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Fu J, Yang Q, Luo Y, Zhang S, Tang J, Zhang Y, Zhang H, Xu H, Zhu F. Label-free proteome quantification and evaluation. Brief Bioinform 2023; 24:6833644. [PMID: 36403090 DOI: 10.1093/bib/bbac477] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/24/2022] [Accepted: 10/08/2022] [Indexed: 11/21/2022] Open
Abstract
The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Department of Bioinformatics, Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Song Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hanxiang Xu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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2
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Asim MN, Ibrahim MA, Zehe C, Trygg J, Dengel A, Ahmed S. BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA–miRNA interaction prediction. Interdiscip Sci 2022; 14:841-862. [PMID: 35947255 PMCID: PMC9581873 DOI: 10.1007/s12539-022-00535-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 06/16/2022] [Accepted: 07/12/2022] [Indexed: 11/30/2022]
Abstract
Background and objective: Interactions of long non-coding ribonucleic acids (lncRNAs) with micro-ribonucleic acids (miRNAs) play an essential role in gene regulation, cellular metabolic, and pathological processes. Existing purely sequence based computational approaches lack robustness and efficiency mainly due to the high length variability of lncRNA sequences. Hence, the prime focus of the current study is to find optimal length trade-offs between highly flexible length lncRNA sequences. Method The paper at hand performs in-depth exploration of diverse copy padding, sequence truncation approaches, and presents a novel idea of utilizing only subregions of lncRNA sequences to generate fixed-length lncRNA sequences. Furthermore, it presents a novel bag of tricks-based deep learning approach “Bot-Net” which leverages a single layer long-short-term memory network regularized through DropConnect to capture higher order residue dependencies, pooling to retain most salient features, normalization to prevent exploding and vanishing gradient issues, learning rate decay, and dropout to regularize precise neural network for lncRNA–miRNA interaction prediction. Results BoT-Net outperforms the state-of-the-art lncRNA–miRNA interaction prediction approach by 2%, 8%, and 4% in terms of accuracy, specificity, and matthews correlation coefficient. Furthermore, a case study analysis indicates that BoT-Net also outperforms state-of-the-art lncRNA–protein interaction predictor on a benchmark dataset by accuracy of 10%, sensitivity of 19%, specificity of 6%, precision of 14%, and matthews correlation coefficient of 26%. Conclusion In the benchmark lncRNA–miRNA interaction prediction dataset, the length of the lncRNA sequence varies from 213 residues to 22,743 residues and in the benchmark lncRNA–protein interaction prediction dataset, lncRNA sequences vary from 15 residues to 1504 residues. For such highly flexible length sequences, fixed length generation using copy padding introduces a significant level of bias which makes a large number of lncRNA sequences very much identical to each other and eventually derail classifier generalizeability. Empirical evaluation reveals that within 50 residues of only the starting region of long lncRNA sequences, a highly informative distribution for lncRNA–miRNA interaction prediction is contained, a crucial finding exploited by the proposed BoT-Net approach to optimize the lncRNA fixed length generation process. Availability: BoT-Net web server can be accessed at https://sds_genetic_analysis.opendfki.de/lncmiRNA/. Graphic Abstract ![]()
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Affiliation(s)
- Muhammad Nabeel Asim
- Department of Computer Science, Technical University of Kaiserslautern, 67663, Kaiserslautern, Rhineland-Palatinate, Germany.
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Rhineland-Palatinate, Germany.
