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Xiong C, Zhang M, Yang H, Wei X, Zhao C, Zhang J. Modelling cell type-specific lncRNA regulatory network in autism with Cycle. BMC Bioinformatics 2024; 25:307. [PMID: 39333906 PMCID: PMC11430139 DOI: 10.1186/s12859-024-05933-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Accepted: 09/17/2024] [Indexed: 09/30/2024] Open
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a class of complex neurodevelopment disorders with high genetic heterogeneity. Long non-coding RNAs (lncRNAs) are vital regulators that perform specific functions within diverse cell types and play pivotal roles in neurological diseases including ASD. Therefore, exploring lncRNA regulation would contribute to deciphering ASD molecular mechanisms. Existing computational methods utilize bulk transcriptomics data to identify lncRNA regulation in all of samples, which could reveal the commonalities of lncRNA regulation in ASD, but ignore the specificity of lncRNA regulation across various cell types. RESULTS Here, we present Cycle (Cell type-specific lncRNA regulatory network) to construct the landscape of cell type-specific lncRNA regulation in ASD. We have found that each ASD cell type is unique in lncRNA regulation, and more than one-third and all cell type-specific lncRNA regulatory networks are characterized as scale-free and small-world, respectively. Across 17 ASD cell types, we have discovered 19 rewired and 11 stable modules, along with eight rewired and three stable hubs within the constructed cell type-specific lncRNA regulatory networks. Enrichment analysis reveals that the discovered rewired and stable modules and hubs are closely related to ASD. Furthermore, more similar ASD cell types tend to be connected with higher strength in the constructed cell similarity network. Finally, the comparison results demonstrate that Cycle is a potential method for uncovering cell type-specific lncRNA regulation. CONCLUSION Overall, these results illustrate that Cycle is a promising method to model the landscape of cell type-specific lncRNA regulation, and provides insights into understanding the heterogeneity of lncRNA regulation between various ASD cell types.
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Affiliation(s)
- Chenchen Xiong
- School of Engineering, Dali University, Dali, Yunnan, China
- Beijing CapitalBio Pharma Technology Co.,Ltd., Beijing, China
| | | | - Haolin Yang
- School of Engineering, Dali University, Dali, Yunnan, China
| | - Xuemei Wei
- School of Engineering, Dali University, Dali, Yunnan, China
| | - Chunwen Zhao
- School of Engineering, Dali University, Dali, Yunnan, China
| | - Junpeng Zhang
- School of Engineering, Dali University, Dali, Yunnan, China.
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2
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Chaudhary U, Banerjee S. Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration. ACS Pharmacol Transl Sci 2024; 7:1901-1915. [PMID: 39022352 PMCID: PMC11249652 DOI: 10.1021/acsptsci.3c00388] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 05/15/2024] [Accepted: 05/27/2024] [Indexed: 07/20/2024]
Abstract
Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.
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Affiliation(s)
- Uma Chaudhary
- Department of Biotechnology,
School of Biosciences and Technology, Vellore
Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
| | - Satarupa Banerjee
- Department of Biotechnology,
School of Biosciences and Technology, Vellore
Institute of Technology (VIT), Vellore, Tamil Nadu 632014, India
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3
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Shahamiri K, Alghasi A, Saki N, Teimori H, Kaydani GA, sheikhi S. Upregulation of the long noncoding RNA GJA9-MYCBP and PVT1 is a potential diagnostic biomarker for acute lymphoblastic leukemia. Cancer Rep (Hoboken) 2024; 7:e2115. [PMID: 38994720 PMCID: PMC11240143 DOI: 10.1002/cnr2.2115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 02/27/2024] [Accepted: 05/14/2024] [Indexed: 07/13/2024] Open
Abstract
BACKGROUND Acute lymphoblastic leukemia (ALL) is the most common type of blood cancer in children. Aberrant expression of long noncoding RNAs (lncRNAs) may set stages for ALL development. LncRNAs are emerging as a novel diagnostic and prognostic biomarker for ALL. Herein, we aimed to evaluate the expression of lncRNA GJA9-MYCBP and PVT1 in blood samples of ALL and healthy individuals. METHODS As a case-control study, 40 pairs of ALL and healthy individual samples were used. The expression of MYC and each candidate lncRNA was measured using quantitative real-time PCR. Any possible association between the expression of putative noncoding RNAs and clinicopathological characteristics was also evaluated. RESULTS LncRNA GJA9-MYCBP and PVT1 were significantly upregulated in ALL samples compared with healthy ones. Similarly, mRNA levels of MYC were increased in ALL samples than control ones. Receiver operating characteristic curve analysis indicated a satisfactory diagnostic efficacy (p-value <.0001), suggesting that lncRNA GJA9-MYCBP and PVT1 may serve as a diagnostic biomarker for ALL. Linear regression analysis unveiled positive correlations between the expression level of MYC and lncRNA GJA9-MYCBP and PVT1 in ALL patients (p-values <.01). CONCLUSIONS In this study, we provided approval for the clinical diagnostic significance of lncRNA GJA9-MYCBP and PVT1that their upregulations may be a diagnostic biomarker for ALL.
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Affiliation(s)
- Kamal Shahamiri
- Cellular and Molecular Research Center, Basic Health Sciences InstituteShahrekord University of Medical SciencesShahrekordIran
| | - Arash Alghasi
- Thalassemia & Hemoglobinopathy Research center, Health research instituteAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Najmaldin Saki
- Thalassemia & Hemoglobinopathy Research center, Health research instituteAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Hossein Teimori
- Cellular and Molecular Research Center, Basic Health Sciences InstituteShahrekord University of Medical SciencesShahrekordIran
| | - Gholam Abbas Kaydani
- Department of Laboratory Sciences, School of Allied Medical SciencesAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Setare sheikhi
- Department of Hematology and Blood Transfusion, School of Allied Medical SciencesTehran University of Medical scienceTehranIran
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4
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Elmasri RA, Rashwan AA, Gaber SH, Rostom MM, Karousi P, Yasser MB, Kontos CK, Youness RA. Puzzling out the role of MIAT LncRNA in hepatocellular carcinoma. Noncoding RNA Res 2024; 9:547-559. [PMID: 38515792 PMCID: PMC10955557 DOI: 10.1016/j.ncrna.2024.01.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 12/31/2023] [Accepted: 01/09/2024] [Indexed: 03/23/2024] Open
Abstract
A non-negligible part of our DNA has been proven to be transcribed into non-protein coding RNA and its intricate involvement in several physiological processes has been highly evidenced. The significant biological role of non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs) has been variously reported. In the current review, the authors highlight the multifaceted role of myocardial infarction-associated transcript (MIAT), a well-known lncRNA, in hepatocellular carcinoma (HCC). Since its discovery, MIAT has been described as a regulator of carcinogenesis in several malignant tumors and its overexpression predicts poor prognosis in most of them. At the molecular level, MIAT is closely linked to the initiation of metastasis, invasion, cellular migration, and proliferation, as evidenced by several in-vitro and in-vivo models. Thus, MIAT is considered a possible theranostic agent and therapeutic target in several malignancies. In this review, the authors provide a comprehensive overview of the underlying molecular mechanisms of MIAT in terms of its downstream target genes, interaction with other classes of ncRNAs, and potential clinical implications as a diagnostic and/or prognostic biomarker in HCC.
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Affiliation(s)
- Rawan Amr Elmasri
- Molecular Genetics Research Team (MGRT), Biology and Biochemistry Department, Faculty of Biotechnology, German International University (GIU), New Administrative Capital, 11835, Cairo, Egypt
| | - Alaa A. Rashwan
- Molecular Genetics Research Team (MGRT), Biology and Biochemistry Department, Faculty of Biotechnology, German International University (GIU), New Administrative Capital, 11835, Cairo, Egypt
- Biotechnology Graduate Program, School of Sciences and Engineering, The American University in Cairo (AUC), 11835, Cairo, Egypt
| | - Sarah Hany Gaber
- Molecular Genetics Research Team (MGRT), Biology and Biochemistry Department, Faculty of Biotechnology, German International University (GIU), New Administrative Capital, 11835, Cairo, Egypt
| | - Monica Mosaad Rostom
- Pharmacology and Toxicology Department, Faculty of Pharmacy and Biotechnology, German University in Cairo (GUC), 11835, Cairo, Egypt
| | - Paraskevi Karousi
- Department of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, 15701, Athens, Greece
| | - Montaser Bellah Yasser
- Bioinformatics Group, Center for Informatics Sciences (CIS), School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt
| | - Christos K. Kontos
- Department of Biochemistry and Molecular Biology, Faculty of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, 15701, Athens, Greece
| | - Rana A. Youness
- Molecular Genetics Research Team (MGRT), Biology and Biochemistry Department, Faculty of Biotechnology, German International University (GIU), New Administrative Capital, 11835, Cairo, Egypt
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5
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Zeinelabdeen Y, Abaza T, Yasser MB, Elemam NM, Youness RA. MIAT LncRNA: A multifunctional key player in non-oncological pathological conditions. Noncoding RNA Res 2024; 9:447-462. [PMID: 38511054 PMCID: PMC10950597 DOI: 10.1016/j.ncrna.2024.01.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 12/27/2023] [Accepted: 01/14/2024] [Indexed: 03/22/2024] Open
Abstract
The discovery of non-coding RNAs (ncRNAs) has unveiled a wide range of transcripts that do not encode proteins but play key roles in several cellular and molecular processes. Long noncoding RNAs (lncRNAs) are specific class of ncRNAs that are longer than 200 nucleotides and have gained significant attention due to their diverse mechanisms of action and potential involvement in various pathological conditions. In the current review, the authors focus on the role of lncRNAs, specifically highlighting the Myocardial Infarction Associated Transcript (MIAT), in non-oncological context. MIAT is a nuclear lncRNA that has been directly linked to myocardial infarction and is reported to control post-transcriptional processes as a competitive endogenous RNA (ceRNA) molecule. It interacts with microRNAs (miRNAs), thereby limiting the translation and expression of their respective target messenger RNA (mRNA) and regulating protein expression. Yet, MIAT has been implicated in other numerous pathological conditions such as other cardiovascular diseases, autoimmune disease, neurodegenerative diseases, metabolic diseases, and many others. In this review, the authors emphasize that MIAT exhibits distinct expression patterns and functions across different pathological conditions and is emerging as potential diagnostic, prognostic, and therapeutic agent. Additionally, the authors highlight the regulatory role of MIAT and shed light on the involvement of lncRNAs and specifically MIAT in various non-oncological pathological conditions.
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Affiliation(s)
- Yousra Zeinelabdeen
- Molecular Genetics Research Team, Molecular Biology and Biochemistry Department, Faculty of Biotechnology, German International University (GIU), Cairo, 11835, Egypt
- Faculty of Medical Sciences/UMCG, University of Groningen, Antonius Deusinglaan 1, Groningen, 9713 AV, the Netherlands
| | - Tasneem Abaza
- Molecular Genetics Research Team, Molecular Biology and Biochemistry Department, Faculty of Biotechnology, German International University (GIU), Cairo, 11835, Egypt
- Biotechnology and Biomolecular Biochemistry Program, Faculty of Science, Cairo University, Cairo, Egypt
| | - Montaser Bellah Yasser
- Bioinformatics Group, Center for Informatics Sciences (CIS), School of Information Technology and Computer Science (ITCS), Nile University, Giza, Egypt
| | - Noha M. Elemam
- Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
- Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Rana A. Youness
- Molecular Genetics Research Team, Molecular Biology and Biochemistry Department, Faculty of Biotechnology, German International University (GIU), Cairo, 11835, Egypt
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6
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DeSouza NR, Jarboe T, Carnazza M, Quaranto D, Islam HK, Tiwari RK, Geliebter J. Long Non-Coding RNAs as Determinants of Thyroid Cancer Phenotypes: Investigating Differential Gene Expression Patterns and Novel Biomarker Discovery. BIOLOGY 2024; 13:304. [PMID: 38785786 PMCID: PMC11118935 DOI: 10.3390/biology13050304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 04/18/2024] [Accepted: 04/22/2024] [Indexed: 05/25/2024]
Abstract
Thyroid Cancer (TC) is the most common endocrine malignancy, with increasing incidence globally. Papillary thyroid cancer (PTC), a differentiated form of TC, accounts for approximately 90% of TC and occurs predominantly in women of childbearing age. Although responsive to current treatments, recurrence of PTC by middle age is common and is much more refractive to treatment. Undifferentiated TC, particularly anaplastic thyroid cancer (ATC), is the most aggressive TC subtype, characterized by it being resistant and unresponsive to all therapeutic and surgical interventions. Further, ATC is one of the most aggressive and lethal malignancies across all cancer types. Despite the differences in therapeutic needs in differentiated vs. undifferentiated TC subtypes, there is a critical unmet need for the identification of molecular biomarkers that can aid in early diagnosis, prognosis, and actionable therapeutic targets for intervention. Advances in the field of cancer genomics have enabled for the elucidation of differential gene expression patterns between tumors and healthy tissue. A novel category of molecules, known as non-coding RNAs, can themselves be differentially expressed, and extensively contribute to the up- and downregulation of protein coding genes, serving as master orchestrators of regulated and dysregulated gene expression patterns. These non-coding RNAs have been identified for their roles in driving carcinogenic patterns at various stages of tumor development and have become attractive targets for study. The identification of specific genes that are differentially expressed can give insight into mechanisms that drive carcinogenic patterns, filling the gaps of deciphering molecular and cellular processes that modulate TC subtypes, outside of well-known driver mutations.
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Affiliation(s)
- Nicole R. DeSouza
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA; (N.R.D.); (T.J.); (H.K.I.); (R.K.T.)
| | - Tara Jarboe
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA; (N.R.D.); (T.J.); (H.K.I.); (R.K.T.)
| | - Michelle Carnazza
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA; (N.R.D.); (T.J.); (H.K.I.); (R.K.T.)
| | - Danielle Quaranto
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA; (N.R.D.); (T.J.); (H.K.I.); (R.K.T.)
| | - Humayun K. Islam
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA; (N.R.D.); (T.J.); (H.K.I.); (R.K.T.)
| | - Raj K. Tiwari
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA; (N.R.D.); (T.J.); (H.K.I.); (R.K.T.)
- Department of Otolaryngology, New York Medical College, Valhalla, NY 10595, USA
| | - Jan Geliebter
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA; (N.R.D.); (T.J.); (H.K.I.); (R.K.T.)
- Department of Otolaryngology, New York Medical College, Valhalla, NY 10595, USA
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7
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Swindell WR. Meta-analysis of differential gene expression in lower motor neurons isolated by laser capture microdissection from post-mortem ALS spinal cords. Front Genet 2024; 15:1385114. [PMID: 38689650 PMCID: PMC11059082 DOI: 10.3389/fgene.2024.1385114] [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: 02/11/2024] [Accepted: 04/03/2024] [Indexed: 05/02/2024] Open
Abstract
Introduction ALS is a fatal neurodegenerative disease for which underlying mechanisms are incompletely understood. The motor neuron is a central player in ALS pathogenesis but different transcriptome signatures have been derived from bulk analysis of post-mortem tissue and iPSC-derived motor neurons (iPSC-MNs). Methods This study performed a meta-analysis of six gene expression studies (microarray and RNA-seq) in which laser capture microdissection (LCM) was used to isolate lower motor neurons from post-mortem spinal cords of ALS and control (CTL) subjects. Differentially expressed genes (DEGs) with consistent ALS versus CTL expression differences across studies were identified. Results The analysis identified 222 ALS-increased DEGs (FDR <0.10, SMD >0.80) and 278 ALS-decreased DEGs (FDR <0.10, SMD < -0.80). ALS-increased DEGs were linked to PI3K-AKT signaling, innate immunity, inflammation, motor neuron differentiation and extracellular matrix. ALS-decreased DEGs were associated with the ubiquitin-proteosome system, microtubules, axon growth, RNA-binding proteins and synaptic membrane. ALS-decreased DEG mRNAs frequently interacted with RNA-binding proteins (e.g., FUS, HuR). The complete set of DEGs (increased and decreased) overlapped significantly with genes near ALS-associated SNP loci (p < 0.01). Transcription factor target motifs with increased proximity to ALS-increased DEGs were identified, most notably DNA elements predicted to interact with forkhead transcription factors (e.g., FOXP1) and motor neuron and pancreas homeobox 1 (MNX1). Some of these DNA elements overlie ALS-associated SNPs within known enhancers and are predicted to have genotype-dependent MNX1 interactions. DEGs were compared to those identified from SOD1-G93A mice and bulk spinal cord segments or iPSC-MNs from ALS patients. There was good correspondence with transcriptome changes from SOD1-G93A mice (r ≤ 0.408) but most DEGs were not differentially expressed in bulk spinal cords or iPSC-MNs and transcriptome-wide effect size correlations were weak (bulk tissue: r ≤ 0.207, iPSC-MN: r ≤ 0.037). Conclusion This study defines a robust transcriptome signature from LCM-based motor neuron studies of post-mortem tissue from ALS and CTL subjects. This signature differs from those obtained from analysis of bulk spinal cord segments and iPSC-MNs. Results provide insight into mechanisms underlying gene dysregulation in ALS and highlight connections between these mechanisms, ALS genetics, and motor neuron biology.
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Affiliation(s)
- William R. Swindell
- Department of Internal Medicine, Division of Hospital Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States
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8
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Dhaka B, Zimmerli M, Hanhart D, Moser M, Guillen-Ramirez H, Mishra S, Esposito R, Polidori T, Widmer M, García-Pérez R, Julio MKD, Pervouchine D, Melé M, Chouvardas P, Johnson R. Functional identification of cis-regulatory long noncoding RNAs at controlled false discovery rates. Nucleic Acids Res 2024; 52:2821-2835. [PMID: 38348970 PMCID: PMC11014264 DOI: 10.1093/nar/gkae075] [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: 05/31/2023] [Revised: 01/15/2024] [Accepted: 01/26/2024] [Indexed: 03/09/2024] Open
Abstract
A key attribute of some long noncoding RNAs (lncRNAs) is their ability to regulate expression of neighbouring genes in cis. However, such 'cis-lncRNAs' are presently defined using ad hoc criteria that, we show, are prone to false-positive predictions. The resulting lack of cis-lncRNA catalogues hinders our understanding of their extent, characteristics and mechanisms. Here, we introduce TransCistor, a framework for defining and identifying cis-lncRNAs based on enrichment of targets amongst proximal genes. TransCistor's simple and conservative statistical models are compatible with functionally defined target gene maps generated by existing and future technologies. Using transcriptome-wide perturbation experiments for 268 human and 134 mouse lncRNAs, we provide the first large-scale survey of cis-lncRNAs. Known cis-lncRNAs are correctly identified, including XIST, LINC00240 and UMLILO, and predictions are consistent across analysis methods, perturbation types and independent experiments. We detect cis-activity in a minority of lncRNAs, primarily involving activators over repressors. Cis-lncRNAs are detected by both RNA interference and antisense oligonucleotide perturbations. Mechanistically, cis-lncRNA transcripts are observed to physically associate with their target genes and are weakly enriched with enhancer elements. In summary, TransCistor establishes a quantitative foundation for cis-lncRNAs, opening a path to elucidating their molecular mechanisms and biological significance.
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Affiliation(s)
- Bhavya Dhaka
- School of Biology and Environmental Science, University College Dublin, Dublin D04 V1W8, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin D04 V1W8, Ireland
| | - Marc Zimmerli
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Daniel Hanhart
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Mario B Moser
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Hugo Guillen-Ramirez
- School of Biology and Environmental Science, University College Dublin, Dublin D04 V1W8, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin D04 V1W8, Ireland
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
- Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Sanat Mishra
- Indian Institute of Science Education and Research, Mohali, India
| | - Roberta Esposito
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Taisia Polidori
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Maro Widmer
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
| | - Raquel García-Pérez
- Department of Life Sciences, Barcelona Supercomputing Centre, Barcelona 08034, Spain
| | - Marianna Kruithof-de Julio
- Department of Urology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Urology Research Laboratory, Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Dmitri Pervouchine
- Center for Cellular and Molecular Biology, Skolkovo Institute of Science and Technology, Moscow, Russia
| | - Marta Melé
- Department of Life Sciences, Barcelona Supercomputing Centre, Barcelona 08034, Spain
| | - Panagiotis Chouvardas
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
- Department of Urology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Urology Research Laboratory, Department for BioMedical Research, University of Bern, 3008, Bern, Switzerland
| | - Rory Johnson
- School of Biology and Environmental Science, University College Dublin, Dublin D04 V1W8, Ireland
- Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin D04 V1W8, Ireland
- Department of Medical Oncology, Inselspital, Bern University Hospital, University of Bern, Bern 3010, Switzerland
- Department for BioMedical Research, University of Bern, Bern 3008, Switzerland
- FutureNeuro SFI Research Centre, University College Dublin, Dublin D04 V1W8, Ireland
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9
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Liadaki K, Zafiriou E, Giannoulis T, Alexouda S, Chaidaki K, Gidarokosta P, Roussaki-Schulze AV, Tsiogkas SG, Daponte A, Mamuris Z, Bogdanos DP, Moschonas NK, Sarafidou T. PDE4 Gene Family Variants Are Associated with Response to Apremilast Treatment in Psoriasis. Genes (Basel) 2024; 15:369. [PMID: 38540428 PMCID: PMC10970167 DOI: 10.3390/genes15030369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/07/2024] [Accepted: 03/14/2024] [Indexed: 06/14/2024] Open
Abstract
Moderate-to-severe psoriasis (Ps) treatment includes systemic drugs and biological agents. Apremilast, a small molecule primarily metabolized by cytochrome CYP3A4, modulates the immune system by specifically inhibiting phosphodiesterase type 4 (PDE4) isoforms and is currently used for the treatment of Ps and psoriatic arthritis (PsA). Clinical trials and real-world data showed variable efficacy in response among Ps patients underlying the need for personalized therapy. This study implements a candidate-gene and a network-based approach to identify genetic markers associated with apremilast response in forty-nine Greek Ps patients. Our data revealed an association of sixty-four SNPs within or near PDE4 and CYP3A4 genes, four SNPs in ncRNAs ANRIL, LINC00941 and miR4706, which influence the abundance or function of PDE4s, and thirty-three SNPs within fourteen genes whose protein products either interact directly with PDE4 proteins or constitute components of the cAMP signaling pathway which is modulated by PDE4s. Notably, fifty-six of the aforementioned SNPs constitute eQTLs for the respective genes in relevant to psoriasis tissues/cells implying that these variants could be causal. Our analysis provides a number of novel genetic variants that, upon validation in larger cohorts, could be utilized as predictive markers regarding the response of Ps patients to apremilast treatment.
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Affiliation(s)
- Kalliopi Liadaki
- Department of Biochemistry and Biotechnology, University of Thessaly, Viopolis, 41500 Larissa, Greece; (K.L.); (Z.M.)
| | - Efterpi Zafiriou
- Department of Dermatology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Viopolis, 41500 Larissa, Greece; (E.Z.); (K.C.); (P.G.); (A.-V.R.-S.)
| | | | - Sofia Alexouda
- Department of Biochemistry and Biotechnology, University of Thessaly, Viopolis, 41500 Larissa, Greece; (K.L.); (Z.M.)
| | - Kleoniki Chaidaki
- Department of Dermatology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Viopolis, 41500 Larissa, Greece; (E.Z.); (K.C.); (P.G.); (A.-V.R.-S.)
| | - Polyxeni Gidarokosta
- Department of Dermatology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Viopolis, 41500 Larissa, Greece; (E.Z.); (K.C.); (P.G.); (A.-V.R.-S.)
| | - Angeliki-Viktoria Roussaki-Schulze
- Department of Dermatology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Viopolis, 41500 Larissa, Greece; (E.Z.); (K.C.); (P.G.); (A.-V.R.-S.)
| | - Sotirios G. Tsiogkas
- Department of Rheumatology and Clinical Immunology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Viopolis, 41500 Larissa, Greece; (S.G.T.); (A.D.); (D.P.B.)
| | - Athina Daponte
- Department of Rheumatology and Clinical Immunology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Viopolis, 41500 Larissa, Greece; (S.G.T.); (A.D.); (D.P.B.)
| | - Zissis Mamuris
- Department of Biochemistry and Biotechnology, University of Thessaly, Viopolis, 41500 Larissa, Greece; (K.L.); (Z.M.)
| | - Dimitrios P. Bogdanos
- Department of Rheumatology and Clinical Immunology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Viopolis, 41500 Larissa, Greece; (S.G.T.); (A.D.); (D.P.B.)
| | - Nicholas K. Moschonas
- School of Medicine, University of Patras, 26500 Patras, Greece
- Foundation for Research and Technology Hellas, Institute of Chemical Engineering Sciences, 26504 Patras, Greece
| | - Theologia Sarafidou
- Department of Biochemistry and Biotechnology, University of Thessaly, Viopolis, 41500 Larissa, Greece; (K.L.); (Z.M.)
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10
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Zou HT, Ji BY, Xie XL. A multi-source molecular network representation model for protein-protein interactions prediction. Sci Rep 2024; 14:6184. [PMID: 38485942 PMCID: PMC10940665 DOI: 10.1038/s41598-024-56286-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 03/05/2024] [Indexed: 03/18/2024] Open
Abstract
The prediction of potential protein-protein interactions (PPIs) is a critical step in decoding diseases and understanding cellular mechanisms. Traditional biological experiments have identified plenty of potential PPIs in recent years, but this problem is still far from being solved. Hence, there is urgent to develop computational models with good performance and high efficiency to predict potential PPIs. In this study, we propose a multi-source molecular network representation learning model (called MultiPPIs) to predict potential protein-protein interactions. Specifically, we first extract the protein sequence features according to the physicochemical properties of amino acids by utilizing the auto covariance method. Second, a multi-source association network is constructed by integrating the known associations among miRNAs, proteins, lncRNAs, drugs, and diseases. The graph representation learning method, DeepWalk, is adopted to extract the multisource association information of proteins with other biomolecules. In this way, the known protein-protein interaction pairs can be represented as a concatenation of the protein sequence and the multi-source association features of proteins. Finally, the Random Forest classifier and corresponding optimal parameters are used for training and prediction. In the results, MultiPPIs obtains an average 86.03% prediction accuracy with 82.69% sensitivity at the AUC of 93.03% under five-fold cross-validation. The experimental results indicate that MultiPPIs has a good prediction performance and provides valuable insights into the field of potential protein-protein interactions prediction. MultiPPIs is free available at https://github.com/jiboyalab/multiPPIs .
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Affiliation(s)
- Hai-Tao Zou
- College of Information Science and Engineering, Guilin University of Technology, Guilin, 541000, China
| | - Bo-Ya Ji
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China.
| | - Xiao-Lan Xie
- College of Information Science and Engineering, Guilin University of Technology, Guilin, 541000, China.
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11
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Liu J, Li J, Jin F, Li Q, Zhao G, Wu L, Li X, Xia J, Cheng N. dbCRAF: a curated knowledgebase for regulation of radiation response in human cancer. NAR Cancer 2024; 6:zcae008. [PMID: 38406264 PMCID: PMC10894039 DOI: 10.1093/narcan/zcae008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/10/2023] [Accepted: 02/15/2024] [Indexed: 02/27/2024] Open
Abstract
Radiation therapy (RT) is one of the primary treatment modalities of cancer, with 40-60% of cancer patients benefiting from RT during their treatment course. The intrinsic radiosensitivity or acquired radioresistance of tumor cells would affect the response to RT and clinical outcomes in patients. Thus, mining the regulatory mechanisms in tumor radiosensitivity or radioresistance that have been verified by biological experiments and computational analysis methods will enhance the overall understanding of RT. Here, we describe a comprehensive database dbCRAF (http://dbCRAF.xialab.info/) to document and annotate the factors (1,677 genes, 49 proteins and 612 radiosensitizers) linked with radiation response, including radiosensitivity, radioresistance in cancer cells and prognosis in cancer patients receiving RT. On the one hand, dbCRAF enables researchers to directly access knowledge for regulation of radiation response in human cancer buried in the vast literature. On the other hand, dbCRAF provides four flexible modules to analyze and visualize the functional relationship between these factors and clinical outcome, KEGG pathway and target genes. In conclusion, dbCRAF serves as a valuable resource for elucidating the regulatory mechanisms of radiation response in human cancers as well as for the improvement of RT options.
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Affiliation(s)
- Jie Liu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Jing Li
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Fangfang Jin
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Qian Li
- School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, Anhui 230026, China
| | - Guoping Zhao
- Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China
| | - Lijun Wu
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
- Key Laboratory of High Magnetic Field and Ion Beam Physical Biology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui 230031, China
| | - Xiaoyan Li
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Junfeng Xia
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education and Information Materials and Intelligent Sensing Laboratory of Anhui Province, Institutes of Physical Science and Information Technology, Anhui University, Hefei, Anhui 230601, China
| | - Na Cheng
- School of Biomedical Engineering, Anhui Medical University, Hefei, Anhui 230032, China
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12
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Wen Y, Li C, Huang P, Liu Z, He Y, Liu B. Transcriptional landscape of intestinal environment in DSS-induced ulcerative colitis mouse model. BMC Gastroenterol 2024; 24:60. [PMID: 38308210 PMCID: PMC10836045 DOI: 10.1186/s12876-024-03128-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 01/05/2024] [Indexed: 02/04/2024] Open
Abstract
Ulcerative colitis (UC) is a chronic inflammatory disease that targets the colon and has seen an increasing prevalence worldwide. In our pursuit of new diagnostic and therapeutic approaches for UC, we undertook a sequencing of colons from UC mouse models. We focused on analyzing their differentially expressed genes (DEGs), enriching pathways, and constructing protein-protein interaction (PPI) and Competing Endogenous RNA (ceRNA) networks. Our analysis highlighted novel DEGs such as Tppp3, Saa3, Cemip, Pappa, and Nr1d1. These DEGs predominantly play roles in pathways like cytokine-mediated signaling, extracellular matrix organization, extracellular structure organization, and external encapsulating structure organization. This suggests that the UC pathogenesis is intricately linked to the interactions between immune and non-immune cells with the extracellular matrix (ECM). To corroborate our findings, we also verified certain DEGs through quantitative real-time PCR. Within the PPI network, nodes like Stat3, Il1b, Mmp3, and Lgals3 emerged as significant and were identified to be involved in the crucial cytokine-mediated signaling pathway, which is central to inflammation. Our ceRNA network analysis further brought to light the role of the Smad7 Long non-coding RNA (lncRNA). Key MicroRNA (miRNAs) in the ceRNA network were pinpointed as mmu-miR-17-5p, mmu-miR-93-5p, mmu-miR-20b-5p, mmu-miR-16-5p, and mmu-miR-106a-5p, while central mRNAs included Egln3, Plagl2, Sema7a, Arrdc3, and Stat3. These insights imply that ceRNA networks are influential in UC progression and could provide further clarity on its pathogenesis. In conclusion, this research deepens our understanding of UC pathogenesis and paves the way for potential new diagnostic and therapeutic methods. Nevertheless, to solidify our findings, additional experiments are essential to confirm the roles and molecular interplay of the identified DEGs in UC.
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Affiliation(s)
- Yuefei Wen
- Foshan Maternity and Child Health Care Hospital, Southern Medical University, 528000, Foshan, China
| | - Chenyang Li
- Zhujiang Hospital, Southern Medical University, 510282, Guangzhou, China
| | - Peng Huang
- Foshan Maternity and Child Health Care Hospital, Southern Medical University, 528000, Foshan, China
| | - Zhigang Liu
- Foshan Maternity and Child Health Care Hospital, Southern Medical University, 528000, Foshan, China
| | - Yanjun He
- Foshan Maternity and Child Health Care Hospital, Southern Medical University, 528000, Foshan, China.
- Zhujiang Hospital, Southern Medical University, 510282, Guangzhou, China.
| | - Bin Liu
- Zhujiang Hospital, Southern Medical University, 510282, Guangzhou, China.
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13
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Das G, Das T, Parida S, Ghosh Z. LncRTPred: Predicting RNA-RNA mode of interaction mediated by lncRNA. IUBMB Life 2024; 76:53-68. [PMID: 37606159 DOI: 10.1002/iub.2778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 07/19/2023] [Indexed: 08/23/2023]
Abstract
Long non-coding RNAs (lncRNAs) play a significant role in various biological processes. Hence, it is utmost important to elucidate their functions in order to understand the molecular mechanism of a complex biological system. This versatile RNA molecule has diverse modes of interaction, one of which constitutes lncRNA-mRNA interaction. Hence, identifying its target mRNA is essential to understand the function of an lncRNA explicitly. Existing lncRNA target prediction tools mainly adopt thermodynamics approach. Large execution time and inability to perform real-time prediction limit their usage. Further, lack of negative training dataset has been a hindrance in the path of developing machine learning (ML) based lncRNA target prediction tools. In this work, we have developed a ML-based lncRNA-mRNA target prediction model- 'LncRTPred'. Here we have addressed the existing problems by generating reliable negative dataset and creating robust ML models. We have identified the non-interacting lncRNA and mRNAs from the unlabelled dataset using BLAT. It is further filtered to get a reliable set of outliers. LncRTPred provides a cumulative_model_score as the final output against each query. In terms of prediction accuracy, LncRTPred outperforms other popular target prediction protocols like LncTar. Further, we have tested its performance against experimentally validated disease-specific lncRNA-mRNA interactions. Overall, performance of LncRTPred is heavily dependent on the size of the training dataset, which is highly reflected by the difference in its performance for human and mouse species. Its performance for human species shows better as compared to that for mouse when applied on an unknown data due to smaller size of the training dataset in case of mouse compared to that of human. Availability of increased number of lncRNA-mRNA interaction data for mouse will improve the performance of LncRTPred in future. Both webserver and standalone versions of LncRTPred are available. Web server link: http://bicresources.jcbose.ac.in/zhumur/lncrtpred/index.html. Github Link: https://github.com/zglabDIB/LncRTPred.
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Affiliation(s)
- Gourab Das
- Division of Bioinformatics, Bose Institute, Kolkata, India
| | - Troyee Das
- Division of Bioinformatics, Bose Institute, Kolkata, India
| | - Sibun Parida
- Division of Bioinformatics, Bose Institute, Kolkata, India
| | - Zhumur Ghosh
- Division of Bioinformatics, Bose Institute, Kolkata, India
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14
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Xie W, Chen X, Zheng Z, Wang F, Zhu X, Lin Q, Sun Y, Wong KC. LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms. iScience 2023; 26:108197. [PMID: 37965148 PMCID: PMC10641498 DOI: 10.1016/j.isci.2023.108197] [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: 04/24/2023] [Revised: 08/10/2023] [Accepted: 10/10/2023] [Indexed: 11/16/2023] Open
Abstract
By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships. Specifically, we constructed controlled deep-learning methods using 12417 lncRNAs and 16127 genes. We have provided retrospective and innovative views among negative sampling, random seeds, cross-validation, metrics, and independent datasets. The AUC, AUPR, and our defined precision@k were leveraged to evaluate performance. In-depth case studies demonstrate that 47 out of 100 projected top unknown pairings were recorded in publications, supporting the predictive power. Our additional software can annotate the scores with target candidates. The lncRNA-Top will be a helpful tool to uncover prospective lncRNA targets and better comprehend the regulatory processes of lncRNAs.
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Affiliation(s)
- Weidun Xie
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Xingjian Chen
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Zetian Zheng
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Fuzhou Wang
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Xiaowei Zhu
- Department of Neuroscience, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Qiuzhen Lin
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Yanni Sun
- Department of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
| | - Ka-Chun Wong
- Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
- Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
- Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
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15
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Han L, Wang Z, Li C, Fan M, Wang Y, Sun G, Dai G. Functional identification and prediction of lncRNAs in esophageal cancer. Comput Biol Med 2023; 165:107205. [PMID: 37611425 DOI: 10.1016/j.compbiomed.2023.107205] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/29/2023] [Accepted: 06/25/2023] [Indexed: 08/25/2023]
Abstract
Esophageal cancer is a highly lethal malignancy with poor prognosis, and the identification of molecular biomarkers is crucial for improving diagnosis and treatment. Long non-coding RNAs (lncRNAs) have been shown to play important roles in the development and progression of esophageal cancer. However, due to the time cost of biological experiments, only a small number of lncRNAs related to esophageal cancer have been discovered. Currently, computational methods have emerged as powerful tools for identifying and characterizing lncRNAs, as well as predicting their potential functions. Therefore, this article proposes a transformer-based method for identifying esophageal cancer-related lncRNAs. Experimental results show that the AUC and AUPR of this method are superior to other comparison methods, with an AUC of 0.87 and an AUPR of 0.83, and the identified lncRNA targets are closely associated with esophageal cancer. We focus on the role of esophageal cancer-related lncRNAs in the immune microenvironment, and fully explore the functions of the target genes regulated by lncRNAs. Enrichment analysis shows that the predicted target genes are related to multiple pathways involved in the occurrence, development, and prognosis of esophageal cancer. This not only demonstrates the effectiveness of the method but also indicates the accuracy of the prediction results.
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Affiliation(s)
- Lu Han
- Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China; Medical School of Chinese PLA, Beijing, China
| | - Zhikuan Wang
- Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Congyong Li
- Medical School of Chinese PLA, Beijing, China; Sixth Health Care Department, The Second Medical Center of PLA General Hospital, Beijing, China
| | - Mengjiao Fan
- Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China; Medical School of Chinese PLA, Beijing, China
| | - Yanrong Wang
- Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China; Medical School of Chinese PLA, Beijing, China
| | - Gang Sun
- Department of Gastroenterology and Hepatology, The First Medical Center of Chinese PLA General Hospital, Beijing, China.
| | - Guanghai Dai
- Department of Oncology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China.
