| Title |
The lncRNA myocardial infarction associated transcript-centric competing endogenous RNA network in non-small-cell lung cancer
|
|---|---|
| Published in |
Cancer Management and Research, May 2018
|
| DOI | 10.2147/cmar.s163395 |
| Pubmed ID | |
| Authors |
Chang Zheng, Xuelian Li, Biyun Qian, Nannan Feng, Sumeng Gao, Yuxia Zhao, Baosen Zhou |
| Abstract |
The leading cause of death for cancer is lung cancer, of which the majority subtype is non-small cell lung cancer (NSCLC). Recent studies have shown long non-coding RNAs are transcribed and contribute to cancer. Previous study has shown that a few single nucleotide polymorphisms (SNPs) in myocardial infarction associated transcript (MIAT) were associated with some diseases or function as competing endogenous RNA (ceRNA) in some cancer. We performed bioinformatic methods for analyzing RNA-seq and miRNA-seq data of NSCLC from The Cancer Genome Atlas database. 1352 NSCLC patients and 1320 cancer-free controls for genotyping, and dual luciferase reporter assay, real-time PCR are performed in A549 and H1975 lung cancer cell lines. Results are analyzed by SPSS v16.0. In the present study, we focus on the role of over-expression MIAT in NSCLC. We confirmed that rs1061451 T>C (allele odds ratio = 0.22; P < 0.01) was associated with NSCLC. Furthermore, we constructed MIAT-centric ceRNA network, and three mRNAs (MYO1B, SGK1 and WNT9A) was identified as targets by MIAT via miR-133a-5p. C-containing genotypes of MIAT rs1061451 were protective factor of NSCLC, and MIAT, which may act as ceRNA via miR-133a-5p, modulated MYO1B, SGK1 and WNT9A expression level. |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 23% |
| Student > Doctoral Student | 2 | 15% |
| Student > Ph. D. Student | 1 | 8% |
| Researcher | 1 | 8% |
| Unknown | 6 | 46% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 3 | 23% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 15% |
| Psychology | 1 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 8% |
| Unknown | 6 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 May 2018.
All research outputs
#20,492,220
of 23,055,429 outputs
Outputs from Cancer Management and Research
#1,405
of 2,017 outputs
Outputs of similar age
#287,140
of 326,179 outputs
Outputs of similar age from Cancer Management and Research
#48
of 61 outputs
Altmetric has tracked 23,055,429 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,017 research outputs from this source. They receive a mean Attention Score of 3.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 326,179 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 61 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.