| | - Muhammad Ali Ibrahim
- Department of Computer Science, Technical University of Kaiserslautern, 67663, Kaiserslautern, Rhineland-Palatinate, Germany
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Rhineland-Palatinate, Germany
| | - Christoph Zehe
- Sartorius Stedim Cellca GmbH, 88471, Laupheim, Baden-Wurttemberg, Germany
| | - Johan Trygg
- Sartorius Stedim Cellca GmbH, 88471, Laupheim, Baden-Wurttemberg, Germany
- Computational Life Science Cluster (CLiC), Umea University, 90187, Umea, Sweden
| | - Andreas Dengel
- Department of Computer Science, Technical University of Kaiserslautern, 67663, Kaiserslautern, Rhineland-Palatinate, Germany
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Rhineland-Palatinate, Germany
| | - Sheraz Ahmed
- German Research Center for Artificial Intelligence GmbH, 67663, Kaiserslautern, Rhineland-Palatinate, Germany
- Computational Life Science Cluster (CLiC), Umea University, 90187, Umea, Sweden
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3
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Fan Y, Dong X, Li M, Liu P, Zheng J, Li H, Zhang Y. LncRNA KRT19P3 Is Involved in Breast Cancer Cell Proliferation, Migration and Invasion. Front Oncol 2022; 11:799082. [PMID: 35059320 PMCID: PMC8763666 DOI: 10.3389/fonc.2021.799082] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 12/08/2021] [Indexed: 12/13/2022] Open
Abstract
Long non-coding RNAs (LncRNAs) have already been taken as critical regulatory molecules in breast carcinoma (BC). Besides, the progression of BC is closely associated with the immune system. However, the relationship between lncRNAs and the tumor immune system in BC has not been fully studied. LncRNA KRT19P3 has been reported to inhibit the progression of gastric cancer. In the present study, we first discovered that KRT19P3 was downregulated in BC tissues compared with para cancer tissue. Then we showed that KRT19P3 could be used as a marker to differentiate BC from para cancer tissue. Increased expression of KRT19P3 markedly inhibited the proliferation, migration, and invasion rate of BC cells in vitro and tumor growth of BC in vivo. Conversely, KRT19P3 knockdown by siRNA markedly promoted the proliferation, migration, and invasion rate of BC cells after being transfected. Comparison of clinical parameters showed an inverse relationship between the expression of KRT19P3 and pathological grade. Furthermore, immunohistochemistry (IHC) was applied to reveal the positive rate of the expression of Ki-67, programmed death-ligand 1 (PD-L1), and CD8 in BC tissues. Correlation analysis showed that Ki-67 and PD-L1 were inversely proportional to KRT19P3 but CD8 was directly proportional to KRT19P3. In conclusion, this study demonstrated that lncRNA KRT19P3 inhibits BC progression, and may affect the expression of PD-L1 in BC, which in turn affects CD8+ T (CD8 positive Cytotoxic T lymphocyte) cells in the immune microenvironment.
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Affiliation(s)
- Yanping Fan
- Pathology Department, First Affiliated Hospital of Weifang Medical University (Weifang People's Hospital), Weifang, China.,Department of Basic Medicine, Weifang Medical University, Weifang, China
| | - Xiaotong Dong
- Pathology Department, First Affiliated Hospital of Weifang Medical University (Weifang People's Hospital), Weifang, China.,Department of Basic Medicine, Weifang Medical University, Weifang, China
| | - Meizeng Li
- Pathology Department, First Affiliated Hospital of Weifang Medical University (Weifang People's Hospital), Weifang, China.,Department of Basic Medicine, Weifang Medical University, Weifang, China
| | - Pengju Liu
- School of Economics, Qingdao University, Qingdao, China
| | - Jie Zheng
- Department of Basic Medicine, Weifang Medical University, Weifang, China
| | - Hongli Li
- Department of Basic Medicine, Weifang Medical University, Weifang, China
| | - Yunxiang Zhang
- Pathology Department, First Affiliated Hospital of Weifang Medical University (Weifang People's Hospital), Weifang, China
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Fu J, Zhang Y, Liu J, Lian X, Tang J, Zhu F. Pharmacometabonomics: data processing and statistical analysis. Brief Bioinform 2021; 22:6236068. [PMID: 33866355 DOI: 10.1093/bib/bbab138] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 02/09/2021] [Accepted: 03/23/2021] [Indexed: 12/14/2022] Open
Abstract
Individual variations in drug efficacy, side effects and adverse drug reactions are still challenging that cannot be ignored in drug research and development. The aim of pharmacometabonomics is to better understand the pharmacokinetic properties of drugs and monitor the drug effects on specific metabolic pathways. Here, we systematically reviewed the recent technological advances in pharmacometabonomics for better understanding the pathophysiological mechanisms of diseases as well as the metabolic effects of drugs on bodies. First, the advantages and disadvantages of all mainstream analytical techniques were compared. Second, many data processing strategies including filtering, missing value imputation, quality control-based correction, transformation, normalization together with the methods implemented in each step were discussed. Third, various feature selection and feature extraction algorithms commonly applied in pharmacometabonomics were described. Finally, the databases that facilitate current pharmacometabonomics were collected and discussed. All in all, this review provided guidance for researchers engaged in pharmacometabonomics and metabolomics, and it would promote the wide application of metabolomics in drug research and personalized medicine.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Ying Zhang
