101
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Lu M, Zhang Y, Yang F, Mai J, Gao Q, Xu X, Kang H, Hou L, Shang Y, Qain Q, Liu J, Jiang M, Zhang H, Bu C, Wang J, Zhang Z, Zhang Z, Zeng J, Li J, Xiao J. TWAS Atlas: a curated knowledgebase of transcriptome-wide association studies. Nucleic Acids Res 2022; 51:D1179-D1187. [PMID: 36243959 PMCID: PMC9825460 DOI: 10.1093/nar/gkac821] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/08/2022] [Accepted: 09/14/2022] [Indexed: 01/30/2023] Open
Abstract
Transcriptome-wide association studies (TWASs), as a practical and prevalent approach for detecting the associations between genetically regulated genes and traits, are now leading to a better understanding of the complex mechanisms of genetic variants in regulating various diseases and traits. Despite the ever-increasing TWAS outputs, there is still a lack of databases curating massive public TWAS information and knowledge. To fill this gap, here we present TWAS Atlas (https://ngdc.cncb.ac.cn/twas/), an integrated knowledgebase of TWAS findings manually curated from extensive literature. In the current implementation, TWAS Atlas collects 401,266 high-quality human gene-trait associations from 200 publications, covering 22,247 genes and 257 traits across 135 tissue types. In particular, an interactive knowledge graph of the collected gene-trait associations is constructed together with single nucleotide polymorphism (SNP)-gene associations to build up comprehensive regulatory networks at multi-omics levels. In addition, TWAS Atlas, as a user-friendly web interface, efficiently enables users to browse, search and download all association information, relevant research metadata and annotation information of interest. Taken together, TWAS Atlas is of great value for promoting the utility and availability of TWAS results in explaining the complex genetic basis as well as providing new insights for human health and disease research.
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Affiliation(s)
| | | | | | | | - Qianwen Gao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaowei Xu
- Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China
| | - Hongyu Kang
- Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China
| | - Li Hou
- Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College, Beijing 100020, China
| | - Yunfei Shang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qiheng Qain
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jie Liu
- North China University of Science and Technology Affiliated Hospital, Tangshan 063000, China
| | - Meiye Jiang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hao Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Congfan Bu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Jinyue Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhewen Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China,CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zaichao Zhang
- Department of Biology, The University of Western Ontario, London, OntarioN6A 5B7, Canada
| | - Jingyao Zeng
- Correspondence may also be addressed to Jingyao Zeng.
| | - Jiao Li
- Correspondence may also be addressed to Jiao Li.
| | - Jingfa Xiao
- To whom correspondence should be addressed. Tel: +86 10 8409 7443; Fax: +86 10 8409 7720;
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102
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Zhang L, Liu M, Zhang Z, Chen D, Chen G, Liu M. Machine learning based identification of hub genes in renal clear cell carcinoma using multi-omics data. Methods 2022; 207:110-117. [PMID: 36179770 DOI: 10.1016/j.ymeth.2022.09.008] [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: 07/25/2022] [Revised: 09/12/2022] [Accepted: 09/24/2022] [Indexed: 11/18/2022] Open
Abstract
Renal cell carcinoma is one of the most universal urinary system cancers in the world. The most common renal cell carcinoma subtype is renal clear cell carcinoma. It is usually associated with high rates of metastasis and mortality. Therefore, finding effective therapeutic targets and prognostic molecular markers is of great significance to improve the early diagnosis rate and prognostic accuracy of renal clear cell carcinoma. In this work, we successfully identified six hub genes that are closely related to the occurrence, development and prognosis of renal clear cell carcinoma and proposed three new potential prognostic markers, namely ATP4B, AC144831.1 and Tfcp2l1 through differentially expressed genes (DEGs) analysis, GO functional enrichment and KEGG pathway analysis, WGCNA analysis, and survival analysis. In addition, we established machine learning models to predict the occurrence of tumors through the gene expression data of patients. It is expected that the results of this study can provide reference value for the treatment of renal clear cell carcinoma.