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16
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Wang H, Liu B, Long J, Yu J, Ji X, Li J, Zhu N, Zhuang X, Li L, Chen Y, Liu Z, Wang S, Zhao S. Integrative analysis identifies two molecular and clinical subsets in Luminal B breast cancer. iScience 2023; 26:107466. [PMID: 37636034 PMCID: PMC10448479 DOI: 10.1016/j.isci.2023.107466] [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: 09/28/2022] [Revised: 01/30/2023] [Accepted: 07/21/2023] [Indexed: 08/29/2023] Open
Abstract
Comprehensive multiplatform analysis of Luminal B breast cancer (LBBC) specimens identifies two molecularly distinct, clinically relevant subtypes: Cluster A associated with cell cycle and metabolic signaling and Cluster B with predominant epithelial mesenchymal transition (EMT) and immune response pathways. Whole-exome sequencing identified significantly mutated genes including TP53, PIK3CA, ERBB2, and GATA3 with recurrent somatic mutations. Alterations in DNA methylation or transcriptomic regulation in genes (FN1, ESR1, CCND1, and YAP1) result in tumor microenvironment reprogramming. Integrated analysis revealed enriched biological pathways and unexplored druggable targets (cancer-testis antigens, metabolic enzymes, kinases, and transcription regulators). A systematic comparison between mRNA and protein displayed emerging expression patterns of key therapeutic targets (CD274, YAP1, AKT1, and CDH1). A potential ceRNA network was developed with a significantly different prognosis between the two subtypes. This integrated analysis reveals a complex molecular landscape of LBBC and provides the utility of targets and signaling pathways for precision medicine.
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Affiliation(s)
- Huina Wang
- School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Bo Liu
- School of Mathematical and Computational Sciences, Massey University, Palmerston North 4472, New Zealand
| | - Junqi Long
- School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jiangyong Yu
- Department of Medical Oncology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Xinchan Ji
- School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jinmeng Li
- School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Nian Zhu
- School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Xujie Zhuang
- School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Lujia Li
- School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Yuhaoran Chen
- School of Software Engineering, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Zhidong Liu
- Department of Thoracic Surgery, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
| | - Shu Wang
- Breast Disease Center, Peking University People’s Hospital, Peking University, Beijing 100044, China
| | - Shuangtao Zhao
- Department of Thoracic Surgery, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
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17
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Thatai AKS, Ammankallu S, Devasahayam Arokia Balaya R, Soman SP, Nisar M, Babu S, John L, George A, Anto CK, Sanjeev D, Kandiyil MK, Raj SS, Awasthi K, Vinodchandra SS, Prasad TSK, Raju R. VirhostlncR: A comprehensive database to explore lncRNAs and their targets in viral infections. Comput Biol Med 2023; 164:107279. [PMID: 37572440 DOI: 10.1016/j.compbiomed.2023.107279] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 07/11/2023] [Accepted: 07/16/2023] [Indexed: 08/14/2023]
Abstract
Long non-coding-RNAs (lncRNAs) are an expanding set of cis-/trans-regulatory RNA genes that outnumber the protein-coding genes. Although being increasingly discovered, the functional role of the majority of lncRNAs in diverse biological conditions is undefined. Increasing evidence supports the critical role of lncRNAs in the emergence, regulation, and progression of various viral infections including influenza, hepatitis, coronavirus, and human immunodeficiency virus. Hence, the identification of signature lncRNAs would facilitate focused analysis of their functional roles accounting for their targets and regulatory mechanisms associated with infections. Towards this, we compiled 2803 lncRNAs identified to be modulated by 33 viral strains in various mammalian cell types and are provided through the resource named VirhostlncR (http://ciods.in/VirhostlncR/). The information on each of the viral strains, their multiplicity of infection, duration of infection, host cell name and cell types, fold change of lncRNA expression, and their specific identification methods are integrated into VirhostlncR. Based on the current datasets, we report 150 lncRNAs including differentiation antagonizing non-protein coding RNA (DANCR), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), maternally expressed gene 3 (MEG3), nuclear paraspeckle assembly transcript 1 (NEAT1), and plasmacytoma variant translocation 1 (PVT1) to be perturbed by two or more viruses. Analysis of viral protein interactions with human transcription factors (TFs) or TF-containing protein complexes identified that distinct viruses can transcriptionally regulate many of these lncRNAs through multiple protein complexes. Together, we believe that the current dataset will enable priority selection of lncRNAs for identification of their targets and serve as an effective platform for the analysis of noncoding RNA-mediated regulations in viral infections.
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Affiliation(s)
- Arun Kumar Sumaithangi Thatai
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - Shruthi Ammankallu
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - Rex Devasahayam Arokia Balaya
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Sreelakshmi Pathappillil Soman
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India; Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Mahammad Nisar
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Sreeranjini Babu
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India; Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Levin John
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Anju George
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - Christy Kallely Anto
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Diya Sanjeev
- Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India.
| | - Mrudula Kinarulla Kandiyil
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - Sini S Raj
- Department of Computer Science, University of Kerala, Thiruvananthapuram, 695 581, Kerala, India.
| | - Kriti Awasthi
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - S S Vinodchandra
- Department of Computer Science, University of Kerala, Thiruvananthapuram, 695 581, Kerala, India.
| | - Thottethodi Subrahmanya Keshava Prasad
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India; Omics Analytics Pvt. Ltd., Yenepoya Incubator, Deralakatte, Mangalore, 575 018, Karnataka, India.
| | - Rajesh Raju
- Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Deralakatte, Mangalore, 575 018, Karnataka, India; Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Manjanade, Mangalore, 575 018, Karnataka, India; Omics Analytics Pvt. Ltd., Yenepoya Incubator, Deralakatte, Mangalore, 575 018, Karnataka, India.
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18
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Triantaphyllopoulos KA. Long Non-Coding RNAs and Their "Discrete" Contribution to IBD and Johne's Disease-What Stands out in the Current Picture? A Comprehensive Review. Int J Mol Sci 2023; 24:13566. [PMID: 37686376 PMCID: PMC10487966 DOI: 10.3390/ijms241713566] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/23/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023] Open
Abstract
Non-coding RNAs (ncRNA) have paved the way to new perspectives on the regulation of gene expression, not only in biology and medicine, but also in associated fields and technologies, ensuring advances in diagnostic means and therapeutic modalities. Critical in this multistep approach are the associations of long non-coding RNA (lncRNA) with diseases and their causal genes in their networks of interactions, gene enrichment and expression analysis, associated pathways, the monitoring of the involved genes and their functional roles during disease progression from one stage to another. Studies have shown that Johne's Disease (JD), caused by Mycobacterium avium subspecies partuberculosis (MAP), shares common lncRNAs, clinical findings, and other molecular entities with Crohn's Disease (CD). This has been a subject of vigorous investigation owing to the zoonotic nature of this condition, although results are still inconclusive. In this review, on one hand, the current knowledge of lncRNAs in cells is presented, focusing on the pathogenesis of gastrointestinal-related pathologies and MAP-related infections and, on the other hand, we attempt to dissect the associated genes and pathways involved. Furthermore, the recently characterized and novel lncRNAs share common pathologies with IBD and JD, including the expression, molecular networks, and dataset analysis results. These are also presented in an attempt to identify potential biomarkers pertinent to cattle and human disease phenotypes.
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Affiliation(s)
- Kostas A Triantaphyllopoulos
- Department of Biotechnology, School of Applied Biology and Biotechnology, Agricultural University of Athens, 75 Iera Odos St., 11855 Athens, Greece
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19
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Saberi F, Dehghan Z, Noori E, Zali H. Identification of Renal Transplantation Rejection Biomarkers in Blood Using the Systems Biology Approach. IRANIAN BIOMEDICAL JOURNAL 2023; 27:375-87. [PMID: 38224029 PMCID: PMC10826908 DOI: 10.52547/ibj.3871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Accepted: 08/19/2023] [Indexed: 01/16/2024]
Abstract
Background Renal transplantation plays an essential role in the quality of life of patients with end-stage renal disease. At least 12% of the renal patients receiving transplantations show graft rejection. One of the methods used to diagnose renal transplantation rejection is renal allograft biopsy. This procedure is associated with some risks such as bleeding and arteriovenous fistula formation. In this study, we applied a bioinformatics approach to identify serum markers for graft rejection in patients receiving a renal transplantation. Methods Transcriptomic data were first retrieved from the blood of renal transplantation rejection patients using the GEO database. The data were then used to construct the protein-protein interaction and gene regulatory networks using Cytoscape software. Next, network analysis was performed to identify hub-bottlenecks, and key blood markers involved in renal graft rejection. Lastly, the gene ontology and functional pathways related to hub-bottlenecks were detected using PANTHER and DAVID servers. Results In PPIN and GRN, SYNCRIP, SQSTM1, GRAMD1A, FAM104A, ND2, TPGS2, ZNF652, RORA, and MALAT1 were the identified critical genes. In GRN, miR-155, miR17, miR146b, miR-200 family, and GATA2 were the factors that regulated critical genes. The MAPK, neurotrophin, and TNF signaling pathways, IL-17, and human cytomegalovirus infection, human papillomavirus infection, and shigellosis were identified as significant pathways involved in graft rejection. Concusion The above-mentioned genes can be used as diagnostic and therapeutic serum markers of transplantation rejection in renal patients. The newly predicted biomarkers and pathways require further studies.
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Affiliation(s)
- Fatemeh Saberi
- Student Research Committee, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zeinab Dehghan
- Department of Comparative Biomedical Sciences, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Effat Noori
- Student Research Committee, Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Cellular and Molecular Biology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hakimeh Zali
- Department of Tissue Engineering and Applied Cell Sciences, School of Advanced Technologies in Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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20
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Austin BK, Firooz A, Valafar H, Blenda AV. An Updated Overview of Existing Cancer Databases and Identified Needs. BIOLOGY 2023; 12:1152. [PMID: 37627037 PMCID: PMC10452211 DOI: 10.3390/biology12081152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/26/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lipidomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.
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Affiliation(s)
- Brittany K. Austin
- Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, SC 29605, USA;
| | - Ali Firooz
- Department of Computer Science and Engineering, College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA;
| | - Homayoun Valafar
- Department of Computer Science and Engineering, College of Engineering and Computing, University of South Carolina, Columbia, SC 29208, USA;
| | - Anna V. Blenda
- Department of Biomedical Sciences, School of Medicine Greenville, University of South Carolina, Greenville, SC 29605, USA;
- Prisma Health Cancer Institute, Prisma Health, Greenville, SC 29605, USA
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21
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Yang D, Shi M, You Q, Zhang Y, Hu Z, Xu J, Cai Q, Zhu Z. Tumor- and metastasis-promoting roles of miR-488 inhibition via HULC enhancement and EZH2-mediated p53 repression in gastric cancer. Cell Biol Toxicol 2023; 39:1341-1358. [PMID: 36449143 DOI: 10.1007/s10565-022-09760-y] [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: 07/27/2021] [Accepted: 08/26/2022] [Indexed: 12/02/2022]
Abstract
Dysregulation of microRNAs (miRNAs or miRs) is implicated in the development of gastric cancer (GC), which is possibly related to their roles in targeting tumor-suppressive or tumor-promoting genes. Herein, the current study was intended to ascertain the function of miR-488 and its modulatory mechanism in GC. Initially, human GC cells were assayed for their in vitro malignancy after miRNA gain- or loss-of-function and RNA interference or overexpression. Also, tumorigenesis and liver metastasis were evaluated in nude mouse models. Results demonstrated that miR-488 elevation suppressed GC (MKN-45 and OCUM-1) cell proliferation, migration, and invasiveness in vitro and reduced their tumorigenesis and liver metastasis in vivo. The luciferase assay identified that miR-488 bound to HULC and inhibited its expression. Furthermore, HULC could enhance EZH2-H3K27me3 enrichment at the p53 promoter region and epigenetically repress the p53 expression based on the data from RIP- and ChIP-qPCR assay. Additionally, HULC was validated to enhance GC growth and metastasis in vitro and in vivo. Overall, HULC re-expression elicited by miR-488 inhibition can enhance EZH2-H3K27me3 enrichment in the p53 promoter and repress the p53 expression, thus promoting the growth and metastasis of GC.
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Affiliation(s)
- Dejun Yang
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Huangpu District, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Mengyao Shi
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Huangpu District, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Qing You
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Huangpu District, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Yu Zhang
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Huangpu District, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Zunqi Hu
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Huangpu District, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Jiapeng Xu
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Huangpu District, No. 415 Fengyang Road, Shanghai, 200003, China
| | - Qingping Cai
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Huangpu District, No. 415 Fengyang Road, Shanghai, 200003, China.
| | - Zhenxin Zhu
- Department of Gastrointestinal Surgery, Second Affiliated Hospital of Naval Medical University, Huangpu District, No. 415 Fengyang Road, Shanghai, 200003, China.
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22
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Lei Y, Meng Q, Hong F, Zhao M, Gao X. Pan-cancer survey of lncRNA rewiring and functional alternation in tumor-infiltrating T cell by scLNC. Cancer Lett 2023:216319. [PMID: 37468058 DOI: 10.1016/j.canlet.2023.216319] [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: 04/27/2023] [Revised: 06/27/2023] [Accepted: 07/16/2023] [Indexed: 07/21/2023]
Abstract
Long non-coding RNAs (lncRNAs) have been reported to involve in diverse biological processes, including tumor immunity. Since lncRNAs are expressed with high cell-type specificity, investigation of lncRNAs at the single-cell level will unveil the cell-type-specific functions of lncRNAs. However, at the single-cell level, a systematic pan-cancer analysis of lncRNA functions in tumor immune microenvironments (TIMEs) remains lacking. Here, we performed pan-cancer single-cell profiling of lncRNA functions in TIMEs and developed a tool, scLNC, tailored for lncRNA functional characterization at the single-cell level. scLNC enabled the comparison of lncRNA function from the levels of lncRNA-mRNA pairs, lncRNA regulatory unit activity and unit function in a cell-type-specific manner. Applying scLNC, our analysis depicted the cross-tumor and tumor-specific lncRNA regulatory profiles in the T cell subtypes and revealed the new regulatory units that lncRNAs established in tumor-infiltrating T cells, particularly in the tumor-enriched T cells. We further characterized the activity and functional alternations of lncRNAs through their regulatory units. Overall, our findings suggested that lncRNAs played an important role in the regulation of cytokine production, cell activation and migration in tumor-enriched T cells and further in immunotherapy.
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Affiliation(s)
- Yang Lei
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Qianqian Meng
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Fang Hong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Mengyu Zhao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Xin Gao
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
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23
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DeSouza NR, Quaranto D, Carnazza M, Jarboe T, Tiwari RK, Geliebter J. Interactome of Long Non-Coding RNAs: Transcriptomic Expression Patterns and Shaping Cancer Cell Phenotypes. Int J Mol Sci 2023; 24:9914. [PMID: 37373059 PMCID: PMC10298192 DOI: 10.3390/ijms24129914] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023] Open
Abstract
RNA biology has gained extensive recognition in the last two decades due to the identification of novel transcriptomic elements and molecular functions. Cancer arises, in part, due to the accumulation of mutations that greatly contribute to genomic instability. However, the identification of differential gene expression patterns of wild-type loci has exceeded the boundaries of mutational study and has significantly contributed to the identification of molecular mechanisms that drive carcinogenic transformation. Non-coding RNA molecules have provided a novel avenue of exploration, providing additional routes for evaluating genomic and epigenomic regulation. Of particular focus, long non-coding RNA molecule expression has been demonstrated to govern and direct cellular activity, thus evidencing a correlation between aberrant long non-coding RNA expression and the pathological transformation of cells. lncRNA classification, structure, function, and therapeutic utilization have expanded cancer studies and molecular targeting, and understanding the lncRNA interactome aids in defining the unique transcriptomic signatures of cancer cell phenotypes.
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Affiliation(s)
- Nicole R. DeSouza
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA
| | - Danielle Quaranto
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA
| | - Michelle Carnazza
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA
| | - Tara Jarboe
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA
| | - Raj K. Tiwari
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA
- Department of Otolaryngology, New York Medical College, Valhalla, NY 10591, USA
| | - Jan Geliebter
- Department of Pathology, Microbiology and Immunology, New York Medical College, Valhalla, NY 10595, USA
- Department of Otolaryngology, New York Medical College, Valhalla, NY 10591, USA
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24
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Zhang P, Zhang W, Sun W, Li L, Xu J, Wang L, Wong L. A lncRNA-disease association prediction tool development based on bridge heterogeneous information network via graph representation learning for family medicine and primary care. Front Genet 2023; 14:1084482. [PMID: 37274787 PMCID: PMC10234424 DOI: 10.3389/fgene.2023.1084482] [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: 12/12/2022] [Accepted: 05/02/2023] [Indexed: 06/07/2023] Open
Abstract
Identification of long non-coding RNAs (lncRNAs) associated with common diseases is crucial for patient self-diagnosis and monitoring of health conditions using artificial intelligence (AI) technology at home. LncRNAs have gained significant attention due to their crucial roles in the pathogenesis of complex human diseases and identifying their associations with diseases can aid in developing diagnostic biomarkers at the molecular level. Computational methods for predicting lncRNA-disease associations (LDAs) have become necessary due to the time-consuming and labor-intensive nature of wet biological experiments in hospitals, enabling patients to access LDAs through their AI terminal devices at any time. Here, we have developed a predictive tool, LDAGRL, for identifying potential LDAs using a bridge heterogeneous information network (BHnet) constructed via Structural Deep Network Embedding (SDNE). The BHnet consists of three types of molecules as bridge nodes to implicitly link the lncRNA with disease nodes and the SDNE is used to learn high-quality node representations and make LDA predictions in a unified graph space. To assess the feasibility and performance of LDAGRL, extensive experiments, including 5-fold cross-validation, comparison with state-of-the-art methods, comparison on different classifiers and comparison of different node feature combinations, were conducted, and the results showed that LDAGRL achieved satisfactory prediction performance, indicating its potential as an effective LDAs prediction tool for family medicine and primary care.
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Affiliation(s)
- Ping Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Weihan Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Weicheng Sun
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Li Li
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Jinsheng Xu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China
| | - Lei Wang
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, China
| | - Leon Wong
- Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Guangxi Academy of Sciences, Nanning, China
- Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China
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25
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Liu Z, Guo T, Yin Z, Zeng Y, Liu H, Yin H. Functional inference of long non-coding RNAs through exploration of highly conserved regions. Front Genet 2023; 14:1177259. [PMID: 37260771 PMCID: PMC10229068 DOI: 10.3389/fgene.2023.1177259] [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: 03/01/2023] [Accepted: 04/28/2023] [Indexed: 06/02/2023] Open
Abstract
Background: Long non-coding RNAs (lncRNAs), which are generally less functionally characterized or less annotated, evolve more rapidly than mRNAs and substantially possess fewer sequence conservation patterns than protein-coding genes across divergent species. People assume that the functional inference could be conducted on the evolutionarily conserved long non-coding RNAs as they are most likely to be functional. In the past decades, substantial progress has been made in discussions on the evolutionary conservation of non-coding genomic regions from multiple perspectives. However, understanding their conservation and the functions associated with sequence conservation in relation to further corresponding phenotypic variability or disorders still remains incomplete. Results: Accordingly, we determined a highly conserved region (HCR) to verify the sequence conservation among long non-coding RNAs and systematically profiled homologous long non-coding RNA clusters in humans and mice based on the detection of highly conserved regions. Moreover, according to homolog clustering, we explored the potential function inference via highly conserved regions on representative long non-coding RNAs. On lncRNA XACT, we investigated the potential functional competence between XACT and lncRNA XIST by recruiting miRNA-29a, regulating the downstream target genes. In addition, on lncRNA LINC00461, we examined the interaction relationship between LINC00461 and SND1. This interaction or association may be perturbed during the progression of glioma. In addition, we have constructed a website with user-friendly web interfaces for searching, analyzing, and downloading to present the homologous clusters of humans and mice. Conclusion: Collectively, homolog clustering via the highly conserved region definition and detection on long non-coding RNAs, as well as the functional explorations on representative sequences in our research, would provide new evidence for the potential function of long non-coding RNAs. Our results on the remarkable roles of long non-coding RNAs would presumably provide a new theoretical basis and candidate diagnostic indicators for tumors.
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Affiliation(s)
- Zhongpeng Liu
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Tianbin Guo
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Zhuoda Yin
- TJ-YZ School of Network Science, Haikou University of Economics, Haikou, China
| | - Yanluo Zeng
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Haiwen Liu
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
| | - Hongyan Yin
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresources, College of Tropical Crops, Hainan University, Haikou, China
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26
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Li J, Wang Y, Li Z, Lin H, Wu B. LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods. Front Genet 2023; 14:1181592. [PMID: 37229202 PMCID: PMC10203599 DOI: 10.3389/fgene.2023.1181592] [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: 03/07/2023] [Accepted: 04/13/2023] [Indexed: 05/27/2023] Open
Abstract
Introduction: Drug-target interaction (DTI) prediction is a key step in drug function discovery and repositioning. The emergence of large-scale heterogeneous biological networks provides an opportunity to identify drug-related target genes, which led to the development of several computational methods for DTI prediction. Methods: Considering the limitations of conventional computational methods, a novel tool named LM-DTI based on integrated information related to lncRNAs and miRNAs was proposed, which adopted the graph embedding (node2vec) and the network path score methods. First, LM-DTI innovatively constructed a heterogeneous information network containing eight networks composed of four types of nodes (drug, target, lncRNA, and miRNA). Next, the node2vec method was used to obtain feature vectors of drug as well as target nodes, and the path score vector of each drug-target pair was calculated using the DASPfind method. Finally, the feature vectors and path score vectors were merged and input into the XGBoost classifier to predict potential drug-target interactions. Results and Discussion: The 10-fold cross validations evaluate the classification accuracies of the LM-DTI. The prediction performance of LM-DTI in AUPR reached 0.96, which showed a significant improvement compared with those of conventional tools. The validity of LM-DTI has also been verified by manually searching literature and various databases. LM-DTI is scalable and computing efficient; thus representing a powerful drug relocation tool that can be accessed for free at http://www.lirmed.com:5038/lm_dti.
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Affiliation(s)
- Jianwei Li
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
- School of Electronic and Information Engineering, Hebei University of Technology, Tianjin, China
| | - Yinfei Wang
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Zhiguang Li
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Hongxin Lin
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
| | - Baoqin Wu
- School of Artificial Intelligence, Institute of Computational Medicine, Hebei University of Technology, Tianjin, China
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Ranga S, Yadav R, Chhabra R, Chauhan MB, Tanwar M, Yadav C, Kadian L, Ahuja P. Long non-coding RNAs as critical regulators and novel targets in cervical cancer: current status and future perspectives. Apoptosis 2023:10.1007/s10495-023-01840-6. [PMID: 37095313 PMCID: PMC10125867 DOI: 10.1007/s10495-023-01840-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/27/2023] [Indexed: 04/26/2023]
Abstract
Cervical cancer is among the leading causes of cancer-associated mortality in women. In spite of vaccine availability, improved screening procedures, and chemoradiation therapy, cervical cancer remains the most commonly diagnosed cancer in 23 countries and the leading cause of cancer deaths in 36 countries. There is, therefore, a need to come up with novel diagnostic and therapeutic targets. Long non-coding RNAs (lncRNAs) play a remarkable role in genome regulation and contribute significantly to several developmental and disease pathways. The deregulation of lncRNAs is often observed in cancer patients, where they are shown to affect multiple cellular processes, including cell cycle, apoptosis, angiogenesis, and invasion. Many lncRNAs are found to be involved in the pathogenesis as well as progression of cervical cancer and have shown potency to track metastatic events. This review provides an overview of lncRNA mediated regulation of cervical carcinogenesis and highlights their potential as diagnostic and prognostic biomarkers as well as therapeutic targets for cervical cancer. In addition, it also discusses the challenges associated with the clinical implication of lncRNAs in cervical cancer.
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Affiliation(s)
- Shalu Ranga
- Associate Professor, Department of Genetics, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Ritu Yadav
- Associate Professor, Department of Genetics, Maharshi Dayanand University, Rohtak, Haryana, 124001, India.
| | - Ravindresh Chhabra
- Assistant Professor, Department of Biochemistry, Central University of Punjab, Bathinda, Punjab, 151401, India.
| | - Meenakshi B Chauhan
- Department of Obstetrics and Gynaecology, Pandit Bhagwat Dayal Sharma University of Health Sciences, Rohtak, Haryana, 124001, India
| | - Mukesh Tanwar
- Associate Professor, Department of Genetics, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Chetna Yadav
- Associate Professor, Department of Genetics, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Lokesh Kadian
- School of Medicine, Indiana University, Indianapolis, IN, 46202, USA
| | - Parul Ahuja
- Associate Professor, Department of Genetics, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
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Zhang L, Lu D, Bi X, Zhao K, Yu G, Quan N. Predicting disease genes based on multi-head attention fusion. BMC Bioinformatics 2023; 24:162. [PMID: 37085750 PMCID: PMC10122338 DOI: 10.1186/s12859-023-05285-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 04/12/2023] [Indexed: 04/23/2023] Open
Abstract
BACKGROUND The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes. RESULTS This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association. CONCLUSIONS The MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction.