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Jin Liu
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Xichen Lian
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Jing Tang
- Department of Bioinformatics in Chongqing Medical University, China
| | - Feng Zhu
- College of Pharmaceutical Sciences in Zhejiang University, China
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5
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Chen Z, Shen Z, Zhang Z, Zhao D, Xu L, Zhang L. RNA-Associated Co-expression Network Identifies Novel Biomarkers for Digestive System Cancer. Front Genet 2021; 12:659788. [PMID: 33841514 PMCID: PMC8033200 DOI: 10.3389/fgene.2021.659788] [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: 01/28/2021] [Accepted: 02/25/2021] [Indexed: 01/04/2023] Open
Abstract
Cancers of the digestive system are malignant diseases. Our study focused on colon cancer, esophageal cancer (ESCC), rectal cancer, gastric cancer (GC), and rectosigmoid junction cancer to identify possible biomarkers for these diseases. The transcriptome data were downloaded from the TCGA database (The Cancer Genome Atlas Program), and a network was constructed using the WGCNA algorithm. Two significant modules were found, and coexpression networks were constructed. CytoHubba was used to identify hub genes of the two networks. GO analysis suggested that the network genes were involved in metabolic processes, biological regulation, and membrane and protein binding. KEGG analysis indicated that the significant pathways were the calcium signaling pathway, fatty acid biosynthesis, and pathways in cancer and insulin resistance. Some of the most significant hub genes were hsa-let-7b-3p, hsa-miR-378a-5p, hsa-miR-26a-5p, hsa-miR-382-5p, and hsa-miR-29b-2-5p and SECISBP2 L, NCOA1, HERC1, HIPK3, and MBNL1, respectively. These genes were predicted to be associated with the tumor prognostic reference for this patient population.
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Affiliation(s)
- Zheng Chen
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Zijie Shen
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Zilong Zhang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Da Zhao
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Lijun Zhang
- School of Applied Chemistry and Biological Technology, Shenzhen Polytechnic, Shenzhen, China
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6
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Xu L, Jiao S, Zhang D, Wu S, Zhang H, Gao B. Identification of long noncoding RNAs with machine learning methods: a review. Brief Funct Genomics 2021; 20:174-180. [PMID: 33758917 DOI: 10.1093/bfgp/elab017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 02/24/2021] [Accepted: 02/25/2021] [Indexed: 12/11/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.
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Affiliation(s)
- Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic
| | - Shihu Jiao
- College of Chemistry, Sichuan University, Sichuan, China
| | - Dandan Zhang
- Departments of Obstetrics and Gynecology, First Affiliated Hospital of Harbin Medical University
| | - Song Wu
- Preventive Treatment of Disease Centre of Qinhuangdao Hospital of Traditional Chinese Medicine
| | - Haihong Zhang
- First Affiliated Hospital of Harbin Medical University
| | - Bo Gao
- Second Affiliated Hospital, Harbin Medical University, Harbin, China
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7
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Chen Y, Fu X, Li Z, Peng L, Zhuo L. Prediction of lncRNA-Protein Interactions via the Multiple Information Integration. Front Bioeng Biotechnol 2021; 9:647113. [PMID: 33718346 PMCID: PMC7947871 DOI: 10.3389/fbioe.2021.647113] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 01/19/2021] [Indexed: 01/09/2023] Open
Abstract
The long non-coding RNA (lncRNA)-protein interaction plays an important role in the post-transcriptional gene regulation, such as RNA splicing, translation, signaling, and the development of complex diseases. The related research on the prediction of lncRNA-protein interaction relationship is beneficial in the excavation and the discovery of the mechanism of lncRNA function and action occurrence, which are important. Traditional experimental methods for detecting lncRNA-protein interactions are expensive and time-consuming. Therefore, computational methods provide many effective strategies to deal with this problem. In recent years, most computational methods only use the information of the lncRNA-lncRNA or the protein-protein similarity and cannot fully capture all features to identify their interactions. In this paper, we propose a novel computational model for the lncRNA-protein prediction on the basis of machine learning methods. First, a feature method is proposed for representing the information of the network topological properties of lncRNA and protein interactions. The basic composition feature information and evolutionary information based on protein, the lncRNA sequence feature information, and the lncRNA expression profile information are extracted. Finally, the above feature information is fused, and the optimized feature vector is used with the recursive feature elimination algorithm. The optimized feature vectors are input to the support vector machine (SVM) model. Experimental results show that the proposed method has good effectiveness and accuracy in the lncRNA-protein interaction prediction.
