BRCA1, BRCA2, PALB2, ATM, and CHEK2 act as high- to moderate-penetrance cancer susceptibility genes. Genotyping of these genes informs genetic risk assessment and counseling, as well as treatment and management decisions in the case of high-penetrance genes. TGFBR1*6A (rs11466445) is a common variant of the TGF-β receptor type I (TGFBR1) that has a global minor allelic frequency (MAF) of 0.051 according to the 1000 Genomes Project Consortium. It is emerging as a high frequency, low penetrance tumor susceptibility allele associated with increased cancer risk among several cancer types. The TGFBR1*6A allele has been associated with increased breast cancer risk in women, OR 1.15 (95% CI 1.01–1.31). Functionally, TGFBR1*6A promotes breast cancer cell proliferation, migration, and invasion through the regulation of the ERK pathway and Rho-GTP activation. This review discusses current findings on the genetic, functional, and mechanistic associations between TGFBR1*6A and breast cancer risk and proposes future directions as it relates to genetic association studies and mechanisms of action for tumor growth, metastasis, and immune suppression.
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Hozhabri H, Ghasemi Dehkohneh RS, Razavi SM, Razavi SM, Salarian F, Rasouli A, Azami J, Ghasemi Shiran M, Kardan Z, Farrokhzad N, Mikaeili Namini A, Salari A. Comparative analysis of protein-protein interaction networks in metastatic breast cancer. PLoS One 2022; 17:e0260584. [PMID: 35045088 PMCID: PMC8769308 DOI: 10.1371/journal.pone.0260584] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/12/2021] [Indexed: 12/27/2022] Open
Abstract
Metastatic lesions leading causes of the majority of deaths in patients with the breast cancer. The present study aimed to provide a comprehensive analysis of the differentially expressed genes (DEGs) in the brain (MDA-MB-231 BrM2) and lung (MDA-MB-231 LM2) metastatic cell lines obtained from breast cancer patients compared with those who have primary breast cancer. We identified 981 and 662 DEGs for brain and lung metastasis, respectively. Protein-protein interaction (PPI) analysis revealed seven shared (PLCB1, FPR1, FPR2, CX3CL1, GABBR2, GPR37, and CXCR4) hub genes between brain and lung metastasis in breast cancer. Moreover, GNG2 and CXCL8, C3, and PTPN6 in the brain and SAA1 and CCR5 in lung metastasis were found as unique hub genes. Besides, five co-regulation of clusters via seven important co-expression genes (COL1A2, LUM, SPARC, THBS2, IL1B, CXCL8, THY1) were identified in the brain PPI network. Clusters screening followed by biological process (BP) function and pathway enrichment analysis for both metastatic cell lines showed that complement receptor signalling, acetylcholine receptor signalling, and gastric acid secretion pathways were common between these metastases, whereas other pathways were site-specific. According to our findings, there are a set of genes and functional pathways that mark and mediate breast cancer metastasis to the brain and lungs, which may enable us understand the molecular basis of breast cancer development in a deeper levele to the brain and lungs, which may help us gain a more complete understanding of the molecular underpinnings of breast cancer development.
