| Chapter title |
MiR-140-3p Downregulation in Association with PDL-1 Overexpression in Many Cancers: A Review from the Literature Using Predictive Bioinformatics Tools
|
|---|---|
| Chapter number | 18 |
| Book title |
GeNeDis 2016
|
| Published in |
Advances in experimental medicine and biology, January 2017
|
| DOI | 10.1007/978-3-319-56246-9_18 |
| Pubmed ID | |
| Book ISBNs |
978-3-31-956245-2, 978-3-31-956246-9
|
| Authors |
Nikolaos Kapodistrias, Catherine Bobori, Georgia Theocharopoulou, Kapodistrias, Nikolaos, Bobori, Catherine, Theocharopoulou, Georgia |
| Abstract |
Programmed death-ligand 1 (PD-L1) has been speculated to play a critical role in suppression of the immune system and it can be upregulated in cancer cells, which may allow cancers to evade the host immune system. MicroRNAs (miRNAs) are small non-coding RNA molecules (containing about 22 nucleotides), that function in RNA silencing and post-transcriptional regulation of gene expression. MiRNAs were found deregulated (upregulated or downregulated) and implicated in cancer development with various roles which depend on their gene target. Using targetscan web server prediction algorithm, we concluded that miR-140-3p is a targeting mirRNA with conserved consequential pairing of target region for PD-L1. Moreover, by reviewing all the available cancer studies in Pub/Medline about miR-140-3p, was found permanently down regulated. Furthermore, in recent immunotherapy related clinical trials in most cancers, evaluated PD-L1, it is found overexpressed. In the near future, in vitro or in vivo studies need to validate whether there is direct correlation between PD-L1 overexpression and miR-140-3p downregulation as targetscan performed algorithm predicted. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 17% |
| Researcher | 5 | 17% |
| Student > Ph. D. Student | 5 | 17% |
| Other | 3 | 10% |
| Student > Postgraduate | 2 | 7% |
| Other | 5 | 17% |
| Unknown | 4 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 31% |
| Biochemistry, Genetics and Molecular Biology | 8 | 28% |
| Immunology and Microbiology | 2 | 7% |
| Social Sciences | 1 | 3% |
| Neuroscience | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 8 | 28% |