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Affiliation(s)
- Yifan Chen
- College of Information Science and Engineering, Hunan University, Changsha, China
- School of Computer and Information Science, Hunan Institute of Technology, Hengyang, China
| | - Xiangzheng Fu
- College of Information Science and Engineering, Hunan University, Changsha, China
| | - Zejun Li
- School of Computer and Information Science, Hunan Institute of Technology, Hengyang, China
| | - Li Peng
- College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
| | - Linlin Zhuo
- Department of Mathematics and Information Engineering, Wenzhou University Oujiang College, Wenzhou, China
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8
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Zhang X, Ma H, Zou Q, Wu J. Analysis of Cyclin-Dependent Kinase 1 as an Independent Prognostic Factor for Gastric Cancer Based on Statistical Methods. Front Cell Dev Biol 2020; 8:620164. [PMID: 33365314 PMCID: PMC7750425 DOI: 10.3389/fcell.2020.620164] [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: 10/22/2020] [Accepted: 11/03/2020] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE The aim of this study was to investigate the expression of cyclin-dependent kinase 1 (CDK1) in gastric cancer (GC), evaluate its relationship with the clinicopathological features and prognosis of GC, and analyze the advantage of CDK1 as a potential independent prognostic factor for GC. METHODS The Cancer Genome Atlas (TCGA) data and corresponding clinical features of GC were collected. First, the aim gene was selected by combining five topological analysis methods, where the gene expression in paracancerous and GC tissues was analyzed by Limma package and Wilcox test. Second, the correlation between gene expression and clinical features was analyzed by logistic regression. Finally, the survival analysis was carried out by using the Kaplan-Meier. The gene prognostic value was evaluated by univariate and multivariate Cox analyses, and the gene potential biological function was explored by gene set enrichment analysis (GSEA). RESULTS CDK1 was selected as one of the most important genes associated with GC. The expression level of CDK1 in GC tissues was significantly higher than that in paracancerous tissues, which was significantly correlated with pathological stage and grade. The survival rate of the CDK1 high expression group was significantly lower than that of the low expression group. CDK1 expression was significantly correlated with overall survival (OS). CDK1 expression was mainly involved in prostate cancer, small cell lung cancer, and GC and was enriched in the WNT signaling pathway and T cell receptor signaling pathway. CONCLUSION CDK1 may serve as an independent prognostic factor for GC. It is also expected to be a new target for molecular targeted therapy of GC.
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Affiliation(s)
- Xu Zhang
- School of Mathematics and Statistics, Southwest University, Chongqing, China
| | - Hua Ma
- School of Mathematics and Statistics, Southwest University, Chongqing, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
- Hainan Key Laboratory for Computational Science and Application, Hainan Normal University, Haikou, China
| | - Jin Wu
- School of Management, Shenzhen Polytechnic, Shenzhen, China
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Wang W, Guan X, Khan MT, Xiong Y, Wei DQ. LMI-DForest: A deep forest model towards the prediction of lncRNA-miRNA interactions. Comput Biol Chem 2020; 89:107406. [PMID: 33120126 DOI: 10.1016/j.compbiolchem.2020.107406] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 10/12/2020] [Accepted: 10/15/2020] [Indexed: 02/07/2023]
Abstract
The interactions between miRNAs and long non-coding RNAs (lncRNAs) are subject to intensive recent studies due to its critical role in gene regulations. Computational prediction of lncRNA-miRNA interactions has become a popular alternative strategy to the experimental methods for identification of underlying interactions. It is desirable to develop the machine learning-based models for prediction of lncRNA-miRNA based on the experimentally validated interactions between lncRNAs and miRNAs. The accuracy and robustness of existing models based on machine learning techniques are subject to further improvement. Considering that the attributes of lncRNA and miRNA contribute key importance in the interaction between these two RNAs, a deep learning model, named LMI-DForest, is proposed here by combining the deep forest and autoencoder strategies. Systematic comparison on the experiment validated datasets for lncRNA-miRNA interaction datasets demonstrates that the proposed method consistently shows superior performance over the other machine learning models in the lncRNA-miRNA interaction prediction.
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Affiliation(s)
- Wei Wang
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China
| | - Xiaoqing Guan
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Muhammad Tahir Khan
- Institute of Molecular Biology and Biotechnology, The University of Lahore Pakistan, Pakistan
| | - Yi Xiong
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
| | - Dong-Qing Wei
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Peng Cheng Laboratory, Shenzhen, Guangdong, China.
