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Nie R, Niu W, Tang T, Zhang J, Zhang X. Integrating microRNA expression, miRNA-mRNA regulation network and signal pathway: a novel strategy for lung cancer biomarker discovery. PeerJ 2021; 9:e12369. [PMID: 34754623 PMCID: PMC8552790 DOI: 10.7717/peerj.12369] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/01/2021] [Indexed: 12/17/2022] Open
Abstract
Background Since there are inextricably connections among molecules in the biological networks, it would be a more efficient and accurate research strategy to screen microRNA (miRNA) markers combining with miRNA-mRNA regulatory networks. The independent regulation mode is more “fragile” and “influential” than the co-regulation mode. miRNAs can be used as biomarkers if they can independently regulate hub genes with important roles in the PPI network, simultaneously the expression products of the regulated hub genes play important roles in the signaling pathways of related tissue diseases. Methods We collected miRNA expression of non-small cell lung cancer (NSCLC) from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Volcano plot and signal-to-noise ratio (SNR) methods were used to obtain significant differentially expressed (SDE) miRNAs from the TCGA database and GEO database, respectively. A human miRNA-mRNA regulatory network was constructed and the number of genes uniquely targeted (NOG) by a certain miRNA was calculated. The area under the curve (AUC) values were used to screen for clinical sensitivity and specificity. The candidate markers were obtained using the criteria of the top five maximum AUC values and NOG ≥ 3. The protein–protein interaction (PPI) network was constructed and independently regulated hub genes were obtained. Gene Ontology (GO) analysis and KEGG pathway analysis were used to identify genes involved in cancer-related pathways. Finally, the miRNA which can independently regulate a hub gene and the hub gene can participate in an important cancer-related pathway was considered as a biomarker. The AUC values and gene expression profile analysis from two external GEO datasets as well as literature validation were used to verify the screening capability and reliability of this marker. Results Fifteen SDE miRNAs in lung cancer were obtained from the intersection of volcano plot and SNR based on the GEO database and the TCGA database. Five miRNAs with the top five maximum AUC values and NOG ≥ 3 were screened out. A total of 61 hub genes were obtained from the PPI network. It was found that the hub gene GTF2F2 was independently regulated by miR-708-5p. Further pathway analysis indicated that GTF2F2 participates in protein expression by binding with polymerase II, and it can regulate transcription and accelerate tumor growth. Hence, miR-708-5p could be used as a biomarker. The good screening capability and reliability of miR-708-5p as a lung cancer marker were confirmed by AUC values and gene expression profiling of external datasets, and experimental literature. The potential mechanism of miR-708-5p was proposed. Conclusions This study proposes a new idea for lung cancer marker screening by integrating microRNA expression, regulation network and signal pathway. miR-708-5p was identified as a biomarker using this novel strategy. This study may provide some help for cancer marker screening.
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Affiliation(s)
- Renqing Nie
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Wenling Niu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Tang Tang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jin Zhang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Xiaoyi Zhang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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Lin Y, Wang L, Ge W, Hui Y, Zhou Z, Hu L, Pan H, Huang Y, Shen B. Multi-omics network characterization reveals novel microRNA biomarkers and mechanisms for diagnosis and subtyping of kidney transplant rejection. J Transl Med 2021; 19:346. [PMID: 34389032 PMCID: PMC8361655 DOI: 10.1186/s12967-021-03025-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 08/05/2021] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Kidney transplantation is an optimal method for treatment of end-stage kidney failure. However, kidney transplant rejection (KTR) is commonly observed to have negative effects on allograft function. MicroRNAs (miRNAs) are small non-coding RNAs with regulatory role in KTR genesis, the identification of miRNA biomarkers for accurate diagnosis and subtyping of KTR is therefore of clinical significance for active intervention and personalized therapy. METHODS In this study, an integrative bioinformatics model was developed based on multi-omics network characterization for miRNA biomarker discovery in KTR. Compared with existed methods, the topological importance of miRNA targets was prioritized based on cross-level miRNA-mRNA and protein-protein interaction network analyses. The biomarker potential of identified miRNAs was computationally validated and explored by receiver-operating characteristic (ROC) evaluation and integrated "miRNA-gene-pathway" pathogenic survey. RESULTS Three miRNAs, i.e., miR-145-5p, miR-155-5p, and miR-23b-3p, were screened as putative biomarkers for KTR monitoring. Among them, miR-155-5p was a previously reported signature in KTR, whereas the remaining two were novel candidates both for KTR diagnosis and subtyping. The ROC analysis convinced the power of identified miRNAs as single and combined biomarkers for KTR prediction in kidney tissue and blood samples. Functional analyses, including the latent crosstalk among HLA-related genes, immune signaling pathways and identified miRNAs, provided new insights of these miRNAs in KTR pathogenesis. CONCLUSIONS A network-based bioinformatics approach was proposed and applied to identify candidate miRNA biomarkers for KTR study. Biological and clinical validations are further needed for translational applications of the findings.