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Affiliation(s)
- Linlin Zhang
- College of Software Engineering, Xinjiang University, Urumqi, China.
| | - Dianrong Lu
- College of information Science and Engineering, Xinjiang University, Urumqi, China
| | - Xuehua Bi
- Medical Engineering and Technology College, Xinjiang Medical University, Urumqi, China
| | - Kai Zhao
- College of information Science and Engineering, Xinjiang University, Urumqi, China
| | - Guanglei Yu
- Medical Engineering and Technology College, Xinjiang Medical University, Urumqi, China
| | - Na Quan
- College of information Science and Engineering, Xinjiang University, Urumqi, China
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29
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Elzallat M, Hassan M, Elkramani N, Aboushousha T, AbdelLatif A, Helal N, Abu-Taleb H, El-Ahwany E. Nanoconjugated long non-coding RNA MEG3 as a new therapeutic approach for Hepatocellular carcinoma. Heliyon 2023; 9:e15288. [PMID: 37101621 PMCID: PMC10123146 DOI: 10.1016/j.heliyon.2023.e15288] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 03/28/2023] [Accepted: 03/31/2023] [Indexed: 04/09/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is an aggressive human cancer with a poor prognosis. Long non-coding RNAs (lncRNA) have multiple functions: epigenomic regulation, gene transcription, protein-coding gene translation, and genome defense. The involvement of lncRNAs in therapy offers a vast step in cancer treatment. Objective In the current study, a novel therapeutic regimen using polymer nanoparticle-mediated delivery of lncRNA was designed to control the progression of hepatocarcinogenesis. Methods One hundred mice were divided into 5 groups. The first group served as a normal-control group and was injected with saline, whereas the pathological-control group (the second group) was injected with N-Nitrosodiethylamine (DEN) weekly for 16 weeks. Group 3, Group 4, and Group 5 were injected intrahepatically with polymer nanoparticles (NPs) alone, lncRNA MEG3 alone, and conjugated NPs, respectively, once/week for four weeks starting on the 12th week after DEN injection. After 16 weeks, animals were euthanized, and liver specimens and blood samples were collected for pathological, molecular, and biochemical assessment. Results Compared to the pathological-control group, nanoconjugates lncRNA MEG3 demonstrated a significant improvement in histopathology and tumour-associated biomarkers. Furthermore, the expression of the SENP1 and PCNA was downregulated. Conclusion MEG3 conjugated nanoparticles can be considered a novel therapeutic regimen for HCC.
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Zhou S, Huang Y, Xing J, Zhou X, Chen S, Chen J, Wang L, Jiang W. ncFO: A Comprehensive Resource of Curated and Predicted ncRNAs Associated with Ferroptosis. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:278-282. [PMID: 36162726 PMCID: PMC10626053 DOI: 10.1016/j.gpb.2022.09.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 08/25/2022] [Accepted: 09/19/2022] [Indexed: 06/16/2023]
Abstract
Ferroptosis is a form of regulated cell death driven by the accumulation of lipid hydroperoxides. Regulation of ferroptosis might be beneficial to cancer treatment. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play critical roles in regulating ferroptosis. Herein, we developed ncFO, the ncRNA-ferroptosis association database, to document the manually curated and predicted ncRNAs that are associated with ferroptosis. Collectively, ncFO contains 90 experimentally verified entries, including 46 microRNAs (miRNAs), 21 long non-coding RNAs (lncRNAs), and 17 circular RNAs (circRNAs). In addition, ncFO also incorporates two online prediction tools based on the regulation and co-expression of ncRNA and ferroptosis genes. Using default parameters, we obtained 3260 predicted entries, including 598 miRNAs and 178 lncRNAs, by regulation, as well as 2,592,661 predicted entries, including 967 miRNAs and 9632 lncRNAs, by ncRNA-ferroptosis gene co-expression in more than 8000 samples across 20 cancer types. The detailed information of each entry includes ncRNA name, disease, species, tissue, target, regulation, publication time, and PubMed identifier. ncFO also provides survival analysis and differential expression analysis for ncRNAs. In summary, ncFO offers a user-friendly platform to search and predict ferroptosis-associated ncRNAs, which might facilitate research on ferroptosis and discover potential targets for cancer treatment. ncFO can be accessed at http://www.jianglab.cn/ncFO/.
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Affiliation(s)
- Shunheng Zhou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Yu'e Huang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Jiani Xing
- Department of Pathophysiology, School of Medicine, Southeast University, Nanjing 210009, China
| | - Xu Zhou
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Sina Chen
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Jiahao Chen
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
| | - Lihong Wang
- Department of Pathophysiology, School of Medicine, Southeast University, Nanjing 210009, China.
| | - Wei Jiang
- Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
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Aydin B, Beklen H, Arga KY, Bayrakli F, Turanli B. Epigenomic and transcriptomic landscaping unraveled candidate repositioned therapeutics for non-functioning pituitary neuroendocrine tumors. J Endocrinol Invest 2023; 46:727-747. [PMID: 36306107 DOI: 10.1007/s40618-022-01923-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 09/12/2022] [Indexed: 10/31/2022]
Abstract
PURPOSE Non-functioning pituitary neuroendocrine tumors are challengingly diagnosed tumors in the clinic. Transsphenoidal surgery remains the first-line treatment. Despite the development of state-of-the-art techniques, no drug therapy is currently approved for the treatment. There are also no randomized controlled trials comparing therapeutic strategies or drug therapy for the management after surgery. Therefore, novel therapeutic interventions for the therapeutically challenging NF-PitNETs are urgently needed. METHODS We integrated epigenome and transcriptome data (both coding and non-coding) that elucidate disease-specific signatures, in addition to biological and pharmacological data, to utilize rational pathway and drug prioritization in NF-PitNETs. We constructed an epigenome- and transcriptome-based PPI network and proposed hub genes. The signature-based drug repositioning based on the integration of multi-omics data was performed. RESULTS The construction of a disease-specific network based on three different biological levels revealed DCC, DLG5, ETS2, FOXO1, HBP1, HMGA2, PCGF3, PSME4, RBPMS, RREB1, SMAD1, SOCS1, SOX2, YAP1, ZFHX3 as hub proteins. Signature-based drug repositioning using hub proteins yielded repositioned drug candidates that were confirmed in silico via molecular docking. As a result of molecular docking simulations, palbociclib, linifanib, trametinib, eplerenone, niguldipine, and zuclopenthixol showed higher binding affinities with hub genes compared to their inhibitors and were proposed as potential repositioned therapeutics for the management of NF-PitNETs. CONCLUSION The proposed systems' biomedicine-oriented multi-omics data integration for drug repurposing to provide promising results for the construction of effective clinical therapeutics. To the best of our knowledge, this is the first study reporting epigenome- and transcriptome-based drug repositioning for NF-PitNETs using in silico confirmations.
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Affiliation(s)
- B Aydin
- Department of Bioengineering, Faculty of Engineering and Architecture, Konya Food and Agriculture University, Konya, Turkey
| | - H Beklen
- Department of Bioengineering, Faculty of Engineering, Marmara University, RTE Basibuyuk Campus, 34720, Istanbul, Turkey
| | - K Y Arga
- Department of Bioengineering, Faculty of Engineering, Marmara University, RTE Basibuyuk Campus, 34720, Istanbul, Turkey
- Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM), Marmara University, Istanbul, Turkey
| | - F Bayrakli
- Department of Neurosurgery, Faculty of Medicine, Marmara University, Istanbul, Turkey
- Institute of Neurological Sciences, Marmara University, Istanbul, Turkey
| | - B Turanli
- Department of Bioengineering, Faculty of Engineering, Marmara University, RTE Basibuyuk Campus, 34720, Istanbul, Turkey.
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An L, Xia H, Zheng W, Hua L. Comparison of cell subsets in chronic obstructive pulmonary disease and controls based on single-cell transcriptome sequencing. Technol Health Care 2023; 31:9-24. [PMID: 37038777 DOI: 10.3233/thc-236002] [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: 04/12/2023]
Abstract
BACKGROUND Currently, chronic obstructive pulmonary disease (COPD) significantly impacts patients' quality of life and survival as it has a high morbidity and mortality rate. COPD progression is associated with infiltration of adaptive inflammatory immune cells that form lymphatic follicles into the lung. OBJECTIVE The rapid development of single-cell RNA sequencing technology (scRNA-seq) provided us with powerful tools for studying the classification of cell subtypes. Additionally, it is known that COPD is closely related to the abnormal function of long-chain non-coding RNAs (lncRNAs), and scRNA-seq can help to study the expression of lncRNA from a single cell level. METHODS We reanalyzed the scRNA-seq data of peripheral blood mononuclear cells of COPD patients downloaded from Gene Expression Omnibus (GEO) database, and performed the mRNA-based and lncRNA-based single cell clustering to compare the cell subsets in COPD and controls without COPD. Furthermore, we performed Gene Ontology (GO) enrichment analysis for the top ranked differentially expressed genes and target genes of differentially expressed lncRNAs in different cell subtypes for COPD and controls respectively. RESULTS Differences in cell subtypes were found between COPD and controls. CONCLUSION This study may help us to further understand the mechanism of the human adaptive immune cell response of COPD.
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Affiliation(s)
- Li An
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Hong Xia
- School of Biomedical Engineering, Capital Medical University, Beijing, China
- Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Weiying Zheng
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Lin Hua
- School of Biomedical Engineering, Capital Medical University, Beijing, China
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Constructing discriminative feature space for LncRNA-protein interaction based on deep autoencoder and marginal fisher analysis. Comput Biol Med 2023; 157:106711. [PMID: 36924738 DOI: 10.1016/j.compbiomed.2023.106711] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/26/2023] [Accepted: 02/26/2023] [Indexed: 03/04/2023]
Abstract
Long non-coding RNAs (lncRNAs) play important roles by regulating proteins in many biological processes and life activities. To uncover molecular mechanisms of lncRNA, it is very necessary to identify interactions of lncRNA with proteins. Recently, some machine learning methods were proposed to detect lncRNA-protein interactions according to the distribution of known interactions. The performances of these methods were largely dependent upon: (1) how exactly the distribution of known interactions was characterized by feature space; (2) how discriminative the feature space was for distinguishing lncRNA-protein interactions. Because the known interactions may be multiple and complex model, it remains a challenge to construct discriminative feature space for lncRNA-protein interactions. To resolve this problem, a novel method named DFRPI was developed based on deep autoencoder and marginal fisher analysis in this paper. Firstly, some initial features of lncRNA-protein interactions were extracted from the primary sequences and secondary structures of lncRNA and protein. Secondly, a deep autoencoder was exploited to learn encode parameters of the initial features to describe the known interactions precisely. Next, the marginal fisher analysis was employed to optimize the encode parameters of features to characterize a discriminative feature space of the lncRNA-protein interactions. Finally, a random forest-based predictor was trained on the discriminative feature space to detect lncRNA-protein interactions. Verified by a series of experiments, the results showed that our predictor achieved the precision of 0.920, recall of 0.916, accuracy of 0.918, MCC of 0.836, specificity of 0.920, sensitivity of 0.916 and AUC of 0.906 respectively, which outperforms the concerned methods for predicting lncRNA-protein interaction. It may be suggested that the proposed method can generate a reasonable and effective feature space for distinguishing lncRNA-protein interactions accurately. The code and data are available on https://github.com/D0ub1e-D/DFRPI.
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Jagodnik KM, Shvili Y, Bartal A. HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression. PLoS One 2023; 18:e0280839. [PMID: 36791052 PMCID: PMC9931161 DOI: 10.1371/journal.pone.0280839] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 01/10/2023] [Indexed: 02/16/2023] Open
Abstract
Graph analytical approaches permit identifying novel genes involved in complex diseases, but are limited by (i) inferring structural network similarity of connected gene nodes, ignoring potentially relevant unconnected nodes; (ii) using homogeneous graphs, missing gene-disease associations' complexity; (iii) relying on disease/gene-phenotype associations' similarities, involving highly incomplete data; (iv) using binary classification, with gene-disease edges as positive training samples, and non-associated gene and disease nodes as negative samples that may include currently unknown disease genes; or (v) reporting predicted novel associations without systematically evaluating their accuracy. Addressing these limitations, we develop the Heterogeneous Integrated Graph for Predicting Disease Genes (HetIG-PreDiG) model that includes gene-gene, gene-disease, and gene-tissue associations. We predict novel disease genes using low-dimensional representation of nodes accounting for network structure, and extending beyond network structure using the developed Gene-Disease Prioritization Score (GDPS) reflecting the degree of gene-disease association via gene co-expression data. For negative training samples, we select non-associated gene and disease nodes with lower GDPS that are less likely to be affiliated. We evaluate the developed model's success in predicting novel disease genes by analyzing the prediction probabilities of gene-disease associations. HetIG-PreDiG successfully predicts (Micro-F1 = 0.95) gene-disease associations, outperforming baseline models, and is validated using published literature, thus advancing our understanding of complex genetic diseases.
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Affiliation(s)
- Kathleen M. Jagodnik
- The School of Business Administration, Bar-Ilan University, Ramat Gan, Israel
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States of America
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, United States of America
| | - Yael Shvili
- Department of Surgery A, Meir Medical Center, Kfar Sava, Israel
| | - Alon Bartal
- The School of Business Administration, Bar-Ilan University, Ramat Gan, Israel
- * E-mail:
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Recent advances in predicting lncRNA-disease associations based on computational methods. Drug Discov Today 2023; 28:103432. [PMID: 36370992 DOI: 10.1016/j.drudis.2022.103432] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/19/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022]
Abstract
Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA-disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effectively narrow down the screening scope of biological experiments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.
