| Title |
Integrative computational in-depth analysis of dysregulated miRNA-mRNA interactions in drug-resistant pediatric acute lymphoblastic leukemia cells: an attempt to obtain new potential gene-miRNA pathways involved in response to treatment
|
|---|---|
| Published in |
Tumor Biology, December 2015
|
| DOI | 10.1007/s13277-015-4553-1 |
| Pubmed ID | |
| Authors |
Hamzeh Mesrian Tanha, Marjan Mojtabavi Naeini, Soheila Rahgozar, Alireza Moafi, Mohammad Amin Honardoost |
| Abstract |
Acute lymphoblastic leukemia (ALL) is the major neoplasia type among children. Despite the tremendous success of current treatment strategies, drug resistance still remains a major cause of chemotherapy failure and relapse in pediatric patients. Overwhelming evidence illustrates that microRNAs (miRNAs) act as post-transcriptional regulators of drug-resistance-related genes. The current study was aimed at how dysregulated miRNA-mRNA-signaling pathway interaction networks mediate resistance to four commonly used chemotherapy agents in pediatric ALL, including asparaginase, daunorubicin, prednisolone, and vincristine. Using public expression microarray datasets, a holistic in silico approach was utilized to investigate candidate drug resistance miRNA-mRNA-signaling pathway interaction networks in pediatric ALL. Our systems biology approach nominated significant drug resistance and cross-resistance miRNAs, mRNAs, and cell signaling pathways based on anti-correlative relationship between miRNA and mRNA expression pattern. To sum up, our systemic analysis disclosed either a new potential role of miRNAs, or a possible mechanism of cellular drug resistance, in chemotherapy resistance of pediatric ALL. The current study may shed light on predicting drug response and overcoming drug resistance in childhood ALL for subsequent generations of chemotherapies. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 49 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 20% |
| Researcher | 9 | 18% |
| Student > Bachelor | 5 | 10% |
| Student > Master | 5 | 10% |
| Student > Doctoral Student | 2 | 4% |
| Other | 7 | 14% |
| Unknown | 11 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 16 | 33% |
| Medicine and Dentistry | 7 | 14% |
| Agricultural and Biological Sciences | 5 | 10% |
| Unspecified | 1 | 2% |
| Computer Science | 1 | 2% |
| Other | 3 | 6% |
| Unknown | 16 | 33% |
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 25 December 2015.
All research outputs
#20,299,108
of 22,836,570 outputs
Outputs from Tumor Biology
#1,834
of 2,622 outputs
Outputs of similar age
#327,693
of 390,595 outputs
Outputs of similar age from Tumor Biology
#191
of 298 outputs
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