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Affiliation(s)
- Yuxin Lin
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000 China
| | - Liangliang Wang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000 China
| | - Wenqing Ge
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000 China
| | - Yu Hui
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000 China
| | - Zheng Zhou
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000 China
| | - Linkun Hu
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000 China
| | - Hao Pan
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000 China
| | - Yuhua Huang
- Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215000 China
| | - Bairong Shen
- Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212 China
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Lin Y, Qian F, Shen L, Chen F, Chen J, Shen B. Computer-aided biomarker discovery for precision medicine: data resources, models and applications. Brief Bioinform 2020; 20:952-975. [PMID: 29194464 DOI: 10.1093/bib/bbx158] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2017] [Revised: 10/17/2017] [Indexed: 12/21/2022] Open
Abstract
Biomarkers are a class of measurable and evaluable indicators with the potential to predict disease initiation and progression. In contrast to disease-associated factors, biomarkers hold the promise to capture the changeable signatures of biological states. With methodological advances, computer-aided biomarker discovery has now become a burgeoning paradigm in the field of biomedical science. In recent years, the 'big data' term has accumulated for the systematical investigation of complex biological phenomena and promoted the flourishing of computational methods for systems-level biomarker screening. Compared with routine wet-lab experiments, bioinformatics approaches are more efficient to decode disease pathogenesis under a holistic framework, which is propitious to identify biomarkers ranging from single molecules to molecular networks for disease diagnosis, prognosis and therapy. In this review, the concept and characteristics of typical biomarker types, e.g. single molecular biomarkers, module/network biomarkers, cross-level biomarkers, etc., are explicated on the guidance of systems biology. Then, publicly available data resources together with some well-constructed biomarker databases and knowledge bases are introduced. Biomarker identification models using mathematical, network and machine learning theories are sequentially discussed. Based on network substructural and functional evidences, a novel bioinformatics model is particularly highlighted for microRNA biomarker discovery. This article aims to give deep insights into the advantages and challenges of current computational approaches for biomarker detection, and to light up the future wisdom toward precision medicine and nation-wide healthcare.
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Affiliation(s)
- Yuxin Lin
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Fuliang Qian
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Li Shen
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Feifei Chen
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
| | - Jiajia Chen
- School of Chemistry, Biology and Material Engineering, Suzhou University of Science and Technology, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, Suzhou, Jiangsu, China
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Yang Y, Xu C, Liu X, Xu C, Zhang Y, Shen L, Vihinen M, Shen B. NDDVD: an integrated and manually curated Neurodegenerative Diseases Variation Database. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:4922736. [PMID: 29688368 PMCID: PMC5841369 DOI: 10.1093/database/bay018] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 01/31/2018] [Indexed: 12/21/2022]
Abstract
Neurodegenerative diseases (NDDs) are associated with genetic variations including point substitutions, copy number alterations, insertions and deletions. At present, a few genetic variation repositories for some individual NDDs have been created, however, these databases are needed to be integrated and expanded to all the NDDs for systems biological investigation. We here build a relational database termed as NDDVD to integrate all the variations of NDDs using Leiden Open Variation Database (LOVD) platform. The items in the NDDVD are collected manually from PubMed or extracted from the existed variation databases. The cross-disease database includes over 6374 genetic variations of 289 genes associated with 37 different NDDs. The patterns, conservations and biological functions for variations in different NDDs are statistically compared and a user-friendly interface is provided for NDDVD at: http://bioinf.suda.edu.cn/NDDvarbase/LOVDv.3.0. URL: http://bioinf.suda.edu.cn/NDDvarbase/LOVDv.3.0
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Affiliation(s)
- Yang Yang
- Center for Systems Biology, Soochow University, No1. Shizi Street, Suzhou, Jiangsu 215006, China.,School of Computer Science and Technology, Soochow University, No1. Shizi Street, Suzhou, Jiangsu 215006, China.,Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden and
| | - Chen Xu
- Center for Systems Biology, Soochow University, No1. Shizi Street, Suzhou, Jiangsu 215006, China
| | - Xingyun Liu
- Center for Systems Biology, Soochow University, No1. Shizi Street, Suzhou, Jiangsu 215006, China
| | - Chao Xu
- Center for Systems Biology, Soochow University, No1. Shizi Street, Suzhou, Jiangsu 215006, China
| | - Yuanyuan Zhang
- Center for Systems Biology, Soochow University, No1. Shizi Street, Suzhou, Jiangsu 215006, China
| | - Li Shen
- Center for Systems Biology, Soochow University, No1. Shizi Street, Suzhou, Jiangsu 215006, China.,Department of Genetics and Systems Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
| | - Mauno Vihinen
- Department of Experimental Medical Science, Lund University, SE-221 84 Lund, Sweden and
| | - Bairong Shen
- Center for Systems Biology, Soochow University, No1. Shizi Street, Suzhou, Jiangsu 215006, China
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Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1028:17-37. [PMID: 29058214 DOI: 10.1007/978-981-10-6041-0_2] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.
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Lin Y, Chen J, Shen B. Interactions Between Genetics, Lifestyle, and Environmental Factors for Healthcare. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1005:167-191. [PMID: 28916933 DOI: 10.1007/978-981-10-5717-5_8] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The occurrence and progression of diseases are strongly associated with a combination of genetic, lifestyle, and environmental factors. Understanding the interplay between genetic and nongenetic components provides deep insights into disease pathogenesis and promotes personalized strategies for people healthcare. Recently, the paradigm of systems medicine, which integrates biomedical data and knowledge at multidimensional levels, is considered to be an optimal way for disease management and clinical decision-making in the era of precision medicine. In this chapter, epigenetic-mediated genetics-lifestyle-environment interactions within specific diseases and different ethnic groups are systematically discussed, and data sources, computational models, and translational platforms for systems medicine research are sequentially presented. Moreover, feasible suggestions on precision healthcare and healthy longevity are kindly proposed based on the comprehensive review of current studies.
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Affiliation(s)
- Yuxin Lin
- Center for Systems Biology, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Jiajia Chen
- School of Chemistry, Biology and Materials Engineering, Suzhou University of Science and Technology, No.1 Kerui road, Suzhou, Jiangsu, 215011, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China. .,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, China. .,Medical College of Guizhou University, Guiyang, 550025, China.
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