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Affiliation(s)
- Hossein Hozhabri
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
- Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
| | - Roxana Sadat Ghasemi Dehkohneh
- Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Cell and Molecular Biology and Microbiology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran
| | - Seyed Morteza Razavi
- Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Cell and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - S. Mostafa Razavi
- Department of Chemical, Biomolecular and Corrosion Engineering, The University of Akron, Akron, Ohio, United States of America
| | - Fatemeh Salarian
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Cellular Biotechnology, Cell Science Research Center, Royan Institute for Biotechnology, ACECR, Isfahan, Iran
| | - Azade Rasouli
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Toxicology and Pharmacology, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran
| | - Jalil Azami
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Faculty of Veterinary Medicine, Urmia University, Urmia, Iran
| | - Melika Ghasemi Shiran
- Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Biology, Faculty of Sciences, Central Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Zahra Kardan
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Cellular Molecular Biology, Faculty of Life Science and Biotechnology, Shahid Beheshti University, Tehran, Iran
| | - Negar Farrokhzad
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Biology, Faculty of Sciences, University of Guilan, Rasht, Iran
| | - Arsham Mikaeili Namini
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Animal Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran
| | - Ali Salari
- Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran
- Systems Biology Research Lab, Bioinformatics Group, Systems Biology of the Next Generation Company (SBNGC), Qom, Iran
- Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran
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Gui T, Yao C, Jia B, Shen K. Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods. PLoS One 2021; 16:e0253136. [PMID: 34143800 PMCID: PMC8213194 DOI: 10.1371/journal.pone.0253136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 05/31/2021] [Indexed: 12/24/2022] Open
Abstract
Background Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value. Methods Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data. Results A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically. Conclusion Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.
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Affiliation(s)
- Ting Gui
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Chenhe Yao
- Department of R&D Technology Center, Beijing Zhicheng Biomedical Technology Co, Ltd, Beijing, China
| | - Binghan Jia
- Department of R&D Technology Center, Beijing Zhicheng Biomedical Technology Co, Ltd, Beijing, China
| | - Keng Shen
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
- * E-mail:
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Miklikova S, Trnkova L, Plava J, Bohac M, Kuniakova M, Cihova M. The Role of BRCA1/2-Mutated Tumor Microenvironment in Breast Cancer. Cancers (Basel) 2021; 13:575. [PMID: 33540843 PMCID: PMC7867315 DOI: 10.3390/cancers13030575] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 01/23/2021] [Accepted: 01/29/2021] [Indexed: 12/15/2022] Open
Abstract
Taking into account the factors of high incidence rate, prevalence and mortality, breast cancer represents a crucial social and economic burden. Most cases of breast cancer develop as a consequence of somatic mutations accumulating in mammary epithelial cells throughout lifetime and approximately 5-10% can be ascribed to monogenic predispositions. Even though the role of genetic predispositions in breast cancer is well described in the context of genetics, very little is known about the role of the microenvironment carrying the same aberrant cells impaired by the germline mutation in the breast cancer development and progression. Based on the clinical observations, carcinomas carrying mutations in hereditary tumor-suppressor genes involved in maintaining genome integrity such as BRCA1/2 have worse prognosis and aggressive behavior. One of the mechanisms clarifying the aggressive nature of BRCA-associated tumors implies alterations within the surrounding adipose tissue itself. The objective of this review is to look at the role of BRCA1/2 mutations in the context of breast tumor microenvironment and plausible mechanisms by which it contributes to the aggressive behavior of the tumor cells.
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Affiliation(s)
- Svetlana Miklikova
- Cancer Research Institute, Biomedical Research Center, University Science Park for Biomedicine, Slovak Academy of Sciences, 84505 Bratislava, Slovakia; (S.M.); (L.T.); (J.P.)
| | - Lenka Trnkova
- Cancer Research Institute, Biomedical Research Center, University Science Park for Biomedicine, Slovak Academy of Sciences, 84505 Bratislava, Slovakia; (S.M.); (L.T.); (J.P.)
| | - Jana Plava
- Cancer Research Institute, Biomedical Research Center, University Science Park for Biomedicine, Slovak Academy of Sciences, 84505 Bratislava, Slovakia; (S.M.); (L.T.); (J.P.)
| | - Martin Bohac
- 2nd Department of Oncology, Faculty of Medicine, Comenius University, National Cancer Institute, Klenova 1, 83310 Bratislava, Slovakia;
- Department of Oncosurgery, National Cancer Institute, Klenova 1, 83310 Bratislava, Slovakia
- Regenmed Ltd., Medena 29, 81108 Bratislava, Slovakia
| | - Marcela Kuniakova
- Institute of Medical Biology, Genetics and Clinical Genetics, Faculty of Medicine, Comenius University, Sasinkova 4, 81108 Bratislava, Slovakia;
| | - Marina Cihova
- Cancer Research Institute, Biomedical Research Center, University Science Park for Biomedicine, Slovak Academy of Sciences, 84505 Bratislava, Slovakia; (S.M.); (L.T.); (J.P.)
