Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Purpose: Despite vast improvements that have been made in the treatment of children with acute lymphoblastic leukemia (ALL), the majority of infant ALL patients (~80 %, < 1 year of age) that carry a chromosomal translocation involving the mixed lineage leukemia (MLL) gene shows a poor response to chemotherapeutic drugs, especially glucocorticoids (GCs), which are essential components of all current treatment regimens. Although addressed in several studies, the mechanism(s) underlying this phenomenon have remained largely unknown. A major drawback of most previous studies is their primary focus on individual genes, thereby neglecting the putative significance of inter-gene correlations. Here, we aimed at studying GC resistance in MLL-rearranged infant ALL patients by inferring an associated module of genes using co-expression network analysis. The implications of newly identified candidate genes with associations to other well-known relevant genes from the same module, or with associations to known transcription factor or microRNA interactions, were substantiated using literature data.
Methods: A weighted gene co-expression network was constructed to identify gene modules associated with GC resistance in MLL-rearranged infant ALL patients. Significant gene ontology (GO) terms and signaling pathways enriched in relevant modules were used to provide guidance towards which module(s) consisted of promising candidates suitable for further analysis.
Results: Through gene co-expression network analysis a novel set of genes (module) related to GC-resistance was identified. The presence in this module of the S100 and ANXA genes, both well-known biomarkers for GC resistance in MLL-rearranged infant ALL, supports its validity. Subsequent gene set net correlation analyses of the novel module provided further support for its validity by showing that the S100 and ANXA genes act as 'hub' genes with potentially major regulatory roles in GC sensitivity, but having lost this role in the GC resistant phenotype. The detected module implicates new genes as being candidates for further analysis through associations with known GC resistance-related genes.
Conclusions: From our data we conclude that available systems biology approaches can be employed to detect new candidate genes that may provide further insights into drug resistance of MLL-rearranged infant ALL cases. Such approaches complement conