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Mani N, Daiya A, Chowdhury R, Mukherjee S, Chowdhury S. Epigenetic adaptations in drug-tolerant tumor cells. Adv Cancer Res 2023; 158:293-335. [PMID: 36990535 DOI: 10.1016/bs.acr.2022.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Traditional chemotherapy against cancer is often severely hampered by acquired resistance to the drug. Epigenetic alterations and other mechanisms like drug efflux, drug metabolism, and engagement of survival pathways are crucial in evading drug pressure. Herein, growing evidence suggests that a subpopulation of tumor cells can often tolerate drug onslaught by entering a "persister" state with minimal proliferation. The molecular features of these persister cells are gradually unraveling. Notably, the "persisters" act as a cache of cells that can eventually re-populate the tumor post-withdrawal drug pressure and contribute to acquiring stable drug-resistant features. This underlines the clinical significance of the tolerant cells. Accumulating evidence highlights the importance of modulation of the epigenome as a critical adaptive strategy for evading drug pressure. Chromatin remodeling, altered DNA methylation, and de-regulation of non-coding RNA expression and function contribute significantly to this persister state. No wonder targeting adaptive epigenetic modifications is increasingly recognized as an appropriate therapeutic strategy to sensitize them and restore drug sensitivity. Furthermore, manipulating the tumor microenvironment and "drug holiday" is also explored to maneuver the epigenome. However, heterogeneity in adaptive strategies and lack of targeted therapies have significantly hindered the translation of epigenetic therapy to the clinics. In this review, we comprehensively analyze the epigenetic alterations adapted by the drug-tolerant cells, the therapeutic strategies employed to date, and their limitations and future prospects.
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52
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Ha J. SMAP: Similarity-based matrix factorization framework for inferring miRNA-disease association. Knowl Based Syst 2023. [DOI: 10.1016/j.knosys.2023.110295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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53
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Lee K, Hyung D, Cho SY, Yu N, Hong S, Kim J, Kim S, Han JY, Park C. Splicing signature database development to delineate cancer pathways using literature mining and transcriptome machine learning. Comput Struct Biotechnol J 2023; 21:1978-1988. [PMID: 36942103 PMCID: PMC10023904 DOI: 10.1016/j.csbj.2023.02.052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/28/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Alternative splicing (AS) events modulate certain pathways and phenotypic plasticity in cancer. Although previous studies have computationally analyzed splicing events, it is still a challenge to uncover biological functions induced by reliable AS events from tremendous candidates. To provide essential splicing event signatures to assess pathway regulation, we developed a database by collecting two datasets: (i) reported literature and (ii) cancer transcriptome profile. The former includes knowledge-based splicing signatures collected from 63,229 PubMed abstracts using natural language processing, extracted for 202 pathways. The latter is the machine learning-based splicing signatures identified from pan-cancer transcriptome for 16 cancer types and 42 pathways. We established six different learning models to classify pathway activities from splicing profiles as a learning dataset. Top-ranked AS events by learning model feature importance became the signature for each pathway. To validate our learning results, we performed evaluations by (i) performance metrics, (ii) differential AS sets acquired from external datasets, and (iii) our knowledge-based signatures. The area under the receiver operating characteristic values of the learning models did not exhibit any drastic difference. However, random-forest distinctly presented the best performance to compare with the AS sets identified from external datasets and our knowledge-based signatures. Therefore, we used the signatures obtained from the random-forest model. Our database provided the clinical characteristics of the AS signatures, including survival test, molecular subtype, and tumor microenvironment. The regulation by splicing factors was additionally investigated. Our database for developed signatures supported retrieval and visualization system.
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Key Words
- AS, Alternative splicing
- AUCPR, the area under the precision-recall curve
- AUROC, the area under the receiver operating characteristic
- Alternative splicing
- DAS, differential alternative splicing
- Database
- EMT, epithelial mesenchymal transition
- Gene signature
- ML, machine learning
- Machine-learning
- NER, named entity recognition
- NLP, natural language process
- PCA, principal component analysis
- PSI, percent spliced in index
- RF, random-forest
- SF, splicing factor
- TCGA, The Cancer Genome Atlas
- Text-mining
- Tumor transcriptome
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Affiliation(s)
- Kyubin Lee
- Research Institute, National Cancer Center, 232 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Daejin Hyung
- Research Institute, National Cancer Center, 232 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Soo Young Cho
- Department of Molecular & Life Science, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do 15588, Republic of Korea
| | - Namhee Yu
- Research Institute, National Cancer Center, 232 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Sewha Hong
- Research Institute, National Cancer Center, 232 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Jihyun Kim
- Research Institute, National Cancer Center, 232 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
- Department of Precision Medicine, National Institute of Health, Korea Disease Control and Prevention Agency, Osong Health Technology Administration Complex, 187, Osongsaengmyeong 2-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do 28159, Republic of Korea
| | - Sunshin Kim
- Research Institute, National Cancer Center, 232 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Ji-Youn Han
- Research Institute, National Cancer Center, 232 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
| | - Charny Park
- Research Institute, National Cancer Center, 232 Ilsan-ro, Goyang-si, Gyeonggi-do 10408, Republic of Korea
- Correspondence to: 323 Ilsan-ro, Ilsandonggu, Goyang-si, Gyeonggi-do 10408, Republic of Korea.
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54
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Peng S, Liu C, Fan X, Zhu J, Zhang S, Zhou X, Wang T, Gao F, Zhu W. Analysis of aberrant miRNA-mRNA interaction networks in prostate cancer to conjecture its molecular mechanisms. Cancer Biomark 2022; 35:395-407. [PMID: 36373308 DOI: 10.3233/cbm-220051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
BACKGROUND MicroRNAs (miRNAs) capable of post-transcriptionally regulating mRNA expression are essential to tumor occurrence and progression. OBJECTIVE This study aims to find negatively regulatory miRNA-mRNA pairs in prostate adenocarcinoma (PRAD). METHODS Combining The Cancer Genome Atlas (TCGA) RNA-Seq data with Gene Expression Omnibus (GEO) mRNA/miRNA expression profiles, differently expressed miRNA/mRNA (DE-miRNAs/DE-mRNAs) were identified. MiRNA-mRNA pairs were screened by miRTarBase and TarBase, databases collecting experimentally confirmed miRNA-mRNA pairs, and verified in 30 paired prostate specimens by real-time reverse transcription polymerase chain reaction (RT-qPCR). The diagnostic values of miRNA-mRNA pairs were measured by receiver operation characteristic (ROC) curve and Decision Curve Analysis (DCA). DAVID-mirPath database and Connectivity Map were employed in GO/KEGG analysis and compounds research. Interactions between miRNA-mRNA pairs and phenotypic features were analyzed with correlation heatmap in hiplot. RESULTS Based on TCGA RNA-Seq data, 22 miRNA and 14 mRNA GEO datasets, 67 (20 down and 47 up) miRNAs and 351 (139 up and 212 down) mRNAs were selected. After screening from 2 databases, 8 miRNA (up)-mRNA (down) and 7 miRNA (down)-mRNA (up) pairs were identified with Pearson's correlation in TCGA. By external validation, miR-221-3p (down)/GALNT3 (up) and miR-20a-5p (up)/FRMD6 (down) were chosen. The model combing 4 signatures possessed better diagnostic value. These two miRNA-mRNA pairs were significantly connected with immune cells fraction and tumor immune microenvironment. CONCLUSIONS The diagnostic model containing 2 negatively regulatory miRNA-mRNA pairs was established to distinguish PRADs from normal controls.
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Affiliation(s)
- Shuang Peng
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.,Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Cheng Liu
- Department of Gastroenterology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.,Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xingchen Fan
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.,Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Jingfeng Zhu
- Department of Nephrology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Shiyu Zhang
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xin Zhou
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Tongshan Wang
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Feng Gao
- Department of Osteology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Wei Zhu
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
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55
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In Silico and In Vivo Evaluation of microRNA-181c-5p's Role in Hepatocellular Carcinoma. Genes (Basel) 2022; 13:genes13122343. [PMID: 36553610 PMCID: PMC9777864 DOI: 10.3390/genes13122343] [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: 10/29/2022] [Revised: 11/30/2022] [Accepted: 12/09/2022] [Indexed: 12/14/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is a fatal disease, accounting for 75-85% of primary liver cancers. The conclusive research on miR-181c-5p's role in hepatocarcinogenesis, whether it has oncogenic effects or acts as a tumor repressor, is limited and fluctuating. Therefore, the current study aimed to elucidate the role of miR-181c-5p in HCC in silico and in vivo. The bioinformatics analysis of miR-181c-5p expression data in HCC using several databases strongly shed light on its involvement in HCC development, but also confirmed the fluctuating data around its role. miR-181c-5p was proven here to have an oncogenic role by increasing HepG2 cells' viability as confirmed by MTT analysis. In addition, miR-181c-5p was upregulated in the HCC positive control group and progressed the HCC development and malignant features by its forced expression in an HCC mouse model by targeted delivery using a LA-PAMAM polyplex. This is indicated by the cancerous gross and histological features, and the significant increase in liver function biomarkers. The functional enrichment bioinformatics analyses of miR-181c-5p-downregulated targets in HCC indicated that miR-181c-5p targets were significantly enriched in multiple pathways and biological processes involved in HCC development. Fbxl3, an example for miR-181c-5p potential targets, downregulation and its correlation with miR-181c-5p were validated by qPCR. In conclusion, miR-181c-5p is upregulated in HCC and has an oncogenic role enhancing HCC progression.
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56
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SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder. Cells 2022; 11:cells11243984. [PMID: 36552748 PMCID: PMC9776508 DOI: 10.3390/cells11243984] [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: 11/14/2022] [Revised: 11/30/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms.
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57
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The LCNetWork: An electronic representation of the mRNA-lncRNA-miRNA regulatory network underlying mechanisms of non-small cell lung cancer in humans, and its explorative analysis. Comput Biol Chem 2022; 101:107781. [DOI: 10.1016/j.compbiolchem.2022.107781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 10/12/2022] [Accepted: 10/19/2022] [Indexed: 11/18/2022]
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58
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Liu Y, Xie Q, Ma Y, Lin C, Li J, Hu B, Liu C, Zhao Y. Nanobubbles containing PD-L1 Ab and miR-424 mediated PD-L1 blockade, and its expression inhibition to enable and potentiate hepatocellular carcinoma immunotherapy in mice. Int J Pharm 2022; 629:122352. [DOI: 10.1016/j.ijpharm.2022.122352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 10/08/2022] [Accepted: 10/24/2022] [Indexed: 11/11/2022]
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59
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Castro-Mondragon JA, Aure M, Lingjærde O, Langerød A, Martens JWM, Børresen-Dale AL, Kristensen V, Mathelier A. Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of gene regulatory programs in cancers. Nucleic Acids Res 2022; 50:12131-12148. [PMID: 36477895 PMCID: PMC9757053 DOI: 10.1093/nar/gkac1143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 11/03/2022] [Accepted: 11/17/2022] [Indexed: 12/13/2022] Open
Abstract
Most cancer alterations occur in the noncoding portion of the human genome, where regulatory regions control gene expression. The discovery of noncoding mutations altering the cells' regulatory programs has been limited to few examples with high recurrence or high functional impact. Here, we show that transcription factor binding sites (TFBSs) have similar mutation loads to those in protein-coding exons. By combining cancer somatic mutations in TFBSs and expression data for protein-coding and miRNA genes, we evaluate the combined effects of transcriptional and post-transcriptional alterations on the regulatory programs in cancers. The analysis of seven TCGA cohorts culminates with the identification of protein-coding and miRNA genes linked to mutations at TFBSs that are associated with a cascading trans-effect deregulation on the cells' regulatory programs. Our analyses of cis-regulatory mutations associated with miRNAs recurrently predict 12 mature miRNAs (derived from 7 precursors) associated with the deregulation of their target gene networks. The predictions are enriched for cancer-associated protein-coding and miRNA genes and highlight cis-regulatory mutations associated with the dysregulation of key pathways associated with carcinogenesis. By combining transcriptional and post-transcriptional regulation of gene expression, our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients.