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8
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Milanese JS, Wang E. Germline Genetics in Cancer: The New Frontier. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11667-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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Liu C, Lai Y, Ying S, Zhan J, Shen Y. Plasma exosome-derived microRNAs expression profiling and bioinformatics analysis under cross-talk between increased low-density lipoprotein cholesterol level and ATP-sensitive potassium channels variant rs1799858. J Transl Med 2020; 18:459. [PMID: 33272292 PMCID: PMC7713329 DOI: 10.1186/s12967-020-02639-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 11/27/2020] [Indexed: 02/07/2023] Open
Abstract
Background Exosome-derived microRNAs (exo-miRs) as messengers play important roles, in the cross-talk between genetic [ATP-sensitive potassium channels (KATP) genetic variant rs1799858] and environmental [elevated serum low-density lipoprotein cholesterol (LDL-C) level] factors, but the plasma exo-miRs expression profile and its role in biological processes from genotype to phenotype remain unclear. Methods A total of 14 subjects with increased LDL-C serum levels (≥ 1.8 mmol/L) were enrolled in the study. The KATP rs1799858 was genotyped by the Sequenom MassARRAY system. The plasma exo-miRs expression profile was identified by next-generation sequencing. Results 64 exo-miRs were significantly differentially expressed (DE), among which 44 exo-miRs were up-regulated and 20 exo-miRs were down-regulated in those subjects carrying T-allele (TT + CT) of rs1799858 compared to those carrying CC genotype. The top 20 up-regulated DE-exo-miRs were miR-378 family, miR-320 family, miR-208 family, miR-483-5p, miR-22-3p, miR-490-3p, miR-6515-5p, miR-31-5p, miR-210-3p, miR-17-3p, miR-6807-5p, miR-497-5p, miR-33a-5p, miR-3611 and miR-126-5p. The top 20 down-regulated DE-exo-miRs were let-7 family, miR-221/222 family, miR-619-5p, miR-6780a-5p, miR-641, miR-200a-5p, miR-581, miR-605-3p, miR-548ar-3p, miR-135a-3p, miR-451b, miR-509-3-5p, miR-4664-3p and miR-224-5p. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently implemented to identify the top 10 DE-exo-miRs related specific target genes and signaling pathways. Only 5 DE-exo-miRs were validated by qRT-PCR as follows: miR-31-5p, miR-378d, miR-619-5p, miR-320a-3p and let-7a-5p (all P < 0.05). Conclusion These results firstly indicated the plasma exo-miRs expression profile bridging the link between genotype (KATP rs1799858) and phenotype (higher LDL-C serum level), these 5 DE-exo-miRs may be potential target intermediates for molecular intervention points.
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Affiliation(s)
- Cheng Liu
- Department of Cardiology, Guangzhou First People's Hospital, South China University of Technology, 1 Panfu Road, Guangzhou, 510180, China.