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10
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Jiang Y, Song H, Jiang L, Qiao Y, Yang D, Wang D, Li J. Silybin Prevents Prostate Cancer by Inhibited the ALDH1A1 Expression in the Retinol Metabolism Pathway. Front Cell Dev Biol 2020; 8:574394. [PMID: 32984354 PMCID: PMC7487981 DOI: 10.3389/fcell.2020.574394] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 08/14/2020] [Indexed: 12/31/2022] Open
Abstract
Background Silybin was known to exert inhibition in prostate cancer, but the underlying mechanism remained largely unknown. This study was designed to find out the potential target of Silybin on prostate cancer and explore the relative mechanisms. Methods Firstly, we screened the possible targets of Silybin through the PubChem database and Subpathway – GM. Then DU145 cells were transferred to investigate the correction about related targets, magnetic bead sorting and flow cytometry were used to sort and identify the cells. Proliferation, migration and invasion ability of DU145 cells were detected by MTT assay, Transwell assay, plate clonality and sphere formation assay. BALB/c nude mice were constructed models with implanted sarcoma and measured the tumor volume every 5 days as wells tumor weight. The levels of proteins were detected by Western blot and immunocytochemistry. RT-PCR was selected to test the expression of protein’s mRNA. Results It was screened out the ALDH1A1 was highly correlated with subpathways of the Silybin risk metabolic pathway. And ALDH1A1 expression was positively correlated RARα with Ets1 by interfering with the ALDH1A1 gene. Importantly, ALDH1A1(+) cells showed proliferation, migration and invasion ability. In addition, it showed that Silybin exerted the inhibition on prostate cells by suppressed the proliferation, migration and invasion ability of cells in vitro experiment. Silybin also reduced the tumor volume and weight. And Silybin displayed obviously reduced the proteins and mRNA of ALDH1A1, RARα, Ets1 and MMP9 expressions. Conclusion Our results indicated that Silybin showed inhibition of prostate cancer and the mechanism was involving with downregulating ALDH1A1 expression, thereby inhibiting the activation of RARα and preventing the activation of Ets1 to inhibit the growth and invasion of prostate cancer.
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Affiliation(s)
- Ying Jiang
- College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Hanbing Song
- The First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Ling Jiang
- College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Yu Qiao
- College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Dan Yang
- College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Donghua Wang
- Department of General Surgery, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Ji Li
- College of Basic Medicine, Heilongjiang University of Chinese Medicine, Harbin, China
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11
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Liu Z, Zhang Y, Han X, Li C, Yang X, Gao J, Xie G, Du N. Identifying Cancer-Related lncRNAs Based on a Convolutional Neural Network. Front Cell Dev Biol 2020; 8:637. [PMID: 32850792 PMCID: PMC7432192 DOI: 10.3389/fcell.2020.00637] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 06/24/2020] [Indexed: 12/15/2022] Open
Abstract
Millions of people are suffering from cancers, but accurate early diagnosis and effective treatment are still tough for all doctors. In recent years, long non-coding RNAs (lncRNAs) have been proven to play an important role in diseases, especially cancers. These lncRNAs execute their functions by regulating gene expression. Therefore, identifying lncRNAs which are related to cancers could help researchers gain a deeper understanding of cancer mechanisms and help them find treatment options. A large number of relationships between lncRNAs and cancers have been verified by biological experiments, which give us a chance to use computational methods to identify cancer-related lncRNAs. In this paper, we applied the convolutional neural network (CNN) to identify cancer-related lncRNAs by lncRNA's target genes and their tissue expression specificity. Since lncRNA regulates target gene expression and it has been reported to have tissue expression specificity, their target genes and expression in different tissues were used as features of lncRNAs. Then, the deep belief network (DBN) was used to unsupervised encode features of lncRNAs. Finally, CNN was used to predict cancer-related lncRNAs based on known relationships between lncRNAs and cancers. For each type of cancer, we built a CNN model to predict its related lncRNAs. We identified more related lncRNAs for 41 kinds of cancers. Ten-cross validation has been used to prove the performance of our method. The results showed that our method is better than several previous methods with area under the curve (AUC) 0.81 and area under the precision–recall curve (AUPR) 0.79. To verify the accuracy of our results, case studies have been done.
