Journal of Clinical Images and Medical Case Reports
ISSN 2766-7820
Research Article - Open Access, Volume 4
In silico prediction of the miRNAs targeting CYBRD1, SMAD1
and GNG12 genes in multiple sclerosis (MS)
Omid Moeini*; Mahsa Khatibi; Armit Hosseini; Amirali Rahmani
Department of Laboratory Medicine, Faculty of Paramedical Sciences, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
*Corresponding Author : Omid Moeini
Department of Laboratory Medicine, Faculty of Paramedical Sciences, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
Email: [email protected]
Received : Mar 21, 2023
Accepted : Apr 06, 2023
Published : Apr 13, 2023
Archived : www.jcimcr.org
Copyright : © Moeini O (2023).
Abstract
MS is a devastating disease of the Central Nervous System (CNS) caused by autoimmune responses to CNS antigens such as myelin basic protein, leading to the neuronal damage and formation of demyelinating plaques in the brain, spinal cord and optic nerves. As the miRNAs play a significant role in the regulation of the cellular procedures, this study aims to predict the miRNAs, targeting the CYBRD1, SMAD1 and GNG12 genes using bioinformatics tools. After getting the CYBRD1, SMAD1 and GNG12 protein chain from the NCBI database, the miRNAs targeting the genes were predicted using miRDB, DIANA and miRWalk databases using different algorithms. Based on the scoring system of the bioinformatics software and considering the best targeting scores, (miR-3163, miR-2355-3p, miR-3659, miR-302e and miR-548 m), (miR-26a-5p and miR-205-5p) and (miR-5011-5p, miR-4774-5p, miR-4738-3p and miR-186-5p) are suggested as the potential miRNAs, targeting the CYBRD1, SMAD1 and GNG12 genes for future researches. Considering the significant role of the CYBRD1, SMAD1 and GNG12 genes in MS, the predicted miRNAs can be used as the molecular biomarkers for the early detection of MS patients.
Keywords: In silic; Multiple sclerosis; miRNAs; Bioinformatics database.
Citation: Moeini O, Khatibi M, Hosseini A, Rahmani A. In silico prediction of the miRNAs targeting CYBRD1, SMAD1 and GNG12 genes in multiple sclerosis (MS). J Clin Images Med Case Rep. 2023; 4(4): 2369.
Introduction
Multiple Sclerosis (MS) is one of the most important and common progressive neurological disorders, affecting 2.8 million people worldwide in 2020. That number is up 20% from the 2013 data from the League of Nations (MSIF). Over the last two decades, the incidence of MS has increased tremendously in many countries, demonstrating the need and importance of MS research to understand its pathophysiology to overcome the condition. MS is a devastating disease of the central nervous system CNS caused by autoimmune reactions to central nervous system antigens such as myelin basic proteins, causing nerve damage and demyelination plaques in the brain, spinal cord, and optic nerve. Brings the formation of. Immune inflammation in CNS in MS patients is caused by complex interactions between many immune cell types such as T cells, B cells, Dendritic Cells (DCs), macrophages, and NK cells [1-3]. It is now assumed that immune system disorders can be caused by epigenetic mechanisms such as changes in small non-coding RNAs, especially microRNAs (miRNAs) [4]. MicroRNAs are short single-stranded RNAs that are involved in the negative regulation of gene expression at the post-transcriptional level. Due to their properties, miRNAs can play a role in many cellular processes, including: Maintenance of homeostasis, cell differentiation and tissue development and their activity can be determined by many physiological or pathological factors [5]. miRNAs have been shown to be involved in the induction of inflammation by targeting lymphocyte differentiation and inflammatory cytokine secretion [6]. CYBRD1 is a member of the cytochrome B family, which encodes iron-regulating proteins. Like GNG12, most of the existing studies on this gene are associated with cancer [7,8] and knowledge of the role of CYBRD1 in MS remains unclear. Small Body Size (SMA) and Mothers Against Decapentaplegic Family 1 (SMAD1), also known as JV4–1, MADH1, MADR1 are mapped to the human chromosome 5q4 [9] and are the cause of breast cancer [10]. SMAD1 mediates the signaling of Bone Morphogenetic Protein (BMP) [11], which is involved in various biological activities such as cell growth, apoptosis, development, and immune response. BMP ligands induce phosphorylation and activation of SMAD1 by BMP receptor kinase. Phosphorylated SMAD1 forms a complex with SMAD4, where SMAD4 migrates to the cell nucleus, where it works with transcription factors to regulate gene transcription[12]. GNG12 is distributed in glial cells and is highly expressed in reactive astrocytes. Upregulation of GNG12 may promote protein kinase C (PKC) activity, promote phosphorylation of GNG12 protein, and negatively regulate the inflammatory response [13-15]. Unfortunately, research on the role of GNG12 in MS is very sparse. The only report is that GNG12 may become a novel negative regulator in response To Lipopolysaccharide (LPS) -induced inflammation of the microglial cell line BV2, suggesting a potential involvement in MS development [16]. Simultaneously with the identification and discovery of microRNAs, computational methods have been developed and tools for understanding the function of microRNAs and predicting the pairing of microRNAs with target genes have been introduced [17]. Using bioinformatics databases can facilitate the prediction of the miRNA attachment, which can be proved in experimental studies. These computational predictions can reduce the cost and the time of the studies [17].
Materials and methods
Predicting the miRNAs targeting CYBRD1, SMAD1 and GNG12 genes using miRWalk
The database presents the miRNAs of humans, mice, and rats in predicted and experimental categories. The algorithm predicts the attachment site of the miRNA in the genome sequence of the species (the mitochondrial sequence is also included). The site was searched with different names of the CYBRD1, SMAD1 and GNG12 genes.
Predicting the miRNAs targeting CYBRD1, SMAD1 and GNG12 genes using DIANA
The database evaluates the predictions using miTG Score. The algorithm scores the predictions using the score of multiple conserved and non-conserved sites, showing mRNA expression alterations. The database is connected to UCSC, HUGO, and Ensembl SwissProt databases, and the CYBRD1, SMAD1 and GNG12 gene was searched.
Predicting the miRNAs targeting CYBRD1, SMAD1 and GNG12 genes using miRDB
This online database predicts the miRNAs of humans, mice, rats, dogs, and chickens, which provides search by miRNA or the target gene. A 50 to 100 score is provided, which shows the higher probability of miRNA to mRNA attachment. The CYBRD1, SMAD1 and GNG12 genes was searched in this database.
Choosing the top miRNA: The output of each database was extracted to a Microsoft Excel spreadsheet and those with the highest attachment probability in most of the databases were selected for further experimental studies.
Result
Predicted CYBRD1 gene in miRDB, miRWalk, and DIANA (Figure 2)
The miRWalk database also showed that hsa-miR-3163, hsa-miR-548m, hsa-miR-302e, hsa-miR-3659 and hsa-miR-2355-3p target the CYBRD1 gene. Similar genes were also reported by the miRDB and RNA22 (Table 1).
The DIANA database resulted in miR-3163, miR-2355-3p, miR-3659, miR-302 and miR548 m with 0.999, 0.956, 0.936, 0,883 and 0.844 scores, respectively (Table 2).
The miRDB showed miR-3163, miR-548m, miR-302e, miR-3659 and miR-2355-3p with the highest score of probability to target the CYBRD1 gene (Table 3).
Predicted SMAD1 Gene in miRDB, miRWalk, and DIANA (Figure 3)
The miRWalk database also showed that hsa-miR-26a-5p and hsa-miR-205-5p target the SMAD1 gene. Similar genes were also reported by the miRDB and RNA22 (Table 5).
The DIANA database resulted in miR-26a-5p and miR-205-5p with 0.999 and 0.878 scores, respectively (Table 6).
The miRDB showed miR-26a-5p and miR-205-5p with the highest score of probability to target the SMAD1 gene (Table 7).
Predicted GNG12 Gene in miRDB, miRWalk, and DIANA (Figure 4)
The miRWalk database also showed that hsa-miR-186-5p, hsa-miR-4774-5p, hsa-miR-4738-3p and hsa-miR-5011-5p target the GNG12 gene. Similar genes were also reported by the miRDB and RNA22 (Table 9).
The DIANA database resulted in miR-5011-5p, miR-4774-5p, miR-4738-3p and miR-186-5p with 0.944, 0.931, 0.923 and 0.915 scores, respectively (Table 10).
The miRDB showed miR-186-5p, miR-4774-5p, miR-4738-3p and miR-5011-5p with the highest score of probability to target the GNG12 gene (Table 11).