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Sheng N, Huang L, Lu Y, Wang H, Yang L, Gao L, Xie X, Fu Y, Wang Y. Data resources and computational methods for lncRNA-disease association prediction. Comput Biol Med 2023; 153:106527. [PMID: 36610216 DOI: 10.1016/j.compbiomed.2022.106527] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 12/08/2022] [Accepted: 12/31/2022] [Indexed: 01/03/2023]
Abstract
Increasing interest has been attracted in deciphering the potential disease pathogenesis through lncRNA-disease association (LDA) prediction, regarding to the diverse functional roles of lncRNAs in genome regulation. Whilst, computational models and algorithms benefit systematic biology research, even facilitate the classical biological experimental procedures. In this review, we introduce representative diseases associated with lncRNAs, such as cancers, cardiovascular diseases, and neurological diseases. Current publicly available resources related to lncRNAs and diseases have also been included. Furthermore, all of the 64 computational methods for LDA prediction have been divided into 5 groups, including machine learning-based methods, network propagation-based methods, matrix factorization- and completion-based methods, deep learning-based methods, and graph neural network-based methods. The common evaluation methods and metrics in LDA prediction have also been discussed. Finally, the challenges and future trends in LDA prediction have been discussed. Recent advances in LDA prediction approaches have been summarized in the GitHub repository at https://github.com/sheng-n/lncRNA-disease-methods.
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Affiliation(s)
- Nan Sheng
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Lan Huang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China.
| | - Yuting Lu
- School of Artificial Intelligence, Jilin University, Changchun, China
| | - Hao Wang
- Department of Hepatopancreatobiliary Surgery, Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lili Yang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China; Department of Obstetrics, The First Hospital of Jilin University, Changchun, China
| | - Ling Gao
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Xuping Xie
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China
| | - Yuan Fu
- Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, Ceredigion, United Kingdom
| | - Yan Wang
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, China; School of Artificial Intelligence, Jilin University, Changchun, China.
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Li G, Zhang P, Sun W, Xu J, Hu L, Zhang W. GA-ENs: A novel drug-target interactions prediction method by incorporating prior Knowledge Graph into dual Wasserstein Generative Adversarial Network with gradient penalty. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
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38
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Huang M, Ma J, Zhang J. Inferring cell developmental stage-specific lncRNA regulation in the developing human neocortex with CDSlncR. Front Mol Neurosci 2023; 15:1037565. [PMID: 36710930 PMCID: PMC9880432 DOI: 10.3389/fnmol.2022.1037565] [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: 09/06/2022] [Accepted: 12/26/2022] [Indexed: 01/15/2023] Open
Abstract
Noncoding RNAs (ncRNAs) occupy ~98% of the transcriptome in human, and are usually not translated into proteins. Among ncRNAs, long non-coding RNAs (lncRNAs, >200 nucleotides) are important regulators to modulate gene expression, and are involved in many biological processes (e.g., cell development). To study lncRNA regulation, many computational approaches or tools have been proposed by using bulk transcriptomics data. Nevertheless, previous bulk data-driven methods are mostly limited to explore the lncRNA regulation regarding all of cells, instead of the lncRNA regulation specific to cell developmental stages. Fortunately, recent advance in single-cell sequencing data has provided a way to investigate cell developmental stage-specific lncRNA regulation. In this work, we present a novel computational method, CDSlncR (Cell Developmental Stage-specific lncRNA regulation), which combines putative lncRNA-target binding information with single-cell transcriptomics data to infer cell developmental stage-specific lncRNA regulation. For each cell developmental stage, CDSlncR constructs a cell developmental stage-specific lncRNA regulatory network in the cell developmental stage. To illustrate the effectiveness of CDSlncR, we apply CDSlncR into single-cell transcriptomics data of the developing human neocortex for exploring lncRNA regulation across different human neocortex developmental stages. Network analysis shows that the lncRNA regulation is unique in each developmental stage of human neocortex. As a case study, we also perform particular analysis on the cell developmental stage-specific lncRNA regulation related to 18 known lncRNA biomarkers in autism spectrum disorder. Finally, the comparison result indicates that CDSlncR is an effective method for predicting cell developmental stage-specific lncRNA targets. CDSlncR is available at https://github.com/linxi159/CDSlncR.
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Affiliation(s)
- Meng Huang
- Department of Automation, Xiamen University, Xiamen, China,Department of Computer Science, University of Tsukuba, Tsukuba, Japan
| | - Jiangtao Ma
- Department of Automation, Xiamen University, Xiamen, China,School of Engineering, Dali University, Dali, China
| | - Junpeng Zhang
- School of Engineering, Dali University, Dali, China,*Correspondence: Junpeng Zhang, ✉
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Ghafouri-Fard S, Safarzadeh A, Hussen BM, Taheri M, Ayatollahi SA. A review on the role of LINC00511 in cancer. Front Genet 2023; 14:1116445. [PMID: 37124625 PMCID: PMC10140539 DOI: 10.3389/fgene.2023.1116445] [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: 12/05/2022] [Accepted: 04/05/2023] [Indexed: 05/02/2023] Open
Abstract
Long Intergenic Non-Protein Coding RNA 511 (LINC00511) is an RNA gene being mostly associated with lung cancer. Further assessments have shown dysregulation of this lncRNA in a variety of cancers. LINC00511 has interactions with hsa-miR-29b-3p, hsa-miR-765, hsa-mir-150, miR-1231, TFAP2A-AS2, hsa-miR-185-3p, hsa-miR-29b-1-5p, hsa-miR-29c-3p, RAD51-AS1 and EZH2. A number of transcription factors have been identified that regulate expression of LINC00511. The current narrative review summarizes the role of LINC00511 in different cancers with an especial focus on its prognostic impact in human cancers.
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Affiliation(s)
- Soudeh Ghafouri-Fard
- Department of Medical Genetics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Arash Safarzadeh
- Men’s Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Bashdar Mahmud Hussen
- Department of Clinical Analysis, College of Pharmacy, Hawler Medical University, Erbil, Iraq
| | - Mohammad Taheri
- Institute of Human Genetics, Jena University Hospital, Jena, Germany
- Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- *Correspondence: Mohammad Taheri, ; Seyed Abdulmajid Ayatollahi,
| | - Seyed Abdulmajid Ayatollahi
- Phytochemistry Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- *Correspondence: Mohammad Taheri, ; Seyed Abdulmajid Ayatollahi,
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Li Z, Zhang Y, Fang J, Xu Z, Zhang H, Mao M, Chen Y, Zhang L, Pian C. NcPath: a novel platform for visualization and enrichment analysis of human non-coding RNA and KEGG signaling pathways. Bioinformatics 2022; 39:6917072. [PMID: 36525367 PMCID: PMC9825761 DOI: 10.1093/bioinformatics/btac812] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 11/10/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
SUMMARY Non-coding RNAs play important roles in transcriptional processes and participate in the regulation of various biological functions, in particular miRNAs and lncRNAs. Despite their importance for several biological functions, the existing signaling pathway databases do not include information on miRNA and lncRNA. Here, we redesigned a novel pathway database named NcPath by integrating and visualizing a total of 178 308 human experimentally validated miRNA-target interactions (MTIs), 32 282 experimentally verified lncRNA-target interactions (LTIs) and 4837 experimentally validated human ceRNA networks across 222 KEGG pathways (including 27 sub-categories). To expand the application potential of the redesigned NcPath database, we identified 556 798 reliable lncRNA-protein-coding genes (PCG) interaction pairs by integrating co-expression relations, ceRNA relations, co-TF-binding interactions, co-histone-modification interactions, cis-regulation relations and lncPro Tool predictions between lncRNAs and PCG. In addition, to determine the pathways in which miRNA/lncRNA targets are involved, we performed a KEGG enrichment analysis using a hypergeometric test. The NcPath database also provides information on MTIs/LTIs/ceRNA networks, PubMed IDs, gene annotations and the experimental verification method used. In summary, the NcPath database will serve as an important and continually updated platform that provides annotation and visualization of the pathways on which non-coding RNAs (miRNA and lncRNA) are involved, and provide support to multimodal non-coding RNAs enrichment analysis. The NcPath database is freely accessible at http://ncpath.pianlab.cn/. AVAILABILITY AND IMPLEMENTATION NcPath database is freely available at http://ncpath.pianlab.cn/. The code and manual to use NcPath can be found at https://github.com/Marscolono/NcPath/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Zutan Li
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Yuan Zhang
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Jingya Fang
- College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Zhihui Xu
- The State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Diagnostics Co., Ltd., Nanjing 210023, China
| | - Hao Zhang
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | - Minfang Mao
- College of Science, Nanjing Agricultural University, Nanjing 210095, China
| | | | | | - Cong Pian
- To whom correspondence should be addressed. or or
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Mechanism of selective induction of apoptosis of HCT116 tumor cells in circulating blood by riboflavin photochemistry. JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY B: BIOLOGY 2022; 237:112588. [DOI: 10.1016/j.jphotobiol.2022.112588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 10/14/2022] [Accepted: 10/18/2022] [Indexed: 11/28/2022]
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Tan J, Li X, Zhang L, Du Z. Recent advances in machine learning methods for predicting LncRNA and disease associations. Front Cell Infect Microbiol 2022; 12:1071972. [PMID: 36530425 PMCID: PMC9748103 DOI: 10.3389/fcimb.2022.1071972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 11/11/2022] [Indexed: 12/03/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) are involved in almost the entire cell life cycle through different mechanisms and play an important role in many key biological processes. Mutations and dysregulation of lncRNAs have been implicated in many complex human diseases. Therefore, identifying the relationship between lncRNAs and diseases not only contributes to biologists' understanding of disease mechanisms, but also provides new ideas and solutions for disease diagnosis, treatment, prognosis and prevention. Since the existing experimental methods for predicting lncRNA-disease associations (LDAs) are expensive and time consuming, machine learning methods for predicting lncRNA-disease associations have become increasingly popular among researchers. In this review, we summarize some of the human diseases studied by LDAs prediction models, association and similarity features of LDAs prediction, performance evaluation methods of models and some advanced machine learning prediction models of LDAs. Finally, we discuss the potential limitations of machine learning-based methods for LDAs prediction and provide some ideas for designing new prediction models.
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Meta-Analysis of RNA-Seq Datasets Identifies Novel Players in Glioblastoma. Cancers (Basel) 2022; 14:cancers14235788. [PMID: 36497269 PMCID: PMC9737249 DOI: 10.3390/cancers14235788] [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: 10/12/2022] [Revised: 11/18/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022] Open
Abstract
Glioblastoma is a devastating grade IV glioma with poor prognosis. Identification of predictive molecular biomarkers of disease progression would substantially contribute to better disease management. In the current study, we performed a meta-analysis of different RNA-seq datasets to identify differentially expressed protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs). This meta-analysis aimed to improve power and reproducibility of the individual studies while identifying overlapping disease-relevant pathways. We supplemented the meta-analysis with small RNA-seq on glioblastoma tissue samples to provide an overall transcriptomic view of glioblastoma. Co-expression correlation of filtered differentially expressed PCGs and lncRNAs identified a functionally relevant sub-cluster containing DANCR and SNHG6, with two novel lncRNAs and two novel PCGs. Small RNA-seq of glioblastoma tissues identified five differentially expressed microRNAs of which three interacted with the functionally relevant sub-cluster. Pathway analysis of this sub-cluster identified several glioblastoma-linked pathways, which were also previously associated with the novel cell death pathway, ferroptosis. In conclusion, the current meta-analysis strengthens evidence of an overarching involvement of ferroptosis in glioblastoma pathogenesis and also suggests some candidates for further analyses.
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Zhang H, Wang Y, Pan Z, Sun X, Mou M, Zhang B, Li Z, Li H, Zhu F. ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA. Brief Bioinform 2022; 23:6747810. [PMID: 36198065 DOI: 10.1093/bib/bbac411] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/04/2022] [Accepted: 08/23/2022] [Indexed: 12/14/2022] Open
Abstract
In recent years, many studies have illustrated the significant role that non-coding RNA (ncRNA) plays in biological activities, in which lncRNA, miRNA and especially their interactions have been proved to affect many biological processes. Some in silico methods have been proposed and applied to identify novel lncRNA-miRNA interactions (LMIs), but there are still imperfections in their RNA representation and information extraction approaches, which imply there is still room for further improving their performances. Meanwhile, only a few of them are accessible at present, which limits their practical applications. The construction of a new tool for LMI prediction is thus imperative for the better understanding of their relevant biological mechanisms. This study proposed a novel method, ncRNAInter, for LMI prediction. A comprehensive strategy for RNA representation and an optimized deep learning algorithm of graph neural network were utilized in this study. ncRNAInter was robust and showed better performance of 26.7% higher Matthews correlation coefficient than existing reputable methods for human LMI prediction. In addition, ncRNAInter proved its universal applicability in dealing with LMIs from various species and successfully identified novel LMIs associated with various diseases, which further verified its effectiveness and usability. All source code and datasets are freely available at https://github.com/idrblab/ncRNAInter.
