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Su B, Wang W, Lin X, Liu S, Huang X. Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases. Brief Bioinform 2023; 25:bbad427. [PMID: 38018913 PMCID: PMC10753537 DOI: 10.1093/bib/bbad427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 09/24/2023] [Accepted: 10/30/2023] [Indexed: 11/30/2023] Open
Abstract
MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA-disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases.
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Affiliation(s)
- Benzhe Su
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Weiwei Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Shenglan Liu
- School of Innovation and Entrepreneurship, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Xin Huang
- School of Mathematics and Information Science, Anshan Normal University, Anshan 114007, Liaoning, China
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Su B, Wang X, Ouyang Y, Lin X. DA-SRN: Omics data analysis based on the sample network optimization for complex diseases. Comput Biol Med 2023; 164:107252. [PMID: 37454504 DOI: 10.1016/j.compbiomed.2023.107252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/30/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023]
Abstract
Effective biomarker identification and accurate sample label prediction are still challenging for complex diseases. Patient similarity network (PSN) analysis is a powerful tool in disease omics data analysis. The topology of PSN can reflect the discriminative ability of the corresponding feature space on which the sample network is built. In this study, a novel omics data analysis method based on the sample reference network (DA-SRN) is proposed to identify the potential biomarkers and predict the sample categories. DA-SRN defines the informative features and the sample reference network in optimizing the network structure by genetic algorithm. It labels the samples based on the graph neural network, the reference network and the selected informative features. DA-SRN was compared with nine efficient omics data analysis methods on the genomics, metabolomics and transcriptomics datasets to show its validation. The comparison results showed that it outperformed the other methods in area under receiver operating characteristic curve (AUROC), sensitivity, specificity and area under precision-recall curve (AUPRC) in most cases. Besides, the important metabolites identified by DA-SRN for the type 2 diabetes (T2D) metabolomics data were further examined. The pathway analysis revealed the close relationships between the identified metabolites and the critical metabolic pathways related to the occurrence and development of T2D. The experimental results illustrate that DA-SRN can extract the valuable information from the complex omics data by analyzing the sample relationship, and is promising in biomarker identification and sample discrimination for complex diseases.
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Affiliation(s)
- Benzhe Su
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
| | - Xiaoxiao Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
| | - Yang Ouyang
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, Liaoning, China.
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, Liaoning, China.
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Zhong Z, Li J, Zhong J, Huang Y, Hu J, Zhang P, Zhang B, Jin Y, Luo W, Liu R, Zhang Y, Ling F. MAPKAPK2, a potential dynamic network biomarker of α-synuclein prior to its aggregation in PD patients. NPJ Parkinsons Dis 2023; 9:41. [PMID: 36927756 PMCID: PMC10020541 DOI: 10.1038/s41531-023-00479-z] [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: 08/15/2022] [Accepted: 02/22/2023] [Indexed: 03/18/2023] Open
Abstract
One of the important pathological features of Parkinson's disease (PD) is the pathological aggregation of α-synuclein (α-Syn) in the substantia nigra. Preventing the aggregation of α-Syn has become a potential strategy for treating PD. However, the molecular mechanism of α-Syn aggregation is unclear. In this study, using the dynamic network biomarker (DNB) method, we first identified the critical time point when α-Syn undergoes pathological aggregation based on a SH-SY5Y cell model and found that DNB genes encode transcription factors that regulated target genes that were differentially expressed. Interestingly, we found that these DNB genes and their neighbouring genes were significantly enriched in the cellular senescence pathway and thus proposed that the DNB genes HSF1 and MAPKAPK2 regulate the expression of the neighbouring gene SERPINE1. Notably, in Gene Expression Omnibus (GEO) data obtained from substantia nigra, prefrontal cortex and peripheral blood samples, the expression level of MAPKAPK2 was significantly higher in PD patients than in healthy people, suggesting that MAPKAPK2 has potential as an early diagnostic biomarker of diseases related to pathological aggregation of α-Syn, such as PD. These findings provide new insights into the mechanisms underlying the pathological aggregation of α-Syn.
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Affiliation(s)
- Zhenggang Zhong
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Jiabao Li
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Jiayuan Zhong
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China
| | - Yilin Huang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Jiaqi Hu
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Piao Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Baowen Zhang
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China
| | - Yabin Jin
- The First People's Hospital of Foshan, Sun Yat-sen University, Foshan, China
| | - Wei Luo
- The First People's Hospital of Foshan, Sun Yat-sen University, Foshan, China.
| | - Rui Liu
- School of Mathematics, South China University of Technology, Guangzhou, Guangdong, China.
| | - Yuhu Zhang
- Department of Neurology, Guangdong Neuroscience Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
| | - Fei Ling
- Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong, China.