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Affiliation(s)
- Jaime A Castro-Mondragon
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
| | - Miriam Ragle Aure
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway
- Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Ole Christian Lingjærde
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Gaustadalléen 23 B, N-0373 Oslo, Norway
- KG Jebsen Centre for B-cell malignancies, Institute for Clinical Medicine, University of Oslo, Ullernchausseen 70, N-0372 Oslo, Norway
| | - Anita Langerød
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway
| | - John W M Martens
- Erasmus MC Cancer Institute and Cancer Genomics Netherlands, University Medical Center Rotterdam, Department of Medical Oncology, 3015GD Rotterdam, The Netherlands
| | - Anne-Lise Børresen-Dale
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway
| | - Vessela N Kristensen
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway
- Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Anthony Mathelier
- Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership, University of Oslo, 0318 Oslo, Norway
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital Radiumhospitalet, 0310 Oslo, Norway
- Department of Medical Genetics, Institute of Clinical Medicine, University of Oslo and Oslo University Hospital, Oslo, Norway
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60
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: experimental results, databases, webservers and data fusion. Brief Bioinform 2022; 23:6696143. [PMID: 36094095 DOI: 10.1093/bib/bbac397] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/19/2022] [Accepted: 08/15/2022] [Indexed: 12/14/2022] Open
Abstract
MicroRNAs (miRNAs) are gene regulators involved in the pathogenesis of complex diseases such as cancers, and thus serve as potential diagnostic markers and therapeutic targets. The prerequisite for designing effective miRNA therapies is accurate discovery of miRNA-disease associations (MDAs), which has attracted substantial research interests during the last 15 years, as reflected by more than 55 000 related entries available on PubMed. Abundant experimental data gathered from the wealth of literature could effectively support the development of computational models for predicting novel associations. In 2017, Chen et al. published the first-ever comprehensive review on MDA prediction, presenting various relevant databases, 20 representative computational models, and suggestions for building more powerful ones. In the current review, as the continuation of the previous study, we revisit miRNA biogenesis, detection techniques and functions; summarize recent experimental findings related to common miRNA-associated diseases; introduce recent updates of miRNA-relevant databases and novel database releases since 2017, present mainstream webservers and new webserver releases since 2017 and finally elaborate on how fusion of diverse data sources has contributed to accurate MDA prediction.
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Affiliation(s)
- Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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61
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: towards systematic evaluation of computational models. Brief Bioinform 2022; 23:6712303. [PMID: 36151749 DOI: 10.1093/bib/bbac407] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 08/11/2022] [Accepted: 08/20/2022] [Indexed: 12/14/2022] Open
Abstract
Currently, there exist no generally accepted strategies of evaluating computational models for microRNA-disease associations (MDAs). Though K-fold cross validations and case studies seem to be must-have procedures, the value of K, the evaluation metrics, and the choice of query diseases as well as the inclusion of other procedures (such as parameter sensitivity tests, ablation studies and computational cost reports) are all determined on a case-by-case basis and depending on the researchers' choices. In the current review, we include a comprehensive analysis on how 29 state-of-the-art models for predicting MDAs were evaluated. Based on the analytical results, we recommend a feasible evaluation workflow that would suit any future model to facilitate fair and systematic assessment of predictive performance.
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Affiliation(s)
- Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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62
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Yan C, Ding C, Duan G. PMMS: Predicting essential miRNAs based on multi-head self-attention mechanism and sequences. Front Med (Lausanne) 2022; 9:1015278. [DOI: 10.3389/fmed.2022.1015278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022] Open
Abstract
Increasing evidence has proved that miRNA plays a significant role in biological progress. In order to understand the etiology and mechanisms of various diseases, it is necessary to identify the essential miRNAs. However, it is time-consuming and expensive to identify essential miRNAs by using traditional biological experiments. It is critical to develop computational methods to predict potential essential miRNAs. In this study, we provided a new computational method (called PMMS) to identify essential miRNAs by using multi-head self-attention and sequences. First, PMMS computes the statistic and structure features and extracts the static feature by concatenating them. Second, PMMS extracts the deep learning original feature (BiLSTM-based feature) by using bi-directional long short-term memory (BiLSTM) and pre-miRNA sequences. In addition, we further obtained the multi-head self-attention feature (MS-based feature) based on BiLSTM-based feature and multi-head self-attention mechanism. By considering the importance of the subsequence of pre-miRNA to the static feature of miRNA, we obtained the deep learning final feature (WA-based feature) based on the weighted attention mechanism. Finally, we concatenated WA-based feature and static feature as an input to the multilayer perceptron) model to predict essential miRNAs. We conducted five-fold cross-validation to evaluate the prediction performance of PMMS. The areas under the ROC curves (AUC), the F1-score, and accuracy (ACC) are used as performance metrics. From the experimental results, PMMS obtained best prediction performances (AUC: 0.9556, F1-score: 0.9030, and ACC: 0.9097). It also outperformed other compared methods. The experimental results also illustrated that PMMS is an effective method to identify essential miRNA.
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63
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Ilieva M, Panella R, Uchida S. MicroRNAs in Cancer and Cardiovascular Disease. Cells 2022; 11:3551. [PMID: 36428980 PMCID: PMC9688578 DOI: 10.3390/cells11223551] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 11/12/2022] Open
Abstract
Although cardiac tumor formation is rare, accumulating evidence suggests that the two leading causes of deaths, cancers, and cardiovascular diseases are similar in terms of pathogenesis, including angiogenesis, immune responses, and fibrosis. These similarities have led to the creation of new exciting field of study called cardio-oncology. Here, we review the similarities between cancer and cardiovascular disease from the perspective of microRNAs (miRNAs). As miRNAs are well-known regulators of translation by binding to the 3'-untranslated regions (UTRs) of messenger RNAs (mRNAs), we carefully dissect how a specific set of miRNAs are both oncomiRs (miRNAs in cancer) and myomiRs (muscle-related miRNAs). Furthermore, from the standpoint of similar pathogenesis, miRNAs categories related to the similar pathogenesis are discussed; namely, angiomiRs, Immune-miRs, and fibromiRs.
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Affiliation(s)
| | | | - Shizuka Uchida
- Center for RNA Medicine, Department of Clinical Medicine, Aalborg University, DK-2450 Copenhagen SV, Denmark
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64
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CmirC: an integrated database of clustered miRNAs co-localized with copy number variations in cancer. Funct Integr Genomics 2022; 22:1229-1241. [DOI: 10.1007/s10142-022-00909-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/08/2022] [Accepted: 10/14/2022] [Indexed: 11/25/2022]
Abstract
AbstractGenomic rearrangements and copy number variations (CNVs) are the major regulators of clustered microRNAs (miRNAs) expression. Several clustered miRNAs are harbored in and around chromosome fragile sites (CFSs) and cancer-associated genomic hotspots. Aberrant expression of such clusters can lead to oncogenic or tumor suppressor activities. Here, we developed CmirC (Clustered miRNAs co-localized with CNVs), a comprehensive database of clustered miRNAs co-localized with CNV regions. The database consists of 481 clustered miRNAs co-localized with CNVs and their expression patterns in 35 cancer types of the TCGA. The portal also provides information on CFSs, miRNA cluster candidates, genomic coordinates, target gene networks, and gene functionality. The web portal is integrated with advanced tools such as JBrowse, NCBI-BLAST, GeneSCF, visNetwork, and NetworkD3 to help the researchers in data analysis, visualization, and browsing. This portal provides a promising avenue for integrated data analytics and offers additional evidence for the complex regulation of clustered miRNAs in cancer. The web portal is freely accessible at http://slsdb.manipal.edu/cmirclust to explore clinically significant miRNAs.
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65
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Makler A, Narayanan R, Asghar W. An Exosomal miRNA Biomarker for the Detection of Pancreatic Ductal Adenocarcinoma. BIOSENSORS 2022; 12:831. [PMID: 36290970 PMCID: PMC9599289 DOI: 10.3390/bios12100831] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/25/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) remains a difficult tumor to diagnose and treat. To date, PDAC lacks routine screening with no markers available for early detection. Exosomes are 40-150 nm-sized extracellular vesicles that contain DNA, RNA, and proteins. These exosomes are released by all cell types into circulation and thus can be harvested from patient body fluids, thereby facilitating a non-invasive method for PDAC detection. A bioinformatics analysis was conducted utilizing publicly available miRNA pancreatic cancer expression and genome databases. Through this analysis, we identified 18 miRNA with strong potential for PDAC detection. From this analysis, 10 (MIR31, MIR93, MIR133A1, MIR210, MIR330, MIR339, MIR425, MIR429, MIR1208, and MIR3620) were chosen due to high copy number variation as well as their potential to differentiate patients with chronic pancreatitis, neoplasms, and PDAC. These 10 were examined for their mature miRNA expression patterns, giving rise to 18 mature miRs for further analysis. Exosomal RNA from cell culture media was analyzed via RTqPCR and seven mature miRs exhibited statistical significance (miR-31-5p, miR-31-3p, miR-210-3p, miR-339-5p, miR-425-5p, miR-425-3p, and miR-429). These identified biomarkers can potentially be used for early detection of PDAC.
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Affiliation(s)
- Amy Makler
- Micro and Nanotechnology in Medicine, College of Engineering and Computer Science, Boca Raton, FL 33431, USA
- Department of Biomedical Science, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biological Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
| | - Ramaswamy Narayanan
- Department of Biological Sciences, Florida Atlantic University, Boca Raton, FL 33431, USA
- Department of Biology, University of North Florida, Jacksonville, FL 32224, USA
| | - Waseem Asghar
- Micro and Nanotechnology in Medicine, College of Engineering and Computer Science, Boca Raton, FL 33431, USA
- Department of Computer & Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA
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66
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Arshinchi Bonab R, Asfa S, Kontou P, Karakülah G, Pavlopoulou A. Identification of neoplasm-specific signatures of miRNA interactions by employing a systems biology approach. PeerJ 2022; 10:e14149. [PMID: 36213495 PMCID: PMC9536303 DOI: 10.7717/peerj.14149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 09/07/2022] [Indexed: 01/21/2023] Open
Abstract
MicroRNAs represent major regulatory components of the disease epigenome and they constitute powerful biomarkers for the accurate diagnosis and prognosis of various diseases, including cancers. The advent of high-throughput technologies facilitated the generation of a vast amount of miRNA-cancer association data. Computational approaches have been utilized widely to effectively analyze and interpret these data towards the identification of miRNA signatures for diverse types of cancers. Herein, a novel computational workflow was applied to discover core sets of miRNA interactions for the major groups of neoplastic diseases by employing network-based methods. To this end, miRNA-cancer association data from four comprehensive publicly available resources were utilized for constructing miRNA-centered networks for each major group of neoplasms. The corresponding miRNA-miRNA interactions were inferred based on shared functionally related target genes. The topological attributes of the generated networks were investigated in order to detect clusters of highly interconnected miRNAs that form core modules in each network. Those modules that exhibited the highest degree of mutual exclusivity were selected from each graph. In this way, neoplasm-specific miRNA modules were identified that could represent potential signatures for the corresponding diseases.
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Affiliation(s)
- Reza Arshinchi Bonab
- Izmir International Biomedicine and Genome Institute, Dokuz Eylül University, Izmir, Turkey,Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Seyedehsadaf Asfa
- Izmir International Biomedicine and Genome Institute, Dokuz Eylül University, Izmir, Turkey,Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Panagiota Kontou
- Department of Mathematics, University of Thessaly, Lamia, Greece
| | - Gökhan Karakülah
- Izmir International Biomedicine and Genome Institute, Dokuz Eylül University, Izmir, Turkey,Izmir Biomedicine and Genome Center, Izmir, Turkey
| | - Athanasia Pavlopoulou
- Izmir International Biomedicine and Genome Institute, Dokuz Eylül University, Izmir, Turkey,Izmir Biomedicine and Genome Center, Izmir, Turkey
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67
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Bang D, Gu J, Park J, Jeong D, Koo B, Yi J, Shin J, Jung I, Kim S, Lee S. A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective. Int J Mol Sci 2022; 23:ijms231911498. [PMID: 36232792 PMCID: PMC9570358 DOI: 10.3390/ijms231911498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 09/18/2022] [Accepted: 09/26/2022] [Indexed: 02/01/2023] Open
Abstract
Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.
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Affiliation(s)
- Dongmin Bang
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Jeonghyeon Gu
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
| | - Joonhyeong Park
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Korea
| | - Dabin Jeong
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Bonil Koo
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Jungseob Yi
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
| | - Jihye Shin
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
| | - Inuk Jung
- Department of Computer Science and Engineering, Kyungpook National University, Daegu 41566, Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
- Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul 08826, Korea
- Department of Computer Science and Engineering, Seoul National University, Seoul 08826, Korea
- MOGAM Institute for Biomedical Research, Yongin-si 16924, Korea
| | - Sunho Lee
- AIGENDRUG Co., Ltd., Seoul 08826, Korea
- Correspondence:
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68
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A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery. MEDICAL ONCOLOGY (NORTHWOOD, LONDON, ENGLAND) 2022; 39:206. [PMID: 36175717 DOI: 10.1007/s12032-022-01815-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 07/29/2022] [Indexed: 10/14/2022]
Abstract
Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.
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69
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Chen J, Lin J, Hu Y, Ye M, Yao L, Wu L, Zhang W, Wang M, Deng T, Guo F, Huang Y, Zhu B, Wang D. RNADisease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction. Nucleic Acids Res 2022; 51:D1397-D1404. [PMID: 36134718 PMCID: PMC9825423 DOI: 10.1093/nar/gkac814] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/06/2022] [Accepted: 09/09/2022] [Indexed: 02/06/2023] Open
Abstract
Numerous studies have shown that RNA plays an important role in the occurrence and development of diseases, and RNA-disease associations are not limited to noncoding RNAs in mammals but also exist for protein-coding RNAs. Furthermore, RNA-associated diseases are found across species including plants and nonmammals. To better analyze diseases at the RNA level and facilitate researchers in exploring the pathogenic mechanism of diseases, we decided to update and change MNDR v3.0 to RNADisease v4.0, a repository for RNA-disease association (http://www.rnadisease.org/ or http://www.rna-society.org/mndr/). Compared to the previous version, new features include: (i) expanded data sources and categories of species, RNA types, and diseases; (ii) the addition of a comprehensive analysis of RNAs from thousands of high-throughput sequencing data of cancer samples and normal samples; (iii) the addition of an RNA-disease enrichment tool and (iv) the addition of four RNA-disease prediction tools. In summary, RNADisease v4.0 provides a comprehensive and concise data resource of RNA-disease associations which contains a total of 3 428 058 RNA-disease entries covering 18 RNA types, 117 species and 4090 diseases to meet the needs of biological research and lay the foundation for future therapeutic applications of diseases.
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Affiliation(s)
| | | | | | | | | | - Le Wu
- Department of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Wenhai Zhang
- Department of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Meiyi Wang
- Department of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tingting Deng
- Department of Bioinformatics, Guangdong Province Key Laboratory of Molecular Tumor Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Feng Guo
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Yan Huang
- Cancer Research Institute, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Bofeng Zhu
- Correspondence may also be addressed to Bofeng Zhu. Tel: +86 20 61648787; Fax: +86 20 61648787;
| | - Dong Wang
- To whom correspondence should be addressed. Tel: +86 20 61648279; Fax: +86 20 61648279;
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70
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Thakur A, Kumar M. AntiVIRmiR: A repository of host antiviral miRNAs and their expression along with experimentally validated viral miRNAs and their targets. Front Genet 2022; 13:971852. [PMID: 36159991 PMCID: PMC9493126 DOI: 10.3389/fgene.2022.971852] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/16/2022] [Indexed: 11/16/2022] Open
Abstract
miRNAs play an essential role in promoting viral infections as well as modulating the antiviral defense. Several miRNA repositories have been developed for different species, e.g., human, mouse, and plant. However, 'VIRmiRNA' is the only existing resource for experimentally validated viral miRNAs and their targets. We have developed a 'AntiVIRmiR' resource encompassing data on host/virus miRNA expression during viral infection. This resource with 22,741 entries is divided into four sub-databases viz., 'DEmiRVIR', 'AntiVmiR', 'VIRmiRNA2' and 'VIRmiRTar2'. 'DEmiRVIR' has 10,033 differentially expressed host-viral miRNAs for 21 viruses. 'AntiVmiR' incorporates 1,642 entries for host miRNAs showing antiviral activity for 34 viruses. Additionally, 'VIRmiRNA2' includes 3,340 entries for experimentally validated viral miRNAs from 50 viruses along with 650 viral isomeric sequences for 14 viruses. Further, 'VIRmiRTar2' has 7,726 experimentally validated targets for viral miRNAs against 21 viruses. Furthermore, we have also performed network analysis for three sub-databases. Interactions between up/down-regulated human miRNAs and viruses are displayed for 'AntiVmiR' as well as 'DEmiRVIR'. Moreover, 'VIRmiRTar2' interactions are shown among different viruses, miRNAs, and their targets. We have provided browse, search, external hyperlinks, data statistics, and useful analysis tools. The database available at https://bioinfo.imtech.res.in/manojk/antivirmir would be beneficial for understanding the host-virus interactions as well as viral pathogenesis.
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Affiliation(s)
- Anamika Thakur
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39-A, Chandigarh, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Manoj Kumar
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39-A, Chandigarh, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
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71
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Lu S, Liang Y, Li L, Liao S, Ouyang D. Inferring human miRNA–disease associations via multiple kernel fusion on GCNII. Front Genet 2022; 13:980497. [PMID: 36134032 PMCID: PMC9483142 DOI: 10.3389/fgene.2022.980497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 07/20/2022] [Indexed: 11/16/2022] Open
Abstract
Increasing evidence shows that the occurrence of human complex diseases is closely related to the mutation and abnormal expression of microRNAs(miRNAs). MiRNAs have complex and fine regulatory mechanisms, which makes it a promising target for drug discovery and disease diagnosis. Therefore, predicting the potential miRNA-disease associations has practical significance. In this paper, we proposed an miRNA–disease association predicting method based on multiple kernel fusion on Graph Convolutional Network via Initial residual and Identity mapping (GCNII), called MKFGCNII. Firstly, we built a heterogeneous network of miRNAs and diseases to extract multi-layer features via GCNII. Secondly, multiple kernel fusion method was applied to weight fusion of embeddings at each layer. Finally, Dual Laplacian Regularized Least Squares was used to predict new miRNA–disease associations by the combined kernel in miRNA and disease spaces. Compared with the other methods, MKFGCNII obtained the highest AUC value of 0.9631. Code is available at https://github.com/cuntjx/bioInfo.
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Affiliation(s)
- Shanghui Lu
- School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China
- School of Mathematics and Physics, Hechi University, Hechi, China
| | - Yong Liang
- School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China
- Peng Cheng Laboratory, Shenzhen, China
- *Correspondence: Yong Liang,
| | - Le Li
- School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China
| | - Shuilin Liao
- School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China
| | - Dong Ouyang
- School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, China
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72
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Tong W, Wenze G, Libing H, Yuchen C, Hejia Z, Xi G, Xiongyi Y, Guoguo Y, Min F. Exploration of shared TF-miRNA‒mRNA and mRNA-RBP-pseudogene networks in type 2 diabetes mellitus and breast cancer. Front Immunol 2022; 13:915017. [PMID: 36131924 PMCID: PMC9484524 DOI: 10.3389/fimmu.2022.915017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Type 2 diabetes mellitus (T2DM) has been confirmed to be closely associated with breast cancer (BC). However, the shared mechanisms between these diseases remain unclear. By comparing different datasets, we identified shared differentially expressed (DE) RNAs in T2DM and BC, including 427 mRNAs and 6 miRNAs from the GEO(Gene Expression Omnibus) database. We used databases to predict interactions to construct two critical networks. The transcription factor (TF)-miRNA‒mRNA network contained 236 TFs, while the RNA binding protein (RBP)-pseudogene-mRNA network showed that the pseudogene S-phase kinase associated protein 1 pseudogene 1 (SKP1P1) might play a key role in regulating gene expression. The shared mRNAs between T2DM and BC were enriched in cytochrome (CYP) pathways, and further analysis of CPEB1 and COLEC12 expression in cell lines, single cells and other cancers showed that they were strongly correlated with the survival and prognosis of patients with BC. This result suggested that patients with T2DM presenting the downregulation of CPEB1 and COLEC12 might have a higher risk of developing BC. Overall, our work revealed that high expression of CYPs in patients with T2DM might be a susceptibility factor for BC and identified novel gene candidates and immune features that are promising targets for immunotherapy in patients with BC.
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Affiliation(s)
- Wu Tong
- The First Clinical School, Southern Medical University, Guangzhou, China
| | - Gu Wenze
- The First Clinical School, Southern Medical University, Guangzhou, China
| | - Hong Libing
- The Second Clinical School, Southern Medical University, Guangzhou, China
| | - Cao Yuchen
- The Second Clinical School, Southern Medical University, Guangzhou, China
| | - Zhao Hejia
- The Second Clinical School, Southern Medical University, Guangzhou, China
| | - Guo Xi
- The Second Clinical School, Southern Medical University, Guangzhou, China
| | - Yang Xiongyi
- The Second Clinical School, Southern Medical University, Guangzhou, China
| | - Yi Guoguo
- Department of Ophthalmology, The Sixth Affiliated Hospital of Sun-Yat-Sen University Guangzhou, Guangdong, China
- *Correspondence: Fu Min, ; Yi Guoguo,
| | - Fu Min
- Department of Ophthalmology, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong, China
- *Correspondence: Fu Min, ; Yi Guoguo,
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73
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Ke H, Ren Z, Qi J, Chen S, Tseng GC, Ye Z, Ma T. High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression. Bioinformatics 2022; 38:4078-4087. [PMID: 35856716 PMCID: PMC9438953 DOI: 10.1093/bioinformatics/btac518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 06/29/2022] [Accepted: 07/19/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION The advancement of high-throughput technology characterizes a wide variety of epigenetic modifications and noncoding RNAs across the genome involved in disease pathogenesis via regulating gene expression. The high dimensionality of both epigenetic/noncoding RNA and gene expression data make it challenging to identify the important regulators of genes. Conducting univariate test for each possible regulator-gene pair is subject to serious multiple comparison burden, and direct application of regularization methods to select regulator-gene pairs is computationally infeasible. Applying fast screening to reduce dimension first before regularization is more efficient and stable than applying regularization methods alone. RESULTS We propose a novel screening method based on robust partial correlation to detect epigenetic and noncoding RNA regulators of gene expression over the whole genome, a problem that includes both high-dimensional predictors and high-dimensional responses. Compared to existing screening methods, our method is conceptually innovative that it reduces the dimension of both predictor and response, and screens at both node (regulators or genes) and edge (regulator-gene pairs) levels. We develop data-driven procedures to determine the conditional sets and the optimal screening threshold, and implement a fast iterative algorithm. Simulations and applications to long noncoding RNA and microRNA regulation in Kidney cancer and DNA methylation regulation in Glioblastoma Multiforme illustrate the validity and advantage of our method. AVAILABILITY AND IMPLEMENTATION The R package, related source codes and real datasets used in this article are provided at https://github.com/kehongjie/rPCor. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hongjie Ke
- Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA
| | - Zhao Ren
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Jianfei Qi
- Department of Biochemistry and Molecular Biology, University of Maryland, Baltimore, MD 21201, USA
| | - Shuo Chen
- Department of Epidemiology & Public Health, University of Maryland, Baltimore, MD 21201, USA
| | - George C Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Zhenyao Ye
- Department of Epidemiology & Public Health, University of Maryland, Baltimore, MD 21201, USA
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA
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74
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Huang L, Zhang L, Chen X. Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models. Brief Bioinform 2022; 23:6686738. [PMID: 36056743 DOI: 10.1093/bib/bbac358] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 07/24/2022] [Accepted: 07/30/2022] [Indexed: 12/12/2022] Open
Abstract
Since the problem proposed in late 2000s, microRNA-disease association (MDA) predictions have been implemented based on the data fusion paradigm. Integrating diverse data sources gains a more comprehensive research perspective, and brings a challenge to algorithm design for generating accurate, concise and consistent representations of the fused data. After more than a decade of research progress, a relatively simple algorithm like the score function or a single computation layer may no longer be sufficient for further improving predictive performance. Advanced model design has become more frequent in recent years, particularly in the form of reasonably combing multiple algorithms, a process known as model fusion. In the current review, we present 29 state-of-the-art models and introduce the taxonomy of computational models for MDA prediction based on model fusion and non-fusion. The new taxonomy exhibits notable changes in the algorithmic architecture of models, compared with that of earlier ones in the 2017 review by Chen et al. Moreover, we discuss the progresses that have been made towards overcoming the obstacles to effective MDA prediction since 2017 and elaborated on how future models can be designed according to a set of new schemas. Lastly, we analysed the strengths and weaknesses of each model category in the proposed taxonomy and proposed future research directions from diverse perspectives for enhancing model performance.
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Affiliation(s)
- Li Huang
- Academy of Arts and Design, Tsinghua University, Beijing, 10084, China.,The Future Laboratory, Tsinghua University, Beijing, 10084, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China
| | - Xing Chen
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.,Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
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75
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Hofmann L, Abou Kors T, Ezić J, Niesler B, Röth R, Ludwig S, Laban S, Schuler PJ, Hoffmann TK, Brunner C, Medyany V, Theodoraki MN. Comparison of plasma- and saliva-derived exosomal miRNA profiles reveals diagnostic potential in head and neck cancer. Front Cell Dev Biol 2022; 10:971596. [PMID: 36072342 PMCID: PMC9441766 DOI: 10.3389/fcell.2022.971596] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 07/19/2022] [Indexed: 11/13/2022] Open
Abstract
Background: Head and neck squamous cell carcinomas (HNSCC) lack tumor-specific biomarkers. Exosomes from HNSCC patients carry immunomodulatory molecules, and correlate with clinical parameters. We compared miRNA profiles of plasma- and saliva-derived exosomes to reveal liquid biomarker candidates for HNSCC. Methods: Exosomes were isolated by differential ultracentrifugation from corresponding plasma and saliva samples from 11 HNSCC patients and five healthy donors (HD). Exosomal miRNA profiles, as determined by nCounter® SPRINT technology, were analyzed regarding their diagnostic and prognostic potential, correlated to clinical data and integrated into network analysis. Results: 119 miRNAs overlapped between plasma- and saliva-derived exosomes of HNSCC patients, from which 29 tumor-exclusive miRNAs, associated with TP53, TGFB1, PRDM1, FOX O 1 and CDH1 signaling, were selected. By intra-correlation of tumor-exclusive miRNAs from plasma and saliva, top 10 miRNA candidates with the strongest correlation emerged as diagnostic panels to discriminate cancer and healthy as well as potentially prognostic panels for disease-free survival (DFS). Further, exosomal miRNAs were differentially represented in human papillomavirus (HPV) positive and negative as well as low and high stage disease. Conclusion: A plasma- and a saliva-derived panel of tumor-exclusive exosomal miRNAs hold great potential as liquid biopsy for discrimination between cancer and healthy as well as HPV status and disease stage. Exosomal miRNAs from both biofluids represent a promising tool for future biomarker studies, emphasizing the possibility to substitute plasma by less-invasive saliva collection.
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Affiliation(s)
- Linda Hofmann
- Department of Otorhinolaryngology, Head and Neck Surgery, Ulm University Medical Center, Ulm, Germany
| | - Tsima Abou Kors
- Department of Otorhinolaryngology, Head and Neck Surgery, Ulm University Medical Center, Ulm, Germany
| | - Jasmin Ezić
- Department of Otorhinolaryngology, Head and Neck Surgery, Ulm University Medical Center, Ulm, Germany
| | - Beate Niesler
- nCounter Core Facility, Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany
| | - Ralph Röth
- nCounter Core Facility, Institute of Human Genetics, University of Heidelberg, Heidelberg, Germany
| | - Sonja Ludwig
- Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Mannheim, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Simon Laban
- Department of Otorhinolaryngology, Head and Neck Surgery, Ulm University Medical Center, Ulm, Germany
| | - Patrick J. Schuler
- Department of Otorhinolaryngology, Head and Neck Surgery, Ulm University Medical Center, Ulm, Germany
| | - Thomas K. Hoffmann
- Department of Otorhinolaryngology, Head and Neck Surgery, Ulm University Medical Center, Ulm, Germany
| | - Cornelia Brunner
- Department of Otorhinolaryngology, Head and Neck Surgery, Ulm University Medical Center, Ulm, Germany
| | - Valentin Medyany
- Department of Otorhinolaryngology, Head and Neck Surgery, Ulm University Medical Center, Ulm, Germany
| | - Marie-Nicole Theodoraki
- Department of Otorhinolaryngology, Head and Neck Surgery, Ulm University Medical Center, Ulm, Germany
- *Correspondence: Marie-Nicole Theodoraki,
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76
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Yang M, Huang ZA, Gu W, Han K, Pan W, Yang X, Zhu Z. Prediction of biomarker-disease associations based on graph attention network and text representation. Brief Bioinform 2022; 23:6651308. [PMID: 35901464 DOI: 10.1093/bib/bbac298] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/28/2022] [Accepted: 06/30/2022] [Indexed: 02/06/2023] Open
Abstract
MOTIVATION The associations between biomarkers and human diseases play a key role in understanding complex pathology and developing targeted therapies. Wet lab experiments for biomarker discovery are costly, laborious and time-consuming. Computational prediction methods can be used to greatly expedite the identification of candidate biomarkers. RESULTS Here, we present a novel computational model named GTGenie for predicting the biomarker-disease associations based on graph and text features. In GTGenie, a graph attention network is utilized to characterize diverse similarities of biomarkers and diseases from heterogeneous information resources. Meanwhile, a pretrained BERT-based model is applied to learn the text-based representation of biomarker-disease relation from biomedical literature. The captured graph and text features are then integrated in a bimodal fusion network to model the hybrid entity representation. Finally, inductive matrix completion is adopted to infer the missing entries for reconstructing relation matrix, with which the unknown biomarker-disease associations are predicted. Experimental results on HMDD, HMDAD and LncRNADisease data sets showed that GTGenie can obtain competitive prediction performance with other state-of-the-art methods. AVAILABILITY The source code of GTGenie and the test data are available at: https://github.com/Wolverinerine/GTGenie.
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Affiliation(s)
- Minghao Yang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China
| | - Zhi-An Huang
- Center for Computer Science and Information Technology, City University of Hong Kong Dongguan Research Institute, Dongguan, China
| | - Wenhao Gu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China.,GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Kun Han
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Wenying Pan
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Xiao Yang
- GeneGenieDx Corp, 160 E Tasman Dr, San Jose, CA 95134
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518000, China
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Zhang W, Wei H, Liu B. idenMD-NRF: a ranking framework for miRNA-disease association identification. Brief Bioinform 2022; 23:6604995. [PMID: 35679537 DOI: 10.1093/bib/bbac224] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/18/2022] [Accepted: 05/11/2022] [Indexed: 11/12/2022] Open
Abstract
Identifying miRNA-disease associations is an important task for revealing pathogenic mechanism of complicated diseases. Different computational methods have been proposed. Although these methods obtained encouraging performance for detecting missing associations between known miRNAs and diseases, how to accurately predict associated diseases for new miRNAs is still a difficult task. In this regard, a ranking framework named idenMD-NRF is proposed for miRNA-disease association identification. idenMD-NRF treats the miRNA-disease association identification as an information retrieval task. Given a novel query miRNA, idenMD-NRF employs Learning to Rank algorithm to rank associated diseases based on high-level association features and various predictors. The experimental results on two independent test datasets indicate that idenMD-NRF is superior to other compared predictors. A user-friendly web server of idenMD-NRF predictor is freely available at http://bliulab.net/idenMD-NRF/.
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Affiliation(s)
- Wenxiang Zhang
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Hang Wei
- School of Computer Science and Technology, Xidian University, Xi'an, Shaanxi 710071, China
| | - Bin Liu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.,Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, 100081, China
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78
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MicroRNA Methylome Signature and Their Functional Roles in Colorectal Cancer Diagnosis, Prognosis, and Chemoresistance. Int J Mol Sci 2022; 23:ijms23137281. [PMID: 35806286 PMCID: PMC9266458 DOI: 10.3390/ijms23137281] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/27/2022] [Accepted: 06/27/2022] [Indexed: 02/01/2023] Open
Abstract
Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Despite significant advances in the diagnostic services and patient care, several gaps remain to be addressed, from early detection, to identifying prognostic variables, effective treatment for the metastatic disease, and the implementation of tailored treatment strategies. MicroRNAs, the short non-coding RNA species, are deregulated in CRC and play a significant role in the occurrence and progression. Nevertheless, microRNA research has historically been based on expression levels to determine its biological significance. The exact mechanism underpinning microRNA deregulation in cancer has yet to be elucidated, but several studies have demonstrated that epigenetic mechanisms play important roles in the regulation of microRNA expression, particularly DNA methylation. However, the methylation profiles of microRNAs remain unknown in CRC patients. Methylation is the next major paradigm shift in cancer detection since large-scale epigenetic alterations are potentially better in identifying and classifying cancers at an earlier stage than somatic mutations. This review aims to provide insight into the current state of understanding of microRNA methylation in CRC. The new knowledge from this study can be utilized for personalized health diagnostics, disease prediction, and monitoring of treatment.