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Affiliation(s)
- Lichao Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Mingjun Liu
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | - Zhenjiu Zhang
- School of Intelligent Manufacturing and Equipment, Shenzhen Institute of Information Technology, Shenzhen, China
| | | | | | - Mingyang Liu
- Beidahuang Industry Group General Hospital, Harbin, China.
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103
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Jiang L, Liu C, Fan Y, Wu Q, Ye X, Li Q, Wan Y, Sun Y, Zou L, Xiang D, Lv Z. Dynamic transcriptome analysis suggests the key genes regulating seed development and filling in Tartary buckwheat (Fagopyrum tataricum Garetn.). Front Genet 2022; 13:990412. [PMID: 36072657 PMCID: PMC9441574 DOI: 10.3389/fgene.2022.990412] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Tartary buckwheat is highly attractive for the richness of nutrients and quality, yet post-embryonic seed abortion greatly halts the yield. Seed development is crucial for determining grain yield, whereas the molecular basis and regulatory network of Tartary buckwheat seed development and filling is not well understood at present. Here, we assessed the transcriptional dynamics of filling stage Tartary buckwheat seeds at three developmental stages by RNA sequencing. Among the 4249 differentially expressed genes (DEGs), genes related to seed development were identified. Specifically, 88 phytohormone biosynthesis signaling genes, 309 TFs, and 16 expansin genes participating in cell enlargement, 37 structural genes involved in starch biosynthesis represented significant variation and were candidate key seed development genes. Cis-element enrichment analysis indicated that the promoters of differentially expressed expansin genes and starch biosynthesis genes are rich of hormone-responsive (ABA-, AUX-, ET-, and JA-), and seed growth-related (MYB, MYC and WRKY) binding sites. The expansin DEGs showed strong correlations with DEGs in phytohormone pathways and transcription factors (TFs). In total, phytohormone ABA, AUX, ET, BR and CTK, and related TFs could substantially regulate seed development in Tartary buckwheat through targeting downstream expansin genes and structural starch biosynthetic genes. This transcriptome data could provide a theoretical basis for improving yield of Tartary buckwheat.
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Affiliation(s)
- Liangzhen Jiang
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Sichuan Engineering & Technology Research Center of Coarse Cereal Industralization, College of Food and Biological Engineering, Chengdu University, Chengdu, China
| | - Changying Liu
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Sichuan Engineering & Technology Research Center of Coarse Cereal Industralization, College of Food and Biological Engineering, Chengdu University, Chengdu, China
| | - Yu Fan
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Sichuan Engineering & Technology Research Center of Coarse Cereal Industralization, College of Food and Biological Engineering, Chengdu University, Chengdu, China
| | - Qi Wu
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Sichuan Engineering & Technology Research Center of Coarse Cereal Industralization, College of Food and Biological Engineering, Chengdu University, Chengdu, China
| | - Xueling Ye
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Sichuan Engineering & Technology Research Center of Coarse Cereal Industralization, College of Food and Biological Engineering, Chengdu University, Chengdu, China
| | - Qiang Li
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Sichuan Engineering & Technology Research Center of Coarse Cereal Industralization, College of Food and Biological Engineering, Chengdu University, Chengdu, China
| | - Yan Wan
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Sichuan Engineering & Technology Research Center of Coarse Cereal Industralization, College of Food and Biological Engineering, Chengdu University, Chengdu, China
| | - Yanxia Sun
- College of Tourism and Culture Industry, Chengdu University, Chengdu, China
| | - Liang Zou
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Sichuan Engineering & Technology Research Center of Coarse Cereal Industralization, College of Food and Biological Engineering, Chengdu University, Chengdu, China
| | - Dabing Xiang
- Key Laboratory of Coarse Cereal Processing, Ministry of Agriculture and Rural Affairs, Sichuan Engineering & Technology Research Center of Coarse Cereal Industralization, College of Food and Biological Engineering, Chengdu University, Chengdu, China
- *Correspondence: Dabing Xiang, ; Zhibin Lv,
| | - Zhibin Lv
- Department of Medical Instruments and Information, College of Biomedical Engineering, Sichuan University, Chengdu, China
- *Correspondence: Dabing Xiang, ; Zhibin Lv,
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104
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Qiu XY, Wu H, Shao J. TALE-cmap: Protein function prediction based on a TALE-based architecture and the structure information from contact map. Comput Biol Med 2022; 149:105938. [DOI: 10.1016/j.compbiomed.2022.105938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/26/2022] [Accepted: 08/06/2022] [Indexed: 11/03/2022]
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105
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Identify Bitter Peptides by Using Deep Representation Learning Features. Int J Mol Sci 2022; 23:ijms23147877. [PMID: 35887225 PMCID: PMC9315524 DOI: 10.3390/ijms23147877] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/01/2022] [Accepted: 07/14/2022] [Indexed: 02/04/2023] Open
Abstract
A bitter taste often identifies hazardous compounds and it is generally avoided by most animals and humans. Bitterness of hydrolyzed proteins is caused by the presence of bitter peptides. To improve palatability, bitter peptides need to be identified experimentally in a time-consuming and expensive process, before they can be removed or degraded. Here, we report the development of a machine learning prediction method, iBitter-DRLF, which is based on a deep learning pre-trained neural network feature extraction method. It uses three sequence embedding techniques, soft symmetric alignment (SSA), unified representation (UniRep), and bidirectional long short-term memory (BiLSTM). These were initially combined into various machine learning algorithms to build several models. After optimization, the combined features of UniRep and BiLSTM were finally selected, and the model was built in combination with a light gradient boosting machine (LGBM). The results showed that the use of deep representation learning greatly improves the ability of the model to identify bitter peptides, achieving accurate prediction based on peptide sequence data alone. By helping to identify bitter peptides, iBitter-DRLF can help research into improving the palatability of peptide therapeutics and dietary supplements in the future. A webserver is available, too.
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106
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Chen Z, Zhang Z, Wang Z, Zhang Z, Wang Q, Pan Y. Heterozygosity and homozygosity regions affect reproductive success and the loss of reproduction: a case study with litter traits in pigs. Comput Struct Biotechnol J 2022; 20:4060-4071. [PMID: 35983229 PMCID: PMC9364102 DOI: 10.1016/j.csbj.2022.07.039] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 07/21/2022] [Accepted: 07/22/2022] [Indexed: 12/23/2022] Open
Abstract
Runs of heterozygosity (ROHet) and homozygosity (ROH) harbor useful information related to traits of interest. There is a lack of investigating the effect of ROHet and ROH on reproductive success and the loss of reproduction in mammals. Here, we detected and characterized the ROHet and ROH patterns in the genomes of Chinese indigenous pigs (i.e., Jinhua, Chun’an, Longyou Black, and Shengxian Spotted pigs), revealing the similar genetic characteristics of indigenous pigs. Later, we highlighted the underlying litter traits-related ROHet and ROH using association analysis with linear model in these four indigenous pig breeds. To pinpoint the promising candidate genes associated with litter traits, we further in-depth explore the selection patterns of other five pig breeds (i.e., Erhualian, Meishan, Minzhu, Rongchang, and Diqing pigs) with different levels of reproduction performance at the underlying litter traits-related ROHet and ROH using FST and genetic diversity ratio. Then, we identified a set of known and novel candidate genes associated with reproductive performance in pigs. For the novel candidate genes (i.e., CCDC91, SASH1, SAMD5, MACF1, MFSD2A, EPC2, and MBD5), we obtained public available datasets and performed multi-omics analyses integrating transcriptome-wide association studies and comparative single-cell RNA-seq analyses to uncover the roles of them in mammalian reproductive performance. The genes have not been widely reported to be fertility-related genes and can be complementally considered as prior biological information to modify genomic selections models that benefits pig genetic improvement of litter traits. Besides, our findings provide new insights into the function of ROHet and ROH in mammals.