| | - Yanxian Lai
- Department of Cardiology, Guangzhou First People's Hospital, South China University of Technology, 1 Panfu Road, Guangzhou, 510180, China
| | - Songsong Ying
- Department of Gastroenterology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Junfang Zhan
- Department of Health Management Center, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Yan Shen
- Department of Cardiology, Guangzhou First People's Hospital, South China University of Technology, 1 Panfu Road, Guangzhou, 510180, China
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Huang W, Chen J, Weng W, Xiang Y, Shi H, Shan Y. Development of cancer prognostic signature based on pan-cancer proteomics. Bioengineered 2020; 11:1368-1381. [PMID: 33200655 PMCID: PMC8291886 DOI: 10.1080/21655979.2020.1847398] [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] [Indexed: 12/13/2022] Open
Abstract
Utilizing genomic data to predict cancer prognosis was insufficient. Proteomics can improve our understanding of the etiology and progression of cancer and improve the assessment of cancer prognosis. And the Clinical Proteomic Tumor Analysis Consortium (CPTAC) has generated extensive proteomics data of the vast majority of tumors. Based on CPTAC, we can perform a proteomic pan-carcinoma analysis. We collected the proteomics data and clinical features of cancer patients from CPTAC. Then, we screened 69 differentially expressed proteins (DEPs) with R software in five cancers: hepatocellular carcinoma (HCC), children’s brain tumor tissue consortium (CBTTC), clear cell renal cell carcinoma (CCRC), lung adenocarcinoma (LUAD) and uterine corpus endometrial carcinoma (UCEC). GO and KEGG analysis were performed to clarify the function of these proteins. We also identified their interactions. The DEPs-based prognostic model for predicting over survival was identified by least absolute shrinkage and selection operator (LASSO)-Cox regression model in training cohort. Then, we used the time-dependent receiver operating characteristics analysis to evaluate the ability of the prognostic model to predict overall survival and validated it in validation cohort. The results showed that the DEPs-based prognostic model could accurately and effectively predict the survival rate of most cancers.
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Affiliation(s)
- Weiguo Huang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, Wenzhou Medical University , Wenzhou, China
| | - Jianhui Chen
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, Wenzhou Medical University , Wenzhou, China
| | - Wanqing Weng
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, Wenzhou Medical University , Wenzhou, China
| | - Yukai Xiang
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, Wenzhou Medical University , Wenzhou, China
| | - Hongqi Shi
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, Wenzhou Medical University , Wenzhou, China
| | - Yunfeng Shan
- Department of Hepatobiliary Surgery, The First Affiliated Hospital, Wenzhou Medical University , Wenzhou, China
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11
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Liu X, Hui X, Kang H, Fang Q, Chen A, Hu Y, Lu D, Chen X, Wang Y. A Multi-Gene Model Effectively Predicts the Overall Prognosis of Stomach Adenocarcinomas With Large Genetic Heterogeneity Using Somatic Mutation Features. Front Genet 2020; 11:940. [PMID: 33005171 PMCID: PMC7479248 DOI: 10.3389/fgene.2020.00940] [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: 03/31/2020] [Accepted: 07/28/2020] [Indexed: 12/24/2022] Open
Abstract
Background Stomach adenocarcinoma (STAD) is one of the most common malignancies worldwide with poor prognosis. It remains unclear whether the prognosis is associated with somatic gene mutations. Methods In this research, we collected two independent STAD cohorts with both genetic profiling and clinical follow-up data, systematically investigated the association between the prognosis and somatic mutations, and analyzed the influence of heterogeneity on the prognosis-genetics association. Results Typical association was identified between somatic mutations and overall prognosis for individual cohorts. In The Cancer Genome Atlas (TCGA) cohort, a list of 24 genes was also identified that tended to mutate within cases of the poorest prognosis. The association showed apparent heterogeneity between different cohorts, although common signatures could be identified. A machine-learning model was trained with 20 common genes that showed a similar mutation rate difference between prognostic groups in the two cohorts, and it classified the cases in each cohort into two groups with significantly different prognosis. The model outperformed both single-gene models and TNM-based staging system significantly. Conclusion The study made a systematic analysis on the association between STAD prognosis and somatic mutations, identified signature genes that showed mutation preference in different prognostic groups, and developed an effective multi-gene model that can effectively predict the overall prognosis of STAD in different cohorts.