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79
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Xu F, Wang Y, Ling Y, Zhou C, Wang H, Teschendorff AE, Zhao Y, Zhao H, He Y, Zhang G, Yang Z. dbDEMC 3.0: Functional Exploration of Differentially Expressed miRNAs in Cancers of Human and Model Organisms. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022; 20:446-454. [PMID: 35643191 PMCID: PMC9801039 DOI: 10.1016/j.gpb.2022.04.006] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 02/11/2022] [Accepted: 05/08/2022] [Indexed: 01/26/2023]
Abstract
MicroRNAs (miRNAs) are important regulators in gene expression. The dysregulation of miRNA expression is widely reported in the transformation from physiological to pathological states of cells. A large number of differentially expressed miRNAs (DEMs) have been identified in various human cancers by using high-throughput technologies, such as microarray and miRNA-seq. Through mining of published studies with high-throughput experiment information, the database of DEMs in human cancers (dbDEMC) was constructed with the aim of providing a systematic resource for the storage and query of the DEMs. Here we report an update of the dbDEMC to version 3.0, which contains two-fold more data entries than the second version and now includes also data from mice and rats. The dbDEMC 3.0 contains 3268 unique DEMs in 40 different cancer types. The current datasets for differential expression analysis have expanded to 9 generalized categories. Moreover, the current release integrates functional annotations of DEMs obtained by using experimentally validated targets. The annotations can be of great benefit to the intensive analysis of the roles of DEMs in cancer. In summary, dbDEMC 3.0 provides a valuable resource for characterizing molecular functions and regulatory mechanisms of DEMs in human cancers. The dbDEMC 3.0 is freely accessible at https://www.biosino.org/dbDEMC.
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Affiliation(s)
- Feng Xu
- Center for Medical Research and Innovation of Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China; Institutes of Biomedical Science, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai 200032, China
| | - Yifan Wang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yunchao Ling
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chenfen Zhou
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Haizhou Wang
- Center for Medical Research and Innovation of Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China; Institutes of Biomedical Science, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai 200032, China
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Haitao Zhao
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Yungang He
- Institutes of Biomedical Science, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai 200032, China; Shanghai Fifth People's Hospital, Fudan University, Shanghai 200240, China.
| | - Guoqing Zhang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Zhen Yang
- Center for Medical Research and Innovation of Pudong Hospital, Fudan University Pudong Medical Center, Shanghai 201399, China; Institutes of Biomedical Science, Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai 200032, China.
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80
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Zhong J, Zhou W, Kang J, Fang Z, Xie M, Xiao Q, Peng W. DNRLCNN: A CNN Framework for Identifying MiRNA-Disease Associations Using Latent Feature Matrix Extraction with Positive Samples. Interdiscip Sci 2022; 14:607-622. [PMID: 35428965 DOI: 10.1007/s12539-022-00509-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 02/24/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
Emerging evidence indicates that miRNAs have strong relationships with many human diseases. Investigating the associations will contribute to elucidating the activities of miRNAs and pathogenesis mechanisms, and providing new opportunities for disease diagnosis and drug discovery. Therefore, it is of significance to identify potential associations between miRNAs and diseases. The existing databases about the miRNA-disease associations (MDAs) only provide the known MDAs, which can be regarded as positive samples. However, the unknown MDAs are not sufficient to regard as reliable negative samples. To deal with this uncertainty, we proposed a convolutional neural network (CNN) framework, named DNRLCNN, based on a latent feature matrix extracted by only positive samples to predict MDAs. First, by only considering the positive samples into the calculation process, we captured the latent feature matrix for complex interactions between miRNAs and diseases in low-dimensional space. Then, we constructed a feature vector for each miRNA and disease pair based on the feature representation. Finally, we adopted a modified CNN for the feature vector to predict MDAs. As a result, our model achieves better performance than other state-of-the-art methods which based CNN in fivefold cross-validation on both miRNA-disease association prediction task (average AUC of 0.9030) and miRNA-phenotype association prediction task (average AUC of 0. 9442). In addition, we carried out case studies on two human diseases, and all the top-50 predicted miRNAs for lung neoplasms are confirmed by HMDD v3.2 and dbDEMC 2.0 databases, 98% of the top-50 predicted miRNAs for heart failure are confirmed. The experiment results show that our model has the capability of inferring potential disease-related miRNAs.
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Affiliation(s)
- Jiancheng Zhong
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410083, China
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Wubin Zhou
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410083, China
| | - Jiedong Kang
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410083, China
| | - Zhuo Fang
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410083, China
| | - Minzhu Xie
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410083, China
| | - Qiu Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha, 410083, China.
| | - Wei Peng
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China.
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81
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In silico Methods for Identification of Potential Therapeutic Targets. Interdiscip Sci 2022; 14:285-310. [PMID: 34826045 PMCID: PMC8616973 DOI: 10.1007/s12539-021-00491-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 10/19/2021] [Accepted: 11/01/2021] [Indexed: 11/01/2022]
Abstract
AbstractAt the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.
Graphical abstract
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82
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Fan X, Zou X, Liu C, Liu J, Peng S, Zhang S, Zhou X, Wang T, Geng X, Song G, Zhu W. Construction of the miRNA-mRNA Regulatory Networks and Explore Their Role in the Development of Lung Squamous Cell Carcinoma. Front Mol Biosci 2022; 9:888020. [PMID: 35712349 PMCID: PMC9197544 DOI: 10.3389/fmolb.2022.888020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 05/09/2022] [Indexed: 12/24/2022] Open
Abstract
Purpose: MicroRNA (miRNA) binds to target mRNA and inhibit post-transcriptional gene expression. It plays an essential role in regulating gene expression, cell cycle, and biological development. This study aims to identify potential miRNA-mRNA regulatory networks that contribute to the pathogenesis of lung squamous cell carcinoma (LUSC). Patients and Methods: MiRNA microarray and RNA-Seq datasets were obtained from the gene expression omnibus (GEO) databases, the cancer genome atlas (TCGA), miRcancer, and dbDEMC. The GEO2R tool, “limma” and “DEseq” R packages were used to perform differential expression analysis. Gene enrichment analysis was conducted using the DAVID, DIANA, and Hiplot tools. The miRNA-mRNA regulatory networks were screened from the experimentally validated miRNA-target interactions databases (miRTarBase and TarBase). External validation was carried out in 30 pairs of LUSC tissues by Real-Time Quantitative Reverse Transcription PCR (qRT-PCR). Receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were conducted to evaluate the diagnostic value. Clinical, survival and phenotypic analysis of miRNA-mRNA regulatory networks were further explored. Results: We screened 5 miRNA and 10 mRNA expression datasets from GEO and identified 7 DE-miRNAs and 270 DE-mRNAs. After databases screening and correlation analysis, four pairs of miRNA-mRNA regulatory networks were screened out. The miRNA-mRNA network of miR-205-5p (up) and PTPRM (down) was validated in 30 pairs of LUSC tissues. MiR-205-5p and PTPRM have good diagnostic efficacy and are expressed differently in different clinical features and are related to tumor immunity. Conclusion: The research identified a potential miRNA-mRNA regulatory network, providing a new way to explore the genesis and development of LUSC.
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Affiliation(s)
- Xingchen Fan
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xuan Zou
- First Clinical College of Nanjing Medical University, Nanjing, China
| | - Cheng Liu
- Department of Gastroenterology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiawen Liu
- First Clinical College of Nanjing Medical University, Nanjing, China
| | - Shuang Peng
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Shiyu Zhang
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xin Zhou
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Tongshan Wang
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiangnan Geng
- Department of Clinical Engineer, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Xiangnan Geng, ; Guoxin Song, ; Wei Zhu,
| | - Guoxin Song
- Department of Pathology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Xiangnan Geng, ; Guoxin Song, ; Wei Zhu,
| | - Wei Zhu
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Xiangnan Geng, ; Guoxin Song, ; Wei Zhu,
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83
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Ren Y, Yan C, Wu L, Zhao J, Chen M, Zhou M, Wang X, Liu T, Yi Q, Sun J. iUMRG: multi-layered network-guided propagation modeling for the inference of susceptibility genes and potential drugs against uveal melanoma. NPJ Syst Biol Appl 2022; 8:18. [PMID: 35610253 PMCID: PMC9130324 DOI: 10.1038/s41540-022-00227-8] [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: 01/20/2022] [Accepted: 04/28/2022] [Indexed: 11/17/2022] Open
Abstract
Uveal melanoma (UM) is the most common primary malignant intraocular tumor. The use of precision medicine for UM to enable personalized diagnosis, prognosis, and treatment require the development of computer-aided strategies and predictive tools that can identify novel high-confidence susceptibility genes (HSGs) and potential therapeutic drugs. In the present study, a computational framework via propagation modeling on integrated multi-layered molecular networks (abbreviated as iUMRG) was proposed for the systematic inference of HSGs in UM. Under the leave-one-out cross-validation experiments, the iUMRG achieved superior predictive performance and yielded a higher area under the receiver operating characteristic curve value (0.8825) for experimentally verified SGs. In addition, using the experimentally verified SGs as seeds, genome-wide screening was performed to detect candidate HSGs using the iUMRG. Multi-perspective validation analysis indicated that most of the top 50 candidate HSGs were indeed markedly associated with UM carcinogenesis, progression, and outcome. Finally, drug repositioning experiments performed on the HSGs revealed 17 potential targets and 10 potential drugs, of which six have been approved for UM treatment. In conclusion, the proposed iUMRG is an effective supplementary tool in UM precision medicine, which may assist the development of new medical therapies and discover new SGs.
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Affiliation(s)
- Yueping Ren
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, P. R. China
| | - Congcong Yan
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, P. R. China
| | - Lili Wu
- Tibet Medical College, Beijing University of Chinese Medicine, Tibet, 850010, P. R. China
| | - Jingting Zhao
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, P. R. China
| | - Mingwei Chen
- Department of Human Anatomy, Harbin Medical University, Harbin, 150081, P. R. China
| | - Meng Zhou
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, P. R. China
| | - Xiaoyan Wang
- The Affiliated Ningbo Eye Hospital of Wenzhou Medical University, Ningbo, 315042, P. R. China
| | - Tonghua Liu
- Tibet Medical College, Beijing University of Chinese Medicine, Tibet, 850010, P. R. China.
| | - Quanyong Yi
- The Affiliated Ningbo Eye Hospital of Wenzhou Medical University, Ningbo, 315042, P. R. China.
| | - Jie Sun
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, 325027, P. R. China.