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Affiliation(s)
- Hanyu Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Bing Zhang
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhaorong Li
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Honglin Li
- School of Computer Science and Technology, East China Normal University, Shanghai 200062, China.,Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology, Shanghai 200237, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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45
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Gao M, Liu S, Qi Y, Guo X, Shang X. GAE-LGA: integration of multi-omics data with graph autoencoders to identify lncRNA-PCG associations. Brief Bioinform 2022; 23:6775590. [PMID: 36305456 DOI: 10.1093/bib/bbac452] [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: 07/14/2022] [Revised: 09/20/2022] [Accepted: 09/22/2022] [Indexed: 12/14/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) can disrupt the biological functions of protein-coding genes (PCGs) to cause cancer. However, the relationship between lncRNAs and PCGs remains unclear and difficult to predict. Machine learning has achieved a satisfactory performance in association prediction, but to our knowledge, it is currently less used in lncRNA-PCG association prediction. Therefore, we introduce GAE-LGA, a powerful deep learning model with graph autoencoders as components, to recognize potential lncRNA-PCG associations. GAE-LGA jointly explored lncRNA-PCG learning and cross-omics correlation learning for effective lncRNA-PCG association identification. The functional similarity and multi-omics similarity of lncRNAs and PCGs were accumulated and encoded by graph autoencoders to extract feature representations of lncRNAs and PCGs, which were subsequently used for decoding to obtain candidate lncRNA-PCG pairs. Comprehensive evaluation demonstrated that GAE-LGA can successfully capture lncRNA-PCG associations with strong robustness and outperformed other machine learning-based identification methods. Furthermore, multi-omics features were shown to improve the performance of lncRNA-PCG association identification. In conclusion, GAE-LGA can act as an efficient application for lncRNA-PCG association prediction with the following advantages: It fuses multi-omics information into the similarity network, making the feature representation more accurate; it can predict lncRNA-PCG associations for new lncRNAs and identify potential lncRNA-PCG associations with high accuracy.
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Affiliation(s)
- Meihong Gao
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shuhui Liu
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yang Qi
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Xinpeng Guo
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Xuequn Shang
- School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China
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Zhao H, Yin X, Xu H, Liu K, Liu W, Wang L, Zhang C, Bo L, Lan X, Lin S, Feng K, Ning S, Zhang Y, Wang L. LncTarD 2.0: an updated comprehensive database for experimentally-supported functional lncRNA-target regulations in human diseases. Nucleic Acids Res 2022; 51:D199-D207. [PMID: 36321659 PMCID: PMC9825480 DOI: 10.1093/nar/gkac984] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 10/05/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022] Open
Abstract
An updated LncTarD 2.0 database provides a comprehensive resource on key lncRNA-target regulations, their influenced functions and lncRNA-mediated regulatory mechanisms in human diseases. LncTarD 2.0 is freely available at (http://bio-bigdata.hrbmu.edu.cn/LncTarD or https://lnctard.bio-database.com/). LncTarD 2.0 was updated with several new features, including (i) an increased number of disease-associated lncRNA entries, where the current release provides 8360 key lncRNA-target regulations, with 419 disease subtypes and 1355 lncRNAs; (ii) predicted 3312 out of 8360 lncRNA-target regulations as potential diagnostic or therapeutic biomarkers in circulating tumor cells (CTCs); (iii) addition of 536 new, experimentally supported lncRNA-target regulations that modulate properties of cancer stem cells; (iv) addition of an experimentally supported clinical application section of 2894 lncRNA-target regulations for potential clinical application. Importantly, LncTarD 2.0 provides RNA-seq/microarray and single-cell web tools for customizable analysis and visualization of lncRNA-target regulations in diseases. RNA-seq/microarray web tool was used to mining lncRNA-target regulations in both disease tissue samples and CTCs blood samples. The single-cell web tools provide single-cell lncRNA-target annotation from the perspectives of pan-cancer analysis and cancer-specific analysis at the single-cell level. LncTarD 2.0 will be a useful resource and mining tool for the investigation of the functions and mechanisms of lncRNA deregulation in human disease.
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Affiliation(s)
| | | | | | | | - Wangyang Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lixia Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Caiyu Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Lin Bo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Xicheng Lan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shihua Lin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Ke Feng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shangwei Ning
- Correspondence may also be addressed to Shangwei Ning. Tel: +86 451 86615922;
| | - Yunpeng Zhang
- Correspondence may also be addressed to Yunpeng Zhang. Tel: +86 451 86615922;
| | - Li Wang
- To whom correspondence should be addressed. Tel: +86 451 86615922;
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Hardin LT, Xiao N. miRNAs: The Key Regulator of COVID-19 Disease. Int J Cell Biol 2022; 2022:1645366. [PMID: 36345541 PMCID: PMC9637033 DOI: 10.1155/2022/1645366] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 09/30/2022] [Indexed: 01/12/2024] Open
Abstract
As many parts of the world continue to fight the innumerable waves of COVID-19 infection, SARS-CoV-2 continues to sculpt its antigenic determinants to enhance its virulence and evolvability. Several vaccines were developed and used around the world, and oral antiviral medications are being developed against SARS-CoV-2. However, studies showed that the virus is mutating in line with the antibody's neutralization escape; thus, new therapeutic alternatives are solicited. We hereby review the key role that miRNAs can play as epigenetic mediators of the cross-talk between SARS-CoV-2 and the host cells. The limitations resulting from the "virus intelligence" to escape and antagonize the host miRNAs as well as the possible mechanisms that could be used in the viral evasion strategies are discussed. Lastly, we suggest new therapeutic approaches based on viral miRNAs.
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Affiliation(s)
- Leyla Tahrani Hardin
- Department of Biomedical Sciences at the Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, 94103 CA, USA
| | - Nan Xiao
- Department of Biomedical Sciences at the Arthur A. Dugoni School of Dentistry, University of the Pacific, San Francisco, 94103 CA, USA
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Pandey A, Ajgaonkar S, Jadhav N, Saha P, Gurav P, Panda S, Mehta D, Nair S. Current Insights into miRNA and lncRNA Dysregulation in Diabetes: Signal Transduction, Clinical Trials and Biomarker Discovery. Pharmaceuticals (Basel) 2022; 15:1269. [PMID: 36297381 PMCID: PMC9610703 DOI: 10.3390/ph15101269] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 09/27/2022] [Accepted: 10/09/2022] [Indexed: 01/24/2023] Open
Abstract
Diabetes is one of the most frequently occurring metabolic disorders, affecting almost one tenth of the global population. Despite advances in antihyperglycemic therapeutics, the management of diabetes is limited due to its complexity and associated comorbidities, including diabetic neuropathy, diabetic nephropathy and diabetic retinopathy. Noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), are involved in the regulation of gene expression as well as various disease pathways in humans. Several ncRNAs are dysregulated in diabetes and are responsible for modulating the expression of various genes that contribute to the 'symptom complex' in diabetes. We review various miRNAs and lncRNAs implicated in diabetes and delineate ncRNA biological networks as well as key ncRNA targets in diabetes. Further, we discuss the spatial regulation of ncRNAs and their role(s) as prognostic markers in diabetes. We also shed light on the molecular mechanisms of signal transduction with diabetes-associated ncRNAs and ncRNA-mediated epigenetic events. Lastly, we summarize clinical trials on diabetes-associated ncRNAs and discuss the functional relevance of the dysregulated ncRNA interactome in diabetes. This knowledge will facilitate the identification of putative biomarkers for the therapeutic management of diabetes and its comorbidities. Taken together, the elucidation of the architecture of signature ncRNA regulatory networks in diabetes may enable the identification of novel biomarkers in the discovery pipeline for diabetes, which may lead to better management of this metabolic disorder.
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Affiliation(s)
| | | | | | - Praful Saha
- Viridis Biopharma Pvt. Ltd., Mumbai 400 022, India
| | - Pranay Gurav
- Viridis Biopharma Pvt. Ltd., Mumbai 400 022, India
| | | | - Dilip Mehta
- Synergia Life Sciences Pvt. Ltd., Mumbai 400 022, India
| | - Sujit Nair
- Viridis Biopharma Pvt. Ltd., Mumbai 400 022, India
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Manukonda R, Yenuganti VR, Nagar N, Dholaniya PS, Malpotra S, Attem J, Reddy MM, Jakati S, Mishra DK, Reddanna P, Poluri KM, Vemuganti GK, Kaliki S. Comprehensive Analysis of Serum Small Extracellular Vesicles-Derived Coding and Non-Coding RNAs from Retinoblastoma Patients for Identifying Regulatory Interactions. Cancers (Basel) 2022; 14:cancers14174179. [PMID: 36077715 PMCID: PMC9454787 DOI: 10.3390/cancers14174179] [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: 07/06/2022] [Revised: 08/18/2022] [Accepted: 08/24/2022] [Indexed: 11/24/2022] Open
Abstract
The present study employed nanoparticle tracking analysis, transmission electron microscopy, immunoblotting, RNA sequencing, and quantitative real-time PCR validation to characterize serum-derived small extracellular vesicles (sEVs) from RB patients and age-matched controls. Bioinformatics methods were used to analyze functions, and regulatory interactions between coding and non-coding (nc) sEVs RNAs. The results revealed that the isolated sEVs are round-shaped with a size < 150 nm, 5.3 × 1011 ± 8.1 particles/mL, and zeta potential of 11.1 to −15.8 mV, and expressed exosome markers CD9, CD81, and TSG101. A total of 6514 differentially expressed (DE) mRNAs, 123 DE miRNAs, and 3634 DE lncRNAs were detected. Both miRNA-mRNA and lncRNA-miRNA-mRNA network analysis revealed that the cell cycle-specific genes including CDKNI1A, CCND1, c-MYC, and HIF1A are regulated by hub ncRNAs MALAT1, AFAP1-AS1, miR145, 101, and 16-5p. Protein-protein interaction network analysis showed that eye-related DE mRNAs are involved in rod cell differentiation, cone cell development, and retinol metabolism. In conclusion, our study provides a comprehensive overview of the RB sEV RNAs and regulatory interactions between them.
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Affiliation(s)
- Radhika Manukonda
- The Operation Eyesight Universal Institute for Eye Cancer, L V Prasad Eye Institute, Hyderabad 500034, India
- Brien Holden Eye Research Center, L V Prasad Eye Institute, Hyderabad 500034, India
| | - Vengala Rao Yenuganti
- Department of Animal Biology, School of Life Sciences, University of Hyderabad, Prof. C.R. Rao Road, Gachibowli, Hyderabad 500046, India or
| | - Nupur Nagar
- Department of Biosciences and Bioengineering, Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee 247667, India
| | - Pankaj Singh Dholaniya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Prof. C.R. Rao Road, Gachibowli, Hyderabad 500046, India
| | - Shivani Malpotra
- The Operation Eyesight Universal Institute for Eye Cancer, L V Prasad Eye Institute, Hyderabad 500034, India
- Brien Holden Eye Research Center, L V Prasad Eye Institute, Hyderabad 500034, India
| | - Jyothi Attem
- School of Medical Sciences, Science Complex, University of Hyderabad, Prof. C.R. Rao Road, Gachibowli, Hyderabad 500046, India
| | - Mamatha M. Reddy
- The Operation Eyesight Universal Institute for Eye Cancer, L V Prasad Eye Institute, Bhubaneswar 751024, India or
| | - Saumya Jakati
- Ophthalmic Pathology Laboratory, L V Prasad Eye Institute, Hyderabad 500034, India
| | - Dilip K Mishra
- Ophthalmic Pathology Laboratory, L V Prasad Eye Institute, Hyderabad 500034, India
| | - Pallu Reddanna
- Department of Animal Biology, School of Life Sciences, University of Hyderabad, Prof. C.R. Rao Road, Gachibowli, Hyderabad 500046, India or
| | - Krishna Mohan Poluri
- Department of Biosciences and Bioengineering, Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee 247667, India
| | - Geeta K. Vemuganti
- School of Medical Sciences, Science Complex, University of Hyderabad, Prof. C.R. Rao Road, Gachibowli, Hyderabad 500046, India
| | - Swathi Kaliki
- The Operation Eyesight Universal Institute for Eye Cancer, L V Prasad Eye Institute, Hyderabad 500034, India
- Correspondence: ; Tel.: +91-40-68102502
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50
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Comprehensive analysis of DRAIC and TP53TG1 in breast cancer luminal subtypes through the construction of lncRNAs regulatory model. Breast Cancer 2022; 29:1050-1066. [DOI: 10.1007/s12282-022-01385-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 07/07/2022] [Indexed: 12/23/2022]
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