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Affiliation(s)
- Xianming Liu
- Department of Gastrointestinal Surgery, Shenzhen People's Hospital, The Second Clinical Medical College of Jinan University, Shenzhen, China
| | - Xinjie Hui
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Huayu Kang
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Qiongfang Fang
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Aiyue Chen
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Yueming Hu
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Desheng Lu
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Xianxiong Chen
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
| | - Yejun Wang
- School of Basic Medicine, Shenzhen University Health Science Center, Shenzhen, China
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12
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Germline genomes have a dominant-heritable contribution to cancer immune evasion and immunotherapy response. QUANTITATIVE BIOLOGY 2020. [DOI: 10.1007/s40484-020-0212-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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13
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John A, Qin B, Kalari KR, Wang L, Yu J. Patient-specific multi-omics models and the application in personalized combination therapy. Future Oncol 2020; 16:1737-1750. [PMID: 32462937 DOI: 10.2217/fon-2020-0119] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The rapid advancement of high-throughput technologies and sharp decrease in cost have opened up the possibility to generate large amount of multi-omics data on an individual basis. The development of high-throughput -omics, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics, enables the application of multi-omics technologies in the clinical settings. Combination therapy, defined as disease treatment with two or more drugs to achieve efficacy with lower doses or lower drug toxicity, is the basis for the care of diseases like cancer. Patient-specific multi-omics data integration can help the identification and development of combination therapies. In this review, we provide an overview of different -omics platforms, and discuss the methods for multi-omics, high-throughput, data integration, personalized combination therapy.
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Affiliation(s)
- August John
- Mayo Clinic Graduate School of Biomedical Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Bo Qin
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA.,Gastroenterology Research Unit, Mayo Clinic, Rochester, MN 55905, USA.,Department of Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Krishna R Kalari
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Liewei Wang
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Jia Yu
- Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
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14
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Jiang Z, Cheng P, Luo B, Huang J. Construction and Analysis of a Long Non-Coding RNA-Associated Competing Endogenous RNA Network Identified Potential Prognostic Biomarkers in Luminal Breast Cancer. Onco Targets Ther 2020; 13:4271-4282. [PMID: 32547061 PMCID: PMC7244246 DOI: 10.2147/ott.s240973] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 04/26/2020] [Indexed: 12/24/2022] Open
Abstract
Purpose To construct a competing endogenous RNA (ceRNA) topology network of RNA-seq data and micro RNA-seq (miRNA-seq) data to identify key prognostic long non-coding RNA (lncRNAs) in luminal breast cancer, and validate the results by human luminal breast cancer samples. Materials and Methods The RNA-seq data and miRNA-seq data of luminal A breast cancer in the The Cancer Genome Atlas (TCGA) database were downloaded and compared with those in the miRcode database to obtain lncRNA–miRNA relationship pairs. Final target genes were predicted by all three databases (miRDB, miRTarBase, and TargetScan), thereby obtaining the miRNA-messenger RNA (miRNA-mRNA) relationship pairs and a ceRNA topology network was constructed, then mRNA enrichment analysis, ceRNA topological and stability analysis, univariate and multivariate Cox regression analysis were performed. Overall survival (OS) was evaluated and the key prognostic RNAs were identified. The expression difference between normal and tumor, as well as the correlation of high expression in tumor with pathological parameters (Ki-67, Grade, tumor diameter) were validated by human breast cancer specimens. Results A ceRNA topology network was constructed and six lncRNAs were finally identified (The higher expression of PART1, IGF2.AS, WT1.AS, OIP5.AS1, and SLC25A5.AS1 was associated with poor prognosis while AL035706.1 was adverse) and the poor prognostic ones were higher expressed in tumor tissue and correlated with a higher Ki-67 (>10%), tumor grades (II, III) and tumor diameters (>1.5 cm). Using six lncRNAs, we constructed a prognostic model, which performed well for the classification of prognosis in the module. Conclusion We identified and verified six biomarkers (OS-predicting) in luminal breast cancer, which significantly enriched the prediction and potential targets of this subtype.