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84
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Liu P, Luo J, Chen X. miRCom: Tensor Completion Integrating Multi-View Information to Deduce the Potential Disease-Related miRNA-miRNA Pairs. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1747-1759. [PMID: 33180730 DOI: 10.1109/tcbb.2020.3037331] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
MicroRNAs (miRNAs) are consistently capable of regulating gene expression synergistically in a combination mode and play a key role in various biological processes associated with the initiation and development of human diseases, which indicate that comprehending the synergistic molecular mechanism of miRNAs may facilitate understanding the pathogenesis of diseases or even overcome it. However, most existing computational methods had an incomprehensive acknowledge of the miRNA synergistic effect on the pathogenesis of complex diseases, or were hard to be extended to a large-scale prediction task of miRNA synergistic combinations for different diseases. In this article, we propose a novel tensor completion framework integrating multi-view miRNAs and diseases information, called miRCom, for the discovery of potential disease-associated miRNA-miRNA pairs. We first construct an incomplete three-order association tensor and several types of similarity matrices based on existing biological knowledge. Then, we formulate an objective function via performing the factorizations of coupled tensor and matrices simultaneously. Finally, we build an optimization schema by adopting the ADMM algorithm. After that, we obtain the prediction of miRNA-miRNA pairs for different diseases from the full tensor. The contrastive experimental results with other approaches verified that miRCom effectively identify the potential disease-related miRNA-miRNA pairs. Moreover, case study results further illustrated that miRNA-miRNA pairs have more biologically significance and prognostic value than single miRNAs.
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85
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Di Maria A, Alaimo S, Bellomo L, Billeci F, Ferragina P, Ferro A, Pulvirenti A. BioTAGME: A Comprehensive Platform for Biological Knowledge Network Analysis. Front Genet 2022; 13:855739. [PMID: 35571058 PMCID: PMC9096447 DOI: 10.3389/fgene.2022.855739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 03/24/2022] [Indexed: 02/02/2023] Open
Abstract
The inference of novel knowledge and new hypotheses from the current literature analysis is crucial in making new scientific discoveries. In bio-medicine, given the enormous amount of literature and knowledge bases available, the automatic gain of knowledge concerning relationships among biological elements, in the form of semantically related terms (or entities), is rising novel research challenges and corresponding applications. In this regard, we propose BioTAGME, a system that combines an entity-annotation framework based on Wikipedia corpus (i.e., TAGME tool) with a network-based inference methodology (i.e., DT-Hybrid). This integration aims to create an extensive Knowledge Graph modeling relations among biological terms and phrases extracted from titles and abstracts of papers available in PubMed. The framework consists of a back-end and a front-end. The back-end is entirely implemented in Scala and runs on top of a Spark cluster that distributes the computing effort among several machines. The front-end is released through the Laravel framework, connected with the Neo4j graph database to store the knowledge graph.
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Affiliation(s)
- Antonio Di Maria
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Salvatore Alaimo
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | | | - Fabrizio Billeci
- Department of Maths and Computer Science, University of Catania, Catania, Italy
| | - Paolo Ferragina
- Department of Computer Science, University of Pisa, Pisa, Italy
| | - Alfredo Ferro
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Alfredo Pulvirenti
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
- *Correspondence: Alfredo Pulvirenti,
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86
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Mukherjee M, Ghosh S, Goswami S. Investigating the interference of single nucleotide polymorphisms with miRNA mediated gene regulation in pancreatic ductal adenocarcinoma: An in silico approach. Gene 2022; 819:146259. [PMID: 35121024 DOI: 10.1016/j.gene.2022.146259] [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: 07/30/2021] [Revised: 01/14/2022] [Accepted: 01/27/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC) has a strong genetic component and single nucleotide polymorphisms (SNPs) in key genes have been found to modulate the susceptibility of the individuals to the disease. SNPs in 3'-UTR of the target genes or in miRNA seed region has gained much importance as this may lead to impairment of miRNA-mRNA interaction. Not much information about this phenomenon is available with respect to PDAC and we wanted to predict such SNPs which could affect miRNA function in the disease using bioinformatics tools. METHODS After identifying the deregulated miRNAs and genes in PDAC, we determined how many of those altered genes are among experimentally validated targets of those miRNAs. Subsequently, SNPs which could alter these miRNA-mRNA interactions were detected using multiple webtools following high stringent conditions. Disease relevance of the SNPs were also evaluated. RESULTS We identified a total of 2492 experimentally validated target genes for 303 miRNAs deregulated in PDAC. Our meta-analysis from 363 PDAC patients and 162 control individuals resulted in a set of differentially expressed genes in pancreatic cancer, which was further compared with the miRNA target genes to get targets differentially expressed in pancreatic cancer. We further detected SNPs either in 'seed' region of miRNAs or 'seed-match' sequence of mRNAs either having disruption or creation of miRNA binding site, correlated the expression for each miRNA-SNP-mRNA interaction. Selected SNPs were found to be in LD with important GWAS identified SNPs. CONCLUSION Our study, hereby, explores the probable effects of SNPs on miRNA-target mRNA interactions. Through stringent analytical methods, we have identified 3 common variants and 13other rare variants possibly interfering with miRNA mediated gene regulation in PDAC.
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Affiliation(s)
- Moumita Mukherjee
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India
| | - Satyajit Ghosh
- Indian Institute of Technology-Jodhpur, Jodhpur, India(1)
| | - Srikanta Goswami
- National Institute of Biomedical Genomics, Kalyani, West Bengal, India.
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87
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Yousef M, Goy G, Bakir-Gungor B. miRModuleNet: Detecting miRNA-mRNA Regulatory Modules. Front Genet 2022; 13:767455. [PMID: 35495139 PMCID: PMC9039401 DOI: 10.3389/fgene.2022.767455] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 03/24/2022] [Indexed: 12/13/2022] Open
Abstract
Increasing evidence that microRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis. The miRModuleNet tool and all other supplementary files are available at https://github.com/malikyousef/miRModuleNet/
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Affiliation(s)
- Malik Yousef
- Department of Information Systems, Zefat Academic College, Zefat, Israel
- *Correspondence: Malik Yousef,
| | - Gokhan Goy
- Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, Turkey
- The Scientific and Technological Research Council of Turkey, Ankara, Turkey
| | - Burcu Bakir-Gungor
- Department of Computer Engineering, Faculty of Engineering, Abdullah Gul University, Kayseri, Turkey
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88
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Yan C, Duan G, Li N, Zhang L, Wu FX, Wang J. PDMDA: predicting deep-level miRNA-disease associations with graph neural networks and sequence features. Bioinformatics 2022; 38:2226-2234. [PMID: 35150255 DOI: 10.1093/bioinformatics/btac077] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 01/18/2022] [Accepted: 02/05/2022] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Many studies have shown that microRNAs (miRNAs) play a key role in human diseases. Meanwhile, traditional experimental methods for miRNA-disease association identification are extremely costly, time-consuming and challenging. Therefore, many computational methods have been developed to predict potential associations between miRNAs and diseases. However, those methods mainly predict the existence of miRNA-disease associations, and they cannot predict the deep-level miRNA-disease association types. RESULTS In this study, we propose a new end-to-end deep learning method (called PDMDA) to predict deep-level miRNA-disease associations with graph neural networks (GNNs) and miRNA sequence features. Based on the sequence and structural features of miRNAs, PDMDA extracts the miRNA feature representations by a fully connected network (FCN). The disease feature representations are extracted from the disease-gene network and gene-gene interaction network by GNN model. Finally, a multilayer with three fully connected layers and a softmax layer is designed to predict the final miRNA-disease association scores based on the concatenated feature representations of miRNAs and diseases. Note that PDMDA does not take the miRNA-disease association matrix as input to compute the Gaussian interaction profile similarity. We conduct three experiments based on six association type samples (including circulations, epigenetics, target, genetics, known association of which their types are unknown and unknown association samples). We conduct fivefold cross-validation validation to assess the prediction performance of PDMDA. The area under the receiver operating characteristic curve scores is used as metric. The experiment results show that PDMDA can accurately predict the deep-level miRNA-disease associations. AVAILABILITY AND IMPLEMENTATION Data and source codes are available at https://github.com/27167199/PDMDA.
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Affiliation(s)
- Cheng Yan
- School of Information Science and Engineering, Hunan University of Chinese Medicine, Changsha 410208, China.,School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Guihua Duan
- School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Na Li
- School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Lishen Zhang
- School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon SK S7N5A9, Canada
| | - Jianxin Wang
- School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China
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89
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Shil R, Ghosh R, Banerjee AK, Mal C. LncRNA, miRNA and transcriptional co-regulatory network of breast and ovarian cancer reveals hub molecules. Meta Gene 2022. [DOI: 10.1016/j.mgene.2022.101024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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90
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Ding Y, Lei X, Liao B, Wu FX. MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations. Brief Bioinform 2022; 23:6552270. [PMID: 35323901 DOI: 10.1093/bib/bbac079] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/07/2022] [Accepted: 02/15/2022] [Indexed: 01/20/2023] Open
Abstract
MOTIVATION MicroRNAs (miRNAs), as critical regulators, are involved in various fundamental and vital biological processes, and their abnormalities are closely related to human diseases. Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases. RESULTS In this study, we propose a multi-view Laplacian regularized deep factorization machine (DeepFM) model, MLRDFM, to predict novel miRNA-disease associations while improving the standard DeepFM. Specifically, MLRDFM improves DeepFM from two aspects: first, MLRDFM takes the relationships among items into consideration by regularizing their embedding features via their similarity-based Laplacians. In this study, miRNA Laplacian regularization integrates four types of miRNA similarity, while disease Laplacian regularization integrates two types of disease similarity. Second, to judiciously train our model, Laplacian eigenmaps are utilized to initialize the weights in the dense embedding layer. The experimental results on the latest HMDD v3.2 dataset show that MLRDFM improves the performance and reduces the overfitting phenomenon of DeepFM. Besides, MLRDFM is greatly superior to the state-of-the-art models in miRNA-disease association prediction in terms of different evaluation metrics with the 5-fold cross-validation. Furthermore, case studies further demonstrate the effectiveness of MLRDFM.
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Affiliation(s)
- Yulian Ding
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, S7N 5A9, Saskatchewan, Canada
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, 620 West Chang'an Avenue, 710119, Shaanxi, China
| | - Bo Liao
- School of Mathematics and Statistics, Hainan Normal University, 99 Longkun South Road, 571158, Hainan, China
| | - Fang-Xiang Wu
- Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Drive, S7N 5A9, Saskatchewan, Canada.,Department of Mechanical Engineering and Department of Computer Science, University of Saskatchewan, 57 Campus Drive, S7N5A9, Saskatchewan, Canada
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91
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Qin Y, Qi Y, Zhang X, Guan Z, Han W, Peng X. Production and Stabilization of Specific Upregulated Long Noncoding RNA HOXD-AS2 in Glioblastomas Are Mediated by TFE3 and miR-661, Respectively. Int J Mol Sci 2022; 23:ijms23052828. [PMID: 35269968 PMCID: PMC8911140 DOI: 10.3390/ijms23052828] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 02/24/2022] [Accepted: 03/03/2022] [Indexed: 02/04/2023] Open
Abstract
Differential expression of long noncoding RNAs (lncRNA) plays a key role in the development of gliomas. Because gliomas are the most common primary central nervous system tumor and glioblastomas have poor prognosis, it is urgent to develop new diagnostic methods. We have previously reported that lncRNA HOXD-AS2, which is specifically up-regulated in gliomas, can activate cell cycle and promote the development of gliomas. It is expected to be a new marker for molecular diagnosis of gliomas, but little is known about HOXD-AS2. Here, we demonstrate that TFE3 and miR-661 maintain the high expression level of HOXD-AS2 by regulating its production and degradation. We found that TFE3 acted as a transcription factor binding to the HOXD-AS2 promoter region and raised H3K27ac to activate HOXD-AS2. As the cytoplasmic-located lncRNA, HOXD-AS2 could be degraded by miR-661. This process was inhibited in gliomas due to the low expression of miR-661. Our study explains why HOXD-AS2 was specifically up-regulated in gliomas, helps to understand the molecular characteristics of gliomas, and provids insights for the search for specific markers in gliomas.
