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author:

Chen, Huiqin (Chen, Huiqin.) [1] | Zhang, Dihua (Zhang, Dihua.) [2] | Zhang, Guoping (Zhang, Guoping.) [3] | Li, Xiaofeng (Li, Xiaofeng.) [4] | Liang, Ying (Liang, Ying.) [5] | Kasukurthi, Mohan Vamsi (Kasukurthi, Mohan Vamsi.) [6] | Li, Shengyu (Li, Shengyu.) [7] | Borchert, Glen M. (Borchert, Glen M..) [8] | Huang, Jingshan (Huang, Jingshan.) [9]

Indexed by:

Scopus SCIE

Abstract:

Background: Acute lymphoblastic leukemia is the most prevalent neoplasia among children. Despite the tremendous achievements of state-of-the-art treatment strategies, drug resistance is still a major cause of chemotherapy failure leading to relapse in pediatric acute lymphoblastic leukemia. The underlying mechanisms of such phenomenon are not yet clear and subject to further exploration. Prior research has shown that microRNAs can act as post-transcriptional regulators of many genes related to drug resistance. However, details of microRNA regulation mechanisms in pediatric acute lymphoblastic leukemia are far from completely understood. Methods: We utilized a computational approach based upon emerging biomedical and biological ontologies and semantic technologies to investigate the important roles of microRNA: mRNA regulation on glucocorticoid resistance in pediatric acute lymphoblastic leukemia. In particular, various filtering mechanisms were designed based on the user-provided MeSH term to narrow down the most promising microRNAs in an effective manner. Results: During our manual search on background literature, we found a total of 18 candidate microRNAs that possibly regulate glucocorticoid resistance in pediatric acute lymphoblastic leukemia. After the first-round filtering using the Broader-Match option where both the user-provided MeSH term and its direct parent term were utilized, the number of targets for 18 microRNAs was reduced from 232 to 74. During the second-round filtering with the Exact-Match option where only the MeSH term itself was utilized, the number of targets was further reduced to 19. Finally, we conducted semantic searches in the OmniSearch software tool on the five likely regulating microRNAs and identified two most likely microRNAs. Conclusions: We successfully identified two microRNAs, hsa-miR-142-3p and hsa-miR-17-5p, which are computationally predicted to closely relate to glucocorticoid resistance, thus potentially serving as novel biomarkers and therapeutic targets in pediatric acute lymphoblastic leukemia.

Keyword:

Acute lymphoblastic leukemia (ALL) Biomedical and biological ontology (bio-ontology) Drug resistance Glucocorticoids (GC) microRNA (miRNA or miR) miRNA target Semantic integration Semantic search

Community:

  • [ 1 ] [Kasukurthi, Mohan Vamsi]Univ S Alabama, Sch Comp, Mobile, AL 36688 USA
  • [ 2 ] [Li, Shengyu]Univ S Alabama, Sch Comp, Mobile, AL 36688 USA
  • [ 3 ] [Huang, Jingshan]Univ S Alabama, Sch Comp, Mobile, AL 36688 USA
  • [ 4 ] [Huang, Jingshan]Fuzhou Univ, Coll Math & Comp Sci, Fuzhou, Fujian, Peoples R China
  • [ 5 ] [Chen, Huiqin]Sun Yat Sen Univ, Affiliated Hosp 3, Dept Pediat, Guangzhou, Guangdong, Peoples R China
  • [ 6 ] [Li, Xiaofeng]Sun Yat Sen Univ, Affiliated Hosp 3, Dept Pediat, Guangzhou, Guangdong, Peoples R China
  • [ 7 ] [Liang, Ying]Sun Yat Sen Univ, Affiliated Hosp 3, Dept Pediat, Guangzhou, Guangdong, Peoples R China
  • [ 8 ] [Zhang, Dihua]Sun Yat Sen Univ, Affiliated Hosp 1, Dept Nephrol, Guangzhou, Guangdong, Peoples R China
  • [ 9 ] [Zhang, Guoping]Yuebei Peoples Hosp, Dept Oncol, Shaoguan, Guangdong, Peoples R China
  • [ 10 ] [Borchert, Glen M.]Univ S Alabama, Coll Med, Mobile, AL 36688 USA
  • [ 11 ] [Huang, Jingshan]Qilu Univ Technol, Shandong Acad Sci, Sch Informat, Jinan, Shandong, Peoples R China

Reprint 's Address:

  • 蔡其洪

    [Huang, Jingshan]Univ S Alabama, Sch Comp, Mobile, AL 36688 USA;;[Huang, Jingshan]Fuzhou Univ, Coll Math & Comp Sci, Fuzhou, Fujian, Peoples R China;;[Huang, Jingshan]Qilu Univ Technol, Shandong Acad Sci, Sch Informat, Jinan, Shandong, Peoples R China

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Source :

BMC MEDICAL INFORMATICS AND DECISION MAKING

ISSN: 1472-6947

Year: 2018

Volume: 18

2 . 0 6 7

JCR@2018

3 . 3 0 0

JCR@2023

ESI Discipline: CLINICAL MEDICINE;

ESI HC Threshold:182

JCR Journal Grade:3

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 11

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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