Selecting the top miRNAs
Selecting the top miRNAs, targeting the CYBRD1, SMAD1 and GNG12 genes is shown in (Table 4, Table 8 and Table 12). As a result, miRNAs with the highest score and most probable attachment to the target gene are collected in Table 13. miRNAs with the highest frequency in various databases are chosen for the experimental phase of this study. The miR-3163, miR-548m, miR-302e, miR-3659 and miR-2355-3p for CYBRD1 gene, miR-26a-5p and miR-205-5p for SMAD1 gene and miR-186-5p, miR-4774-5p, miR-4738-3p and miR-5011-5p for GNG12 gene, are also chosen to be measured in healthy people and those with MS, in the experimental phase of the study (Table 13, Figure 1).
Table 1: Prediction results of miRNAs targeting the CYBRD1 gene at the miRWalk database.
| miRNA Predicted | miRWalk | MiRDB | DIANA | RNA22 |
|---|---|---|---|---|
| hsa-miR-3163 | □ | □ | □ | □ |
| hsa-miR-548m | □ | □ | □ | □ |
| hsa-miR-302e | □ | □ | □ | □ |
| hsa-miR-3659 | □ | □ | □ | □ |
| hsa-miR-2355-3p | □ | □ | □ | □ |
Table 2: Prediction results of miRNAs targeting the CYBRD1 gene at the DIANA database.
| Number | Ensembl gene ID | miRNA name | miTG Score |
|---|---|---|---|
| 1 | ENSG00000071967(CYBRD1) | miR-3163 | 0.999 |
| 2 | NSG00000071967(CYBRD1) | miR-20a-5p | 0.991 |
| 3 | ENSG00000071967(CYBRD1) | miR-548m | 0.844 |
| 4 | ENSG00000071967(CYBRD1) | miR-106a-5p | 0.988 |
| 5 | ENSG00000071967(CYBRD1) | miR-302e | 0.883 |
| 6 | ENSG00000071967(CYBRD1) | miR-526b-3p | 0.972 |
| 7 | ENSG00000071967(CYBRD1) | miR-3659 | 0.936 |
| 8 | ENSG00000071967(CYBRD1) | miR-3651 | 0.961 |
| 9 | ENSG00000071967(CYBRD1) | miR-2355-3p | 0.956 |
| 10 | ENSG00000071967(CYBRD1) | miR-2113 | 0.926 |
Table 3: Prediction results of miRNAs targeting the CYBRD1gene at the miRDB database.
| Target rank | Gene symbol | miRNA name | Target score |
|---|---|---|---|
| 1 | CYBRD1 | miR-302e | 96 |
| 2 | CYBRD1 | miR-4760-5p | 98 |
| 3 | CYBRD1 | miR-3659 | 96 |
| 4 | CYBRD1 | miR-6844 | 97 |
| 5 | CYBRD1 | miR-3163 | 98 |
| 6 | CYBRD1 | miR-2355-3p | 94 |
| 7 | CYBRD1 | miR-548m | 98 |
| 8 | CYBRD1 | miR-559 | 89 |
| 9 | CYBRD1 | miR-106b-5p | 88 |
| 10 | CYBRD1 | miR-155-3p | 87.0 |
Table 4: How to select the target CYBRD1 gene receptor miRNA.
| miRNA | miRWalk | miRDB | DIANA | Total score |
|---|---|---|---|---|
| hsa-miR-3163 | 1 | 1 | 1 | 3 |
| hsa-miR-548m | 1 | 1 | 1 | 3 |
| hsa-miR-302e | 1 | 1 | 1 | 3 |
| hsa-miR-3659 | 1 | 1 | 1 | 3 |
| hsa-miR-2355-3p | 1 | 1 | 1 | 3 |
| hsa-miR-155-3p | 0 | 1 | 0 | 1 |
| hsa-miR-526b-3p | 0 | 0 | 1 | 1 |
| hsa-miR-20a-5p | 0 | 0 | 1 | 1 |
| hsa-miR-106b-5p | 0 | 1 | 0 | 1 |
| hsa-miR-6844 | 0 | 1 | 0 | 1 |
Table 5: Prediction results of miRNAs targeting the SMAD1 gene at the miRWalk database.