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Affiliation(s)
| | | | | | | | - Wei Han
- Correspondence: (W.H.); or (X.P.)
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92
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Song K, Liu C, Zhang J, Yao Y, Xiao H, Yuan R, Li K, Yang J, Zhao W, Zhang Y. Integrated multi-omics analysis reveals miR-20a as a regulator for metabolic colorectal cancer. Heliyon 2022; 8:e09068. [PMID: 35284668 PMCID: PMC8914124 DOI: 10.1016/j.heliyon.2022.e09068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/21/2021] [Accepted: 03/03/2022] [Indexed: 11/28/2022] Open
Abstract
Single-driver molecular events specific to the metabolic colorectal cancer (CRC) have not been clearly elucidated. Herein, we identified 12 functional miRNAs linked to activated metabolism by integrating multi-omics features in metabolic CRC. These miRNAs exhibited significantly enriched CRC driver miRNAs, significant impacts on CRC cell growth and significantly correlated metabolites. Importantly, miR-20a is minimally expressed in normal colorectal tissues but highly expressed in metabolic CRC, suggesting the potential therapeutic target. Bioinformatics analyses further revealed miR-20a as the most powerful determinant that regulates a cascade of dysregulated events, including Wnt signaling pathway, core enzymes involved in FA metabolism program and triacylglycerol abundances. In vitro assays demonstrated that elevated miR-20a up-regulated FA synthesis enzymes via Wnt/β-catenin signaling, and finally promoted proliferative and migration of metabolic CRC cells. Overall, our study revealed that miR-20a promoted progression of metabolic CRC by regulating FA metabolism and served as a potential target for preventing tumor metastasis.
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Affiliation(s)
- Kai Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
- Zhuhai Interventional Medical Center, Zhuhai Precision Medical Center, Zhuhai People's Hospital, Zhuhai Hospital Affiliated with Jinan University, Jinan University, Zhuhai, Guangdong, 519000, China
| | - Chao Liu
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150086, China
| | - Jiashuai Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Yang Yao
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150086, China
| | - Huiting Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Rongqiang Yuan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Keru Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Jia Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Wenyuan Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
- Corresponding author.
| | - Yanqiao Zhang
- Department of Gastrointestinal Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150086, China
- Corresponding author.
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93
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Hossain KR, Escobar Bermeo JD, Warton K, Valenzuela SM. New Approaches and Biomarker Candidates for the Early Detection of Ovarian Cancer. Front Bioeng Biotechnol 2022; 10:819183. [PMID: 35223789 PMCID: PMC8867026 DOI: 10.3389/fbioe.2022.819183] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- K R Hossain
- School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW, Australia
| | - J D Escobar Bermeo
- School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW, Australia.,ARC Research Hub for Integrated Device for End-user Analysis at Low-levels (IDEAL), Faculty of Science, University of Technology Sydney, Sydney, NSW, Australia
| | - K Warton
- School of Women's and Children's Health, Faculty of Medicine and Health, University of New South Wales, South Wales, NSW, Australia
| | - S M Valenzuela
- School of Life Sciences, Faculty of Science, University of Technology Sydney, Sydney, NSW, Australia.,ARC Research Hub for Integrated Device for End-user Analysis at Low-levels (IDEAL), Faculty of Science, University of Technology Sydney, Sydney, NSW, Australia
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94
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A miRNA-Disease Association Identification Method Based on Reliable Negative Sample Selection and Improved Single-Hidden Layer Feedforward Neural Network. INFORMATION 2022. [DOI: 10.3390/info13030108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
miRNAs are a category of important endogenous non-coding small RNAs and are ubiquitous in eukaryotes. They are widely involved in the regulatory process of post-transcriptional gene expression and play a critical part in the development of human diseases. By utilizing recent advancements in big data technology, using bioinformatics methods to identify causative miRNA becomes a hot spot. In this paper, a method called RNSSLFN is proposed to identify the miRNA-disease associations by reliable negative sample selection and an improved single-hidden layer feedforward neural network (SLFN). It involves, firstly, obtaining integrated similarity for miRNAs and diseases; next, selecting reliable negative samples from unknown miRNA-disease associations via distinguishing up-regulated or down-regulated miRNAs; then, introducing an improved SLFN to solve the prediction task. The experimental results on the latest data sets HMDD v3.2 and the framework of 5-fold cross-validation (CV) show that the average AUC and AUPR of RNSSLFN achieve 0.9316 and 0.9065 m, respectively, which are superior to the other three state-of-the-art methods. Furthermore, in the case studies of 10 common cancers, more than 70% of the top 30 predicted miRNA-disease association pairs are verified in the databases, which further confirms the reliability and effectiveness of the RNSSLFN model. Generally, RNSSLFN in predicting miRNA-disease associations has prodigious potential and extensive foreground.
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95
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Hishida A, Yamada H, Ando Y, Okugawa Y, Shiozawa M, Miyagi Y, Daigo Y, Toiyama Y, Shirai Y, Tanaka K, Kubo Y, Okada R, Nagayoshi M, Tamura T, Mori A, Kondo T, Hamajima N, Takeuchi K, Wakai K. Investigation of miRNA expression profiles using cohort samples reveals potential early detectability of colorectal cancers by serum miR-26a-5p before clinical diagnosis. Oncol Lett 2022; 23:87. [PMID: 35126729 PMCID: PMC8805182 DOI: 10.3892/ol.2022.13207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 01/06/2022] [Indexed: 11/25/2022] Open
Abstract
Previous studies have investigated the usefulness of microRNA (miRNA/miR) expression data for the early detection of colorectal cancer (CRC). However, limited data are available regarding miRNAs that detect CRC before clinical diagnoses. Accordingly, the present study investigated the early detectability of CRC by miRNAs using the preserved serum samples of the cohort participants affected with CRC within 2 years of study enrollment. First, the significant miRNAs were revealed using clinical CRC samples for a (seven early CRCs and seven controls) microarray analysis based on significance analysis of microarrays. Next, replicability was verified by reverse transcription-quantitative (RT-q)PCR (eight early CRCs and eight controls, together with 12 CRCs and 12 controls). Finally, early detectability was tested using the cohort samples of Japan Multi-Institutional Collaborative Cohort Study (17 CRCs and 17 controls) to reveal how a certain number of patients developed CRC within 2 years after participation. In the discovery phase, miRNA expression measurements were conducted using a 3D-Gene Human miRNA Oligo Chip for 2,555 miRNAs, and RT-qPCR analyses were performed to validate the replicability. In the first validation set with eight CRCs with early clinical stage and eight age- and gender-matched controls, miR-26a-5p and miR-223-3p demonstrated the highest diagnostic accuracy of area under the curve (AUC)=1.000 (sensitivity and specificity 100%). In an examination of the predictability of CRC incidence using pre-clinical cohort samples, miR-26a-5p demonstrated good predictability of advanced CRC incidence with an AUC of 0.840. Overall, the present study revealed serum miR-26a-5p as a potential early detection marker for CRC.
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Affiliation(s)
- Asahi Hishida
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Hiroya Yamada
- Department of Hygiene, Fujita Health University School of Medical Sciences, Toyoake, Aichi 470-1192, Japan
| | - Yoshitaka Ando
- Department of Informative Clinical Medicine, Fujita Health University School of Medical Sciences, Toyoake, Aichi 470-1192, Japan
| | - Yoshinaga Okugawa
- Department of Genomic Medicine, Mie University Graduate School of Medicine, Tsu, Mie 514-8507, Japan.,Department of Gastrointestinal and Pediatric Surgery, Mie University Graduate School of Medicine, Tsu, Mie 514-8507, Japan
| | - Manabu Shiozawa
- Department of Gastrointestinal Surgery, Kanagawa Cancer Center Hospital, Yokohama, Kanagawa 241-8515, Japan
| | - Yohei Miyagi
- Molecular Pathology and Genetics Division, Kanagawa Cancer Center Research Institute, Yokohama, Kanagawa 241-8515, Japan
| | - Yataro Daigo
- Center for Antibody and Vaccine Therapy, Institute of Medical Science, Research Hospital, The University of Tokyo, Tokyo 108-8639, Japan.,Department of Medical Oncology and Cancer Center, Center for Advanced Medicine Against Cancer, Shiga University of Medical Science, Otsu, Shiga 520-2192, Japan
| | - Yuji Toiyama
- Department of Gastrointestinal and Pediatric Surgery, Mie University Graduate School of Medicine, Tsu, Mie 514-8507, Japan
| | - Yumiko Shirai
- Department of Nutrition, Iga City General Hospital, Iga, Mie 518-0823, Japan
| | - Koji Tanaka
- Department of Surgery, Iga City General Hospital, Iga, Mie 518-0823, Japan
| | - Yoko Kubo
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Rieko Okada
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Mako Nagayoshi
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Takashi Tamura
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Atsuyoshi Mori
- Seirei Preventive Health Care Center, Hamamatsu, Shizuoka 433-8558, Japan
| | - Takaaki Kondo
- Department of Pathophysiological Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Nobuyuki Hamajima
- Department of Healthcare Administration, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Kenji Takeuchi
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Kenji Wakai
- Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
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96
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Xiang J, Zhang J, Zhao Y, Wu FX, Li M. Biomedical data, computational methods and tools for evaluating disease-disease associations. Brief Bioinform 2022; 23:6522999. [PMID: 35136949 DOI: 10.1093/bib/bbac006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/04/2022] [Accepted: 01/05/2022] [Indexed: 12/12/2022] Open
Abstract
In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.
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Affiliation(s)
- Ju Xiang
- School of Computer Science and Engineering, Central South University, China
| | - Jiashuai Zhang
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Yichao Zhao
- School of Computer Science and Engineering, Central South University, China
| | - Fang-Xiang Wu
- Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Min Li
- Division of Biomedical Engineering and Department of Mechanical Engineering at University of Saskatchewan, Saskatoon, Canada
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97
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Wang J, Shao J, Lu Y, Su W, Dong H, Wang P, Lin Z, Feng J, Wang D, Zhao H, Tan J. Screening Differential CircRNAs Expression Profiles Reveals the Regulatory Role of the has_circTPT1_003-has-miR-218-5p-CCNE2/SMC4 Signaling Axis in Bladder Carcinoma Progression. DNA Cell Biol 2022; 41:128-141. [PMID: 35005988 DOI: 10.1089/dna.2021.0240] [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] [Indexed: 11/13/2022] Open
Abstract
Circular RNAs (circRNAs) are a class of noncoding RNAs closely related to the development and progression of various human cancers. However, it is unclear whether circRNAs play an important role in the development of bladder cancer. We utilized human circRNA array V2 microarrays to screen circRNA expression profiles in bladder cancer tissues. Bioinformatic tools including circBank, dbDEMC 2.0, miRCancer, TarBase v7.0, miRtarbase, TCGA-BLCA, Cytoscape-MCODE, String, ENCORI, and Venny 2.1 were then employed to construct the circRNA-miRNA-mRNA regulatory networks. In total, 105 upregulated circRNAs and 167 downregulated circRNAs (fold change >2 and p < 0.001) were filtered out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of filtered dysregulated circRNAs disclosed that the circRNAs regulatory network was closely related with mRNA processing and cell cycle, etc. Further excavation analysis showed that seven differentially overexpressed circRNAs including hsa_circ_0000133, hsa_circ_0023610, hsa_circ_0005615, hsa_circ_0030162, hsa_circ_0077007, hsa_circ_0001140, and hsa_circ_0107031 were associated with bladder cancer invasiveness, and the cell cycle signal axis. has_circTPT1_003-has-miR-218-5p-CCNE2/SMC4 was finally clarified as a possible mechanism for bladder cancer progression. Based on results derived from multiple approaches, we identified that has_circTPT1_003-has-miR-218-5p-CCNE2/SMC4 signal axis may be involved in the invasion process of bladder cancer.