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Affiliation(s)
- Zhou Jiang
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Hangzhou, Zhejiang, People's Republic of China
| | - Pu Cheng
- Department of Gynecology, Second Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Hangzhou, Zhejiang, People's Republic of China
| | - Biyuan Luo
- Cancer Center, Xiangya 2nd Hospital, Central South University, Changsha, Hunan, People's Republic of China
| | - Jian Huang
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine; Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Hangzhou, Zhejiang, People's Republic of China
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15
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Alabi N, Sheka D, Siddiqui A, Wang E. Methylation-Based Signatures for Gastroesophageal Tumor Classification. Cancers (Basel) 2020; 12:E1208. [PMID: 32403416 PMCID: PMC7281220 DOI: 10.3390/cancers12051208] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 05/04/2020] [Accepted: 05/08/2020] [Indexed: 12/12/2022] Open
Abstract
Contention exists within the field of oncology with regards to gastroesophageal junction (GEJ) tumors, as in the past, they have been classified as gastric cancer, esophageal cancer, or a combination of both. Misclassifications of GEJ tumors ultimately influence treatment options, which may be rendered ineffective if treating for the wrong cancer attributes. It has been suggested that misclassification rates were as high as 45%, which is greater than reported for junctional cancer occurrences. Here, we aimed to use the methylation profiles of GEJ tumors to improve classifications of GEJ tumors. Four cohorts of DNA methylation profiles, containing ~27,000 (27k) methylation sites per sample, were collected from the Gene Expression Omnibus and The Cancer Genome Atlas. Tumor samples were assigned into discovery (nEC = 185, nGC = 395; EC, esophageal cancer; GC gastric cancer) and validation (nEC = 179, nGC = 369) sets. The optimized Multi-Survival Screening (MSS) algorithm was used to identify methylation biomarkers capable of distinguishing GEJ tumors. Three methylation signatures were identified: They were associated with protein binding, gene expression, and cellular component organization cellular processes, and achieved precision and recall rates of 94.7% and 99.2%, 97.6% and 96.8%, and 96.8% and 97.6%, respectively, in the validation dataset. Interestingly, the methylation sites of the signatures were very close (i.e., 170-270 base pairs) to their downstream transcription start sites (TSSs), suggesting that the methylations near TSSs play much more important roles in tumorigenesis. Here we presented the first set of methylation signatures with a higher predictive power for characterizing gastroesophageal tumors. Thus, they could improve the diagnosis and treatment of gastroesophageal tumors.
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Affiliation(s)
- Nikolay Alabi
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada;
| | - Dropen Sheka
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada;
| | - Ashar Siddiqui
- Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada;
| | - Edwin Wang
- Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 1N4, Canada;
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Guo ZH, You ZH, Huang DS, Yi HC, Zheng K, Chen ZH, Wang YB. MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm. Brief Bioinform 2020; 22:2085-2095. [PMID: 32232320 PMCID: PMC7986599 DOI: 10.1093/bib/bbaa037] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 02/13/2020] [Indexed: 02/03/2023] Open
Abstract
Effectively representing Medical Subject Headings (MeSH) headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify. In this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships), which can be constructed by the tree num. Then, five graph embedding algorithms including DeepWalk, LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed methods, we carried out the node classification and relationship prediction tasks. The results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the representation ability of vectors. Thus, it can serve as an input and continue to play a significant role in any computational models related to disease, drug, microbe, etc. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective.
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Affiliation(s)
| | - Zhu-Hong You
- Corresponding author: Zhu-Hong You, The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China; University of Chinese Academy of Sciences, Beijing 100049, China. Tel: +86-991-367-2967; E-mail:
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Peng LH, Zhou LQ, Chen X, Piao X. A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression. Front Bioeng Biotechnol 2020; 8:40. [PMID: 32117922 PMCID: PMC7015868 DOI: 10.3389/fbioe.2020.00040] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 01/17/2020] [Indexed: 12/11/2022] Open
Abstract
As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model’s average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.
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Affiliation(s)
- Li-Hong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Li-Qian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
| | - Xue Piao
- School of Medical Informatics, Xuzhou Medical University, Xuzhou, China
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