| miRNA Predicted | miRWalk | MiRDB | DIANA | RNA22 |
|---|---|---|---|---|
| hsa-miR-26a-5p | □ | □ | □ | □ |
| hsa-miR-205-5p | □ | □ | □ | □ |
Table 6: Prediction results of miRNAs targeting the SMAD1 gene at the DIANA database.
| Number | Ensembl gene ID | miRNA name | miTG score |
|---|---|---|---|
| 1 | ENSG00000170365(SMAD1) | miR-26a-5p | 0.999 |
| 2 | ENSG00000170365(SMAD1) | miR-205-5p | 0.878 |
| 3 | ENSG00000170365(SMAD1) | miR-1297 | 0.999 |
| 4 | ENSG00000170365(SMAD1) | miR-26b-5p | 0.999 |
| 5 | ENSG00000170365(SMAD1) | miR-5697 | 0.991 |
| 6 | ENSG00000170365(SMAD1) | miR-4448 | 0.935 |
| 7 | ENSG00000170365(SMAD1) | miR-223-3p | 0.902 |
| 8 | ENSG00000170365(SMAD1) | miR-4708 | 0.901 |
| 9 | ENSG00000170365(SMAD1) | miR-3922 | 0.861 |
| 10 | ENSG00000170365(SMAD1) | miR-345-5p | 0.818 |
Table 7: Prediction results of miRNAs targeting the SMAD1 gene at the miRDB database.
| Target rank | Gene symbol | miRNA name | Target score |
|---|---|---|---|
| 1 | SMAD1 | miR-26a-5p | 97 |
| 2 | SMAD1 | miR-205-5p | 87 |
| 3 | SMAD1 | miR-5689 | 91 |
| 4 | SMAD1 | miR-4282 | 91 |
| 5 | SMAD1 | miR-12122 | 86 |
| 6 | SMAD1 | miR-12133 | 86 |
| 7 | SMAD1 | miR-545-3p | 89 |
| 8 | SMAD1 | miR-203a-3p | 90 |
| 9 | SMAD1 | miR-4691-5p | 91 |
| 10 | SMAD1 | miR-6792-3p | 91 |
Table 8: How to select the target SMAD1 gene receptor miRNA.
| miRNA | miRWalk | miRDB | DIANA | Total score |
|---|---|---|---|---|
| miR-26a-5p | 1 | 1 | 1 | 3 |
| miR-205-5p | 1 | 1 | 1 | 3 |
| miR-203a-3p | 0 | 1 | 1 | |
| miR-4282 | 0 | 1 | 0 | 1 |
| miR-3922 | 0 | 0 | 1 | 1 |
| miR-5697 | 0 | 0 | 1 | 1 |
| miR-6792 | 0 | 1 | 0 | 1 |
| miR-12133 | 0 | 1 | 0 | 1 |
| miR-345-5p | 0 | 0 | 1 | 1 |
| miR-223-3p | 0 | 0 | 1 | 1 |
Table 9: Prediction results of miRNAs targeting the GNG12 gene at the miRWalk database.
| miRNA Predicted | miRWalk | MiRDB | DIANA | RNA22 |
|---|---|---|---|---|
| miR-186-5p | □ | □ | □ | □ |
| miR-4774-5p | □ | □ | □ | □ |
| miR-4738-3p | □ | □ | □ | □ |
| miR-5011-5p | □ | □ | □ | □ |
Table 10: Prediction results of miRNAs targeting the GNG12 gene at the DIANA database.
| Numbe | Ensembl gene ID | miRNA name | miTG score |
|---|---|---|---|
| 1 | ENSG000000172380(GNG12) | miR-186-5pmiR-186-5p | 0.915 |
| 2 | ENSG000000172380(GNG12) | miR-5011-5p | 0.944 |
| 3 | ENSG000000172380(GNG12) | miR-4738-3p | 0.923 |
| 4 | ENSG000000172380(GNG12) | miR-4774-5p | 0.931 |
| 5 | ENSG000000172380(GNG12) | miR-7853-5p | 0.991 |
| 6 | ENSG000000172380(GNG12) | miR-545-5p | 0.970 |
| 7 | ENSG000000172380(GNG12) | miR-29b-3p | 0.963 |
| 8 | ENSG000000172380(GNG12) | miR-29a-3p | 0.957 |
| 9 | ENSG000000172380(GNG12) | miR-590-3p | 0.946 |
| 10 | ENSG000000172380(GNG12) | miR-6768-3p | 0.932 |
Table 11: Prediction results of miRNAs targeting the GNG12 gene at the miRDB database.
| Target Rank | Gene symbol | miRNA name | Target score |
|---|---|---|---|
| 1 | GNG12 | miR-186-5p | 93 |
| 2 | GNG12 | miR-5011-5p | 91 |
| 3 | GNG12 | miR-4738-3p | 92 |
| 4 | GNG12 | miR-4774-5p | 93 |
| 5 | GNG12 | miR-6773-5p | 93 |
| 6 | GNG12 | miR-4531 | 91 |
| 7 | GNG12 | miR-3123 | 91 |
| 8 | GNG12 | miR-3913-3p | 87 |
| 9 | GNG12 | miR-570-5p | 86 |
| 10 | GNG12 | miR-3617-5p | 82 |
Table 12: How to select the target GNG12gene receptor miRNA.
| miRNA | miRWalk | miRDB | DIANA | Total score |
|---|---|---|---|---|
| miR-186-5p | 1 | 1 | 1 | 3 |
| miR-5011-5p | 1 | 1 | 1 | 3 |
| miR-4738-3p | 1 | 1 | 1 | 3 |
| miR-4774-5p | 1 | 1 | 1 | 3 |
| miR-3617-5p | 0 | 1 | 0 | 1 |
| miR-570-5p | 0 | 1 | 0 | 1 |
| miR-6768 | 0 | 0 | 1 | 1 |
| miR-6773 | 0 | 1 | 0 | 1 |
| miR-7853-3p | 0 | 0 | 1 | 1 |
| miR-29a-3p | 0 | 0 | 1 | 1 |
Table 13: Selected miRNAs for expression in patients with MS.
| Gene Name | MiRNA | Accession number |
|---|---|---|
| CYBRD1 | miR-3163 | MI0014193 |
| miR-548m | MI0006400 | |
| miR-3659 | MI0016060 | |
| miR-302e | MI0006417 | |
| miR-2355-3p | MI0015873 | |
| SMAD1 | miR-26a-5p | MI0000083 |
| miR-205-5p | MI0000285 | |
| GNG12 | miR-186-5p | MI0000483 |
| miR-4774-5p | MI0017417 | |
| miR-4738-3p | MI0017376 | |
| miR-5011-5p | MI0017879 |
Discussion
Bioinformatics is an important technique to manage biological big data. A collection of tools and software are used in modern biotechnology, using mathematics and statistics, to gain insight into the biological data and find answers for medical and biological questions [18]. Extended studies are performed to develop miRNA measurement techniques. Microarray, in-situ hybridization, northern blot, and real-time PCR are used today to measure the miRNA expression. However, they are not feasible techniques for all of the purposes, due to high cost and required time. On the other hand, bioinformatics techniques are effective tools with lower cost to predict the miRNA interactions [19,20]. Mo and colleagues have studied the miRNA roles in cancer and their importance as biomarkers, in 2012 [21].
Conclusion
This study suggests the (miR-3163, miR-2355-3p, miR-3659, miR-302e and miR548m), (miR-26a-5p and miR-205-5p) and (miR-5011-5p, miR-4774-5p, miR-4738-3p and miR-186-5p) and molecules as miRNAs with a high probability of targeting the CYBRD1, SMAD1 and GNG12 genes , based on their scores in different bioinformatics tools. Considering the important role of the CYBRD1, SMAD1 and GNG12 genes in MS, the molecules seem to be useful in the early diagnosis of MS patients.
Declarations and author statements
Declarations: Ethics approval and consent to participate.
All project data were completely anonymous and collected from public resources.
Consent for publication: Not applicable.
Competing interest’s disclosure: The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants, or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.
Funding: Not applicable.
Authors contributions: Data and graphical analysis was performed by Omid Moeini and Seyed Armit Hosseini and Amirali Rahmani wrote the manuscript. And this work was supervised by Mr. Omid Moeini.
Acknowledgements: Not applicable.
References
- Van Kaer L, Postoak JL, Wang C, Yang G, Wu L, et al. Innate, innate-like and adaptive lymphocytes in the pathogenesis of MS and EAE. Cellular & Molecular Immunology. 2019; 16: 531-539.
- El Behi M, Dubucquoi S, Lefranc D, Zéphir H, De Seze J, Vermersch P, Prin L, et al. New insights into cell responses involved in experimental autoimmune encephalomyelitis and multiple sclerosis. Immunology letters 2005; 96: 11-26.
- Gandhi R, Laroni A, Weiner HL. Role of the innate immune system in the pathogenesis of multiple sclerosis.Journal of neuroimmunology. 2010; 221: 7-14.
- Moeini O, Gholamzad A, Khatibi M, Nourikhani M, Hosseini S, et al. Computational Analysis of crRNA to Regulate hsa-miR-301b-3p and hsa-miR-21 related to metastasis and cell proliferation in Acute Myeloid Leukemia (AML) using CRISPR/C2c2.
- Mahmanzar M, Houseini ST, Rahimian K, Mikaeili Namini A, Gholamzad A, et al. The First Geographic Identification by Country of Sustainable Mutations of SARS-COV-2 Sequence Samples: Worldwide Natural Selection Trends. bioRxiv. 2022: 2022-07.
- Gholamzad A, Daryoushi G, Khatibi M, Khonche S, Ahmadpour A, et al. A Comprehensive Review in Novel Therapeutics in Acute Leukemia. IJCMCR. 2022; 19: 3: 46.
- Qing M, Zhou J, Chen W, Cheng L, et al. Highly expressed CYBRD1 associated with glioma recurrence regulates the immune response of glioma cells to interferon. Evidence-based Complementary and Alternative Medicine. 2021; 2021.
- Chen R, Cao J, Jiang W, Wang S, Cheng J, et al. Upregulated expression of CYBRD1 predicts poor prognosis of patients with ovarian cancer. Journal of Oncology. 2021; 2021.
- Chaudhury A, Howe PH. The tale of transforming growth factor‐beta (TGFβ) signaling: A soigné enigma.IUBMB life. 2009; 61: 929-939.
- Heshmatzad K, Gholamzad A, Gholamzad M, Eslami M. Designing and Development of a DNA Vaccine Expressing HER2/HER3 and CCL20 as a Novel Candidate for Breast Cancer Vaccine.
- Chegini H, Chegini A, Gholamzad A, Sadeghi H, Noori Mougahi SM, et al. Morphological evaluation of rat cerebellum following administration of nitrogen monoxide precursor and inhibitor. bioRxiv. 2022: 2022-07.
- Chandrasinghe P, Cereser B, Moorghen M, Al Bakir I, Tabassum N, et al. Role of SMAD proteins in colitis-associated cancer: from known to the unknown.Oncogene. 2018; 37: 1-7.
- Cai YD, Zhang S, Zhang YH, Pan X, Feng K, et al. Identification of the gene expression rules that define the subtypes in glioma.Journal of clinical medicine. 2018; 7: 350.
- Li J, Jin C, Zou C, Qiao X, Ma P. GNG12 regulates PD-L1 expression by activating NF-κB signaling in pancreatic ductal adenocarcinoma.FEBS Open Bio. 2020; 10: 278-287.
- Yuan J, Yuan Z, Ye A, Wu T, Jia J. Low GNG12 expression predicts adverse outcomes: A potential therapeutic target for osteosarcoma.Frontiers in immunology. 2021: 4124.
- Larson KC, Draper MP, Lipko M, Dabrowski M. Gng12 is a novel negative regulator of LPS-induced inflammation in the microglial cell line BV-2.Inflammation research. 2010; 59: 15-22.
- Riffo-Campos ÁL, Riquelme I, Brebi-Mieville P. Tools for sequence-based miRNA target prediction: what to choose? International journal of molecular sciences. 2016; 17: 1987.
- Akhtar MM, Micolucci L, Islam MS, Olivieri F, Procopio AD, et al. Bioinformatic tools for microRNA dissection.Nucleic acids research. 2016; 44: 24-44.
- Ritchie W, Rasko JE, Flamant S. MicroRNA target prediction and validation. MicroRNA cancer regulation. 2013: 39-53.
- Ghanbari H, Latifi P, Loodaricheh MA, Fard AS, Ababaf E, et al. Application of Nanomedicine in Cancers.
- Mo M-H, Chen L, Fu Y, Wang W, Fu SW, et al. Cell-free circulating miRNA biomarkers in cancer. Journal of Cancer. 2012; 3: 432.