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Affiliation(s)
- Zihao Liu
- Department of Oncology, Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China.,Department of Oncology, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ying Zhang
- Department of Pharmacy, Heilongjiang Province Land Reclamation Headquarters General Hospital, Harbin, China
| | - Xudong Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chenxi Li
- Department of Oncology, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Xuhui Yang
- Department of Oncology, Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China
| | - Jie Gao
- Department of Oncology, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Ganfeng Xie
- Department of Oncology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Nan Du
- Department of Oncology, Medical School of Chinese PLA, Chinese PLA General Hospital, Beijing, China.,Department of Oncology, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, China
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Tang J, Wang Y, Luo Y, Fu J, Zhang Y, Li Y, Xiao Z, Lou Y, Qiu Y, Zhu F. Computational advances of tumor marker selection and sample classification in cancer proteomics. Comput Struct Biotechnol J 2020; 18:2012-2025. [PMID: 32802273 PMCID: PMC7403885 DOI: 10.1016/j.csbj.2020.07.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 07/06/2020] [Accepted: 07/08/2020] [Indexed: 12/11/2022] Open
Abstract
Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.
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Key Words
- ANN, Artificial Neural Network
- ANOVA, Analysis of Variance
- CFS, Correlation-based Feature Selection
- Cancer proteomics
- Computational methods
- DAPC, Discriminant Analysis of Principal Component
- DT, Decision Trees
- EDA, Estimation of Distribution Algorithm
- FC, Fold Change
- GA, Genetic Algorithms
- GR, Gain Ratio
- HC, Hill Climbing
- HCA, Hierarchical Cluster Analysis
- IG, Information Gain
- LDA, Linear Discriminant Analysis
- LIMMA, Linear Models for Microarray Data
- MBF, Markov Blanket Filter
- MWW, Mann–Whitney–Wilcoxon test
- OPLS-DA, Orthogonal Partial Least Squares Discriminant Analysis
- PCA, Principal Component Analysis
- PLS-DA, Partial Least Square Discriminant Analysis
- RF, Random Forest
- RF-RFE, Random Forest with Recursive Feature Elimination
- SA, Simulated Annealing
- SAM, Significance Analysis of Microarrays
- SBE, Sequential Backward Elimination
- SFS, and Sequential Forward Selection
- SOM, Self-organizing Map
- SU, Symmetrical Uncertainty
- SVM, Support Vector Machine
- SVM-RFE, Support Vector Machine with Recursive Feature Elimination
- Sample classification
- Tumor marker selection
- sPLSDA, Sparse Partial Least Squares Discriminant Analysis
- t-SNE, Student t Distribution
- χ2, Chi-square
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Affiliation(s)
- Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yang Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yi Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziyu Xiao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yan Lou
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Yunqing Qiu
- Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou 310000, China
| | - Feng Zhu
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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Wang C, Zhao N, Sun K, Zhang Y. A Cancer Gene Module Mining Method Based on Bio-Network of Multi-Omics Gene Groups. Front Oncol 2020; 10:1159. [PMID: 32637361 PMCID: PMC7317001 DOI: 10.3389/fonc.2020.01159] [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: 05/18/2020] [Accepted: 06/08/2020] [Indexed: 11/13/2022] Open
Abstract
The initiation, promotion and progression of cancer are highly associated to the environment a human lives in as well as individual genetic factors. In view of the dangers to life and health caused by this abnormally complex systemic disease, many top scientific research institutions around the world have been actively carrying out research in order to discover the pathogenic mechanisms driving cancer occurrence and development. The emergence of high-throughput sequencing technology has greatly advanced oncology research and given rise to the revelation of important oncogenes and the interrelationship among them. Here, we have studied heterogeneous multi-level data within a context of integrated data, and scientifically introduced lncRNA omics data to construct multi-omics bio-network models, allowing the screening of key cancer-related gene groups. We propose a compactness clustering algorithm based on corrected cumulative rank scores, which uses the functional similarity between groups of genes as a distance measure to excavate key gene modules for abnormal regulation contained in gene groups through clustering. We also conducted a survival analysis using our results and found that our model could divide groups of different levels very well. The results also demonstrate that the integration of multi-omics biological data, key gene modules and their dysregulated gene groups can be discovered, which is crucial for cancer research.
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Affiliation(s)
- Chunyu Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Ning Zhao
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Kai Sun
- Thoracic Surgery Department, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
| | - Ying Zhang
- Department of Pharmacy, General Hospital of Heilongjiang Province Land Reclamation Bureau, Harbin, China
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