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Huo Y, Li C, Li Y, Li X, Xu P, Bao Z, Liu W. Detecting early-warning signals for influenza by dysregulated dynamic network biomarkers. Brief Funct Genomics 2023:7036269. [PMID: 36787234 DOI: 10.1093/bfgp/elad006] [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: 10/03/2022] [Revised: 01/15/2023] [Accepted: 01/25/2023] [Indexed: 02/15/2023] Open
Abstract
As a dynamical system, complex disease always has a sudden state transition at the tipping point, which is the result of the long-term accumulation of abnormal regulations. This paper proposes a novel approach to detect the early-warning signals of influenza A (H3N2 and H1N1) outbreaks by dysregulated dynamic network biomarkers (dysregulated DNBs) for individuals. The results of cross-validation show that our approach can detect early-warning signals before the symptom appears successfully. Unlike the traditional DNBs, our dysregulated DNBs are anchored and very few, which is essential for disease early diagnosis in clinical practice. Moreover, the genes of dysregulated DNBs are significantly enriched in the influenza-related pathways. The source code of this paper can be freely downloaded from https://github.com/YanhaoHuo/dysregulated-DNBs.git.
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Affiliation(s)
- Yanhao Huo
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Chuchu Li
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Yujie Li
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
| | - Xianbin Li
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China.,School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Peng Xu
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China.,School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Zhenshen Bao
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China.,School of Computer Science of Information Technology, Qiannan Normal University for Nationalities, Duyun, China
| | - Wenbin Liu
- Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China
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Huang X, Han C, Zhong J, Hu J, Jin Y, Zhang Q, Luo W, Liu R, Ling F. Low expression of the dynamic network markers FOS/JUN in pre-deteriorated epithelial cells is associated with the progression of colorectal adenoma to carcinoma. J Transl Med 2023; 21:45. [PMID: 36698183 PMCID: PMC9875500 DOI: 10.1186/s12967-023-03890-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Accepted: 01/17/2023] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Deterioration of normal intestinal epithelial cells is crucial for colorectal tumorigenesis. However, the process of epithelial cell deterioration and molecular networks that contribute to this process remain unclear. METHODS Single-cell data and clinical information were downloaded from the Gene Expression Omnibus (GEO) database. We used the recently proposed dynamic network biomarker (DNB) method to identify the critical stage of epithelial cell deterioration. Data analysis and visualization were performed using R and Cytoscape software. In addition, Single-Cell rEgulatory Network Inference and Clustering (SCENIC) analysis was used to identify potential transcription factors, and CellChat analysis was conducted to evaluate possible interactions among cell populations. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analysis (GSVA) analyses were also performed. RESULTS The trajectory of epithelial cell deterioration in adenoma to carcinoma progression was delineated, and the subpopulation of pre-deteriorated epithelial cells during colorectal cancer (CRC) initialization was identified at the single-cell level. Additionally, FOS/JUN were identified as biomarkers for pre-deteriorated epithelial cell subpopulations in CRC. Notably, FOS/JUN triggered low expression of P53-regulated downstream pro-apoptotic genes and high expression of anti-apoptotic genes through suppression of P53 expression, which in turn inhibited P53-induced apoptosis. Furthermore, malignant epithelial cells contributed to the progression of pre-deteriorated epithelial cells through the GDF signaling pathway. CONCLUSIONS We demonstrated the trajectory of epithelial cell deterioration and used DNB to characterize pre-deteriorated epithelial cells at the single-cell level. The expression of DNB-neighboring genes and cellular communication were triggered by DNB genes, which may be involved in epithelial cell deterioration. The DNB genes FOS/JUN provide new insights into early intervention in CRC.
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Affiliation(s)
- Xiaoqi Huang
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Chongyin Han
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Jiayuan Zhong
- grid.79703.3a0000 0004 1764 3838School of Mathematics, South China University of Technology, Guangzhou, 510641 China
| | - Jiaqi Hu
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Yabin Jin
- grid.452881.20000 0004 0604 5998Institute of Clinical Research, The First People’s Hospital of Foshan, Foshan, 528000 China
| | - Qinqin Zhang
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
| | - Wei Luo
- grid.452881.20000 0004 0604 5998Institute of Clinical Research, The First People’s Hospital of Foshan, Foshan, 528000 China
| | - Rui Liu
- grid.79703.3a0000 0004 1764 3838School of Mathematics, South China University of Technology, Guangzhou, 510641 China ,grid.513189.7Pazhou Lab, Guangzhou, 510330 China
| | - Fei Ling
- grid.79703.3a0000 0004 1764 3838Guangdong Key Laboratory of Fermentation and Enzyme Engineering, School of Biology and Biological Engineering, South China University of Technology, Guangzhou, 510006 China
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