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Affiliation(s)
- Jie Wang
- Fujian Provincial Key Laboratory of Transplant Biology, Department of Urology, 900 Hospital of the Joint Logistics Team (Dongfang Hospital), Xiamen University, Fuzhou, Fujian, P.R. China
- Ningbo First Hospital Jiangbei Branch, Ningbo, Zhejiang, P.R. China
| | - Jichun Shao
- Department of Urology, Second Affiliated Hospital of Chengdu Medical College (China National Nuclear Corporation 416 Hospital), Chengdu, P.R. China
| | - Yuan Lu
- Respiratory Department, Zhongda Hospital, Southeast University, Nanjing, P.R. China
| | - Weipeng Su
- Fujian Provincial Key Laboratory of Transplant Biology, Department of Urology, 900 Hospital of the Joint Logistics Team (Dongfang Hospital), Xiamen University, Fuzhou, Fujian, P.R. China
| | - Huiyue Dong
- Fujian Provincial Key Laboratory of Transplant Biology, Department of Urology, 900 Hospital of the Joint Logistics Team (Dongfang Hospital), Xiamen University, Fuzhou, Fujian, P.R. China
| | - Ping Wang
- Fujian Provincial Key Laboratory of Transplant Biology, Department of Urology, 900 Hospital of the Joint Logistics Team (Dongfang Hospital), Xiamen University, Fuzhou, Fujian, P.R. China
| | - Zhijie Lin
- Fujian Provincial Key Laboratory of Transplant Biology, Department of Urology, 900 Hospital of the Joint Logistics Team, Fujian Medical University, Fuzhou, Fujian, P.R. China
| | - Jing Feng
- Department of Radiation Oncology, Fujian Medical University Cancer Hospital, 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, Fujian, P.R. China
| | - Dong Wang
- Fujian Provincial Key Laboratory of Transplant Biology, Department of Urology, 900 Hospital of the Joint Logistics Team (Dongfang Hospital), Xiamen University, Fuzhou, Fujian, P.R. China
| | - Hu Zhao
- Fujian Provincial Key Laboratory of Transplant Biology, Department of Urology, 900 Hospital of the Joint Logistics Team (Dongfang Hospital), Xiamen University, Fuzhou, Fujian, P.R. China
- Department of General Surgery, 900 Hospital of the Joint Logistics Team, Fujian Medical University, Fuzhou, Fujian, P.R. China
| | - Jianming Tan
- Fujian Provincial Key Laboratory of Transplant Biology, Department of Urology, 900 Hospital of the Joint Logistics Team (Dongfang Hospital), Xiamen University, Fuzhou, Fujian, P.R. China
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98
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Meng T, Lan Z, Zhao X, Niu L, Chen C, Zhang W. Comprehensive bioinformatics analysis of functional molecules in colorectal cancer. J Gastrointest Oncol 2022; 13:231-245. [PMID: 35284121 PMCID: PMC8899732 DOI: 10.21037/jgo-21-921] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 01/30/2022] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is the 3rd most common cancer and the 2nd leading cause of cancer-related death. Numerous studies have found that aberrations in cellular molecules play an important role in the development of tumors. Studying and determining the interactions between these molecules can contribute to the diagnosis, treatment, and prognosis of tumors. METHODS The GSE151021, GSE156720, and GSE156719 data sets were analyzed to screen the differentially expressed messenger RNAs (DEmRNAs), long non-coding RNAs (DElncRNAs), and microRNAs (DEmiRNAs) in CRC. Database for Annotation, Visualization and Integrated Discovery (DAVID) and the Search Tool for the Retrieval of Interacting Genes/Proteins software were used to examine gene enrichment and the hub genes. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and UALCAN was used to verify the expression of the hub genes. To analyze the overall survival (OS) of the hub genes, Kaplan-Meier plotter (KM plotter) was performed. Finally, the miRCancer database, TargetScan, and GSE156719 were used to identify the targets of the identified miRNAs. To predict the lncRNA-miRNA interactions, we used DIANA-LncBase v2 and GSE156720. Finally, the visualization protein‑protein interaction (PPI), competitive endogenous RNA (ceRNA) network was constructed using Cytoscape v3.1. RESULTS By analyzing GSE151021 and GSE156720, 23 upregulated mRNAs and 10 downregulated mRNAs were identified as sharing the differentially expressed genes (DEGs) between CRC and adjacent tissues. Furthermore, nucleolar protein 14 (NOP14), the sonic hedgehog (SHH) signaling molecule, phorbol-12-myristate-13-acetate-induced protein 1 (PMAIP1), the BCL2 apoptosis regulator (BCL2), and zinc finger E-box binding homeobox 2 (ZEB2) were considered hub genes. The constructed lncRNA-miRNA-mRNA network revealed 7 intersecting miRNAs (4 upregulated and 3 downregulated), 79 lncRNAs (40 upregulated and 39 downregulated), and 5 mRNAs (3 upregulated and 2 downregulated). Finally, we determined that the dysregulation of lncRNAs, such as HCG16, CASC9, SNHG16, HAND2-AS1, and NR2F1-AS1, secluded altered the expression of several miRNAs, such as hsa-miR-193a-5p, hsa-miR-485-5p, hsa-miR-17-5p, and hsa-miR-92a-3p, and affected the occurrence and development of CRC. CONCLUSIONS We identified a series of DElncRNAs, DEmRNAs, and DEmiRNAs in CRC that might be considered potential biomarkers in understanding the complex molecular pathways leading to CRC development.
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Affiliation(s)
- Tao Meng
- Department of Gastrointestinal Surgery, Xinjiang Medical University Tumor Hospital, Urumqi, China
| | - Zhangzhang Lan
- School of Medicine, Southern University of Science and Technology, Shenzhen, China
| | - Xiaoling Zhao
- CheerLand Clinical Laboratory Co., Ltd., Peking University Medical Industrial Park, Zhongguancun Life Science Park, Beijing, China
| | - Li Niu
- CheerLand Clinical Laboratory Co., Ltd., Peking University Medical Industrial Park, Zhongguancun Life Science Park, Beijing, China
- Shenzhen Cheerland Biotechnology Co., Ltd., Cheerland-Watson Center for Life Sciences and Technology, Shenzhen, China
| | - Chuan Chen
- Shenzhen Cheerland Biotechnology Co., Ltd., Cheerland-Watson Center for Life Sciences and Technology, Shenzhen, China
| | - Wenyong Zhang
- School of Medicine, Southern University of Science and Technology, Shenzhen, China
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99
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Li X, Ai H, Li B, Zhang C, Meng F, Ai Y. MIMRDA: A Method Incorporating the miRNA and mRNA Expression Profiles for Predicting miRNA-Disease Associations to Identify Key miRNAs (microRNAs). Front Genet 2022; 13:825318. [PMID: 35154284 PMCID: PMC8829120 DOI: 10.3389/fgene.2022.825318] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/10/2022] [Indexed: 01/22/2023] Open
Abstract
Identifying cancer-related miRNAs (or microRNAs) that precisely target mRNAs is important for diagnosis and treatment of cancer. Creating novel methods to identify candidate miRNAs becomes an imminent Frontier of researches in the field. One major obstacle lies in the integration of the state-of-the-art databases. Here, we introduce a novel method, MIMRDA, which incorporates the miRNA and mRNA expression profiles for predicting miRNA-disease associations to identify key miRNAs. As a proof-of-principle study, we use the MIMRDA method to analyze TCGA datasets of 20 types (BLCA, BRCA, CESE, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, READ, SKCM, STAD, THCA and UCEC) of cancer, which identified hundreds of top-ranked miRNAs. Some (as Category 1) of them are endorsed by public databases including TCGA, miRTarBase, miR2Disease, HMDD, MISIM, ncDR and mTD; others (as Category 2) are supported by literature evidences. miR-21 (representing Category 1) and miR-1258 (representing Category 2) display the excellent characteristics of biomarkers in multi-dimensional assessments focusing on the function similarity analysis, overall survival analysis, and anti-cancer drugs’ sensitivity or resistance analysis. We compare the performance of the MIMRDA method over the Limma and SPIA packages, and estimate the accuracy of the MIMRDA method in classifying top-ranked miRNAs via the Random Forest simulation test. Our results indicate the superiority and effectiveness of the MIMRDA method, and recommend some top-ranked key miRNAs be potential biomarkers that warrant experimental validations.
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Affiliation(s)
- Xianbin Li
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Hannan Ai
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- Department of Electrical and Computer Engineering, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
- National Center for Quality Supervision and Inspection of Automatic Equipment, National Center for Testing and Evaluation of Robots (Guangzhou), CRAT, SINOMACH-IT, Guangzhou, China
- *Correspondence: Yuncan Ai, ; Hannan Ai,
| | - Bizhou Li
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Chaohui Zhang
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Fanmei Meng
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
| | - Yuncan Ai
- State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Yuncan Ai, ; Hannan Ai,
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Yu L, Zheng Y, Gao L. MiRNA-disease association prediction based on meta-paths. Brief Bioinform 2022; 23:6501422. [PMID: 35018405 DOI: 10.1093/bib/bbab571] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/02/2021] [Accepted: 12/11/2021] [Indexed: 01/09/2023] Open
Abstract
Since miRNAs can participate in the posttranscriptional regulation of gene expression, they may provide ideas for the development of new drugs or become new biomarkers for drug targets or disease diagnosis. In this work, we propose an miRNA-disease association prediction method based on meta-paths (MDPBMP). First, an miRNA-disease-gene heterogeneous information network was constructed, and seven symmetrical meta-paths were defined according to different semantics. After constructing the initial feature vector for the node, the vector information carried by all nodes on the meta-path instance is extracted and aggregated to update the feature vector of the starting node. Then, the vector information obtained by the nodes on different meta-paths is aggregated. Finally, miRNA and disease embedding feature vectors are used to calculate their associated scores. Compared with the other methods, MDPBMP obtained the highest AUC value of 0.9214. Among the top 50 predicted miRNAs for lung neoplasms, esophageal neoplasms, colon neoplasms and breast neoplasms, 49, 48, 49 and 50 have been verified. Furthermore, for breast neoplasms, we deleted all the known associations between breast neoplasms and miRNAs from the training set. These results also show that for new diseases without known related miRNA information, our model can predict their potential miRNAs. Code and data are available at https://github.com/LiangYu-Xidian/MDPBMP.
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Affiliation(s)
- Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an 710071, P.R. China
| | - Yujia Zheng
- School of Computer Science and Technology, Xidian University, Xi'an 710071, P.R. China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an 710071, P.R. China
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