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107
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Lin YC, Liang YJ, Yang HC. Evaluating statistical significance in a meta-analysis by using numerical integration. Comput Struct Biotechnol J 2022; 20:3615-3620. [PMID: 35860413 PMCID: PMC9283883 DOI: 10.1016/j.csbj.2022.06.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/25/2022] [Accepted: 06/25/2022] [Indexed: 11/24/2022] Open
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108
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Zhang Y, Bao W, Cao Y, Cong H, Chen B, Chen Y. A survey on protein–DNA-binding sites in computational biology. Brief Funct Genomics 2022; 21:357-375. [DOI: 10.1093/bfgp/elac009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/07/2022] [Accepted: 04/22/2022] [Indexed: 01/08/2023] Open
Abstract
Abstract
Transcription factors are important cellular components of the process of gene expression control. Transcription factor binding sites are locations where transcription factors specifically recognize DNA sequences, targeting gene-specific regions and recruiting transcription factors or chromatin regulators to fine-tune spatiotemporal gene regulation. As the common proteins, transcription factors play a meaningful role in life-related activities. In the face of the increase in the protein sequence, it is urgent how to predict the structure and function of the protein effectively. At present, protein–DNA-binding site prediction methods are based on traditional machine learning algorithms and deep learning algorithms. In the early stage, we usually used the development method based on traditional machine learning algorithm to predict protein–DNA-binding sites. In recent years, methods based on deep learning to predict protein–DNA-binding sites from sequence data have achieved remarkable success. Various statistical and machine learning methods used to predict the function of DNA-binding proteins have been proposed and continuously improved. Existing deep learning methods for predicting protein–DNA-binding sites can be roughly divided into three categories: convolutional neural network (CNN), recursive neural network (RNN) and hybrid neural network based on CNN–RNN. The purpose of this review is to provide an overview of the computational and experimental methods applied in the field of protein–DNA-binding site prediction today. This paper introduces the methods of traditional machine learning and deep learning in protein–DNA-binding site prediction from the aspects of data processing characteristics of existing learning frameworks and differences between basic learning model frameworks. Our existing methods are relatively simple compared with natural language processing, computational vision, computer graphics and other fields. Therefore, the summary of existing protein–DNA-binding site prediction methods will help researchers better understand this field.
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109
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Daskalakis NP, Meijer OC, Ronald de Kloet E. Mineralocorticoid receptor and glucocorticoid receptor work alone and together in cell-type-specific manner: Implications for resilience prediction and targeted therapy. Neurobiol Stress 2022; 18:100455. [PMID: 35601687 PMCID: PMC9118500 DOI: 10.1016/j.ynstr.2022.100455] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/30/2022] [Accepted: 04/19/2022] [Indexed: 12/24/2022] Open
Abstract
‘You can't roll the clock back and reverse the effects of experiences' Bruce McEwen used to say when explaining how allostasis labels the adaptive process. Here we will for once roll the clock back to the times that the science of the glucocorticoid hormone was honored with a Nobel prize and highlight the discovery of their receptors in the hippocampus as inroad to its current status as master regulator in control of stress coping and adaptation. Glucocorticoids operate in concert with numerous neurotransmitters, neuropeptides, and other hormones with the aim to facilitate processing of information in the neurocircuitry of stress, from anticipation and perception of a novel experience to behavioral adaptation and memory storage. This action, exerted by the glucocorticoids, is guided by two complementary receptor systems, mineralocorticoid receptors (MR) and glucocorticoid receptors (GR), that need to be balanced for a healthy stress response pattern. Here we discuss the cellular, neuroendocrine, and behavioral studies underlying the MR:GR balance concept, highlight the relevance of hypothalamic-pituitary-adrenal (HPA) -axis patterns and note the limited understanding yet of sexual dimorphism in glucocorticoid actions. We conclude with the prospect that (i) genetically and epigenetically regulated receptor variants dictate cell-type-specific transcriptome signatures of stress-related neuropsychiatric symptoms and (ii) selective receptor modulators are becoming available for more targeted treatment. These two new developments may help to ‘restart the clock’ with the prospect to support resilience.
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110
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Zhang S, Jiang H, Gao B, Yang W, Wang G. Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network. Front Cell Dev Biol 2022; 9:811585. [PMID: 35096840 PMCID: PMC8790293 DOI: 10.3389/fcell.2021.811585] [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: 11/09/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Breast cancer is the second largest cancer in the world, the incidence of breast cancer continues to rise worldwide, and women's health is seriously threatened. Therefore, it is very important to explore the characteristic changes of breast cancer from the gene level, including the screening of differentially expressed genes and the identification of diagnostic markers. Methods: The gene expression profiles of breast cancer were obtained from the TCGA database. The edgeR R software package was used to screen the differentially expressed genes between breast cancer patients and normal samples. The function and pathway enrichment analysis of these genes revealed significant enrichment of functions and pathways. Next, download these pathways from KEGG website, extract the gene interaction relations, construct the KEGG pathway gene interaction network. The potential diagnostic markers of breast cancer were obtained by combining the differentially expressed genes with the key genes in the network. Finally, these markers were used to construct the diagnostic prediction model of breast cancer, and the predictive ability of the model and the diagnostic ability of the markers were verified by internal and external data. Results: 1060 differentially expressed genes were identified between breast cancer patients and normal controls. Enrichment analysis revealed 28 significantly enriched pathways (p < 0.05). They were downloaded from KEGG website, and the gene interaction relations were extracted to construct the gene interaction network of KEGG pathway, which contained 1277 nodes and 7345 edges. The key nodes with a degree greater than 30 were extracted from the network, containing 154 genes. These 154 key genes shared 23 genes with differentially expressed genes, which serve as potential diagnostic markers for breast cancer. The 23 genes were used as features to construct the SVM classification model, and the model had good predictive ability in both the training dataset and the validation dataset (AUC = 0.960 and 0.907, respectively). Conclusion: This study showed that the difference of gene expression level is important for the diagnosis of breast cancer, and identified 23 breast cancer diagnostic markers, which provides valuable information for clinical diagnosis and basic treatment experiments.
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Affiliation(s)
- Shumei Zhang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Haoran Jiang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
| | - Bo Gao
- Department of Radiology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Wen Yang
- International Medical Center, Shenzhen University General Hospital, Shenzhen, China
| | - Guohua Wang
- College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
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111
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Gong Y, Zhu W, Sun M, Shi L. Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview. Front Genet 2021; 12:813873. [PMID: 34956340 PMCID: PMC8692768 DOI: 10.3389/fgene.2021.813873] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 11/26/2021] [Indexed: 12/30/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) are usually located in the nucleus and cytoplasm of cells. The transcripts of lncRNAs are >200 nucleotides in length and do not encode proteins. Compared with small RNAs, lncRNAs have longer sequences, more complex spatial structures, and more diverse and complex mechanisms involved in the regulation of gene expression. LncRNAs are widely involved in the biological processes of cells, and in the occurrence and development of many human diseases. Many studies have shown that lncRNAs can induce the occurrence of diseases, and some lncRNAs undergo specific changes in tumor cells. Research into the roles of lncRNAs has covered the diagnosis of, for example, cardiovascular, cerebrovascular, and central nervous system diseases. The bioinformatics of lncRNAs has gradually become a research hotspot and has led to the discovery of a large number of lncRNAs and associated biological functions, and lncRNA databases and recognition models have been developed. In this review, the research progress of lncRNAs is discussed, and lncRNA-related databases and the mechanisms and modes of action of lncRNAs are described. In addition, disease-related lncRNA methods and the relationships between lncRNAs and human lung adenocarcinoma, rectal cancer, colon cancer, heart disease, and diabetes are discussed. Finally, the significance and existing problems of lncRNA research are considered.
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Affiliation(s)
- Yuxin Gong
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China.,Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Haikou, China.,Key Laboratory of Data Science and Smart Education, Hainan Normal University, Ministry of Education, Haikou, China
| | - Wen Zhu
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Meili Sun
- Beidahuang Industry Group General Hospital, Harbin, China
| | - Lei Shi
- Department of Spine Surgery, Changzheng Hospital, Naval Medical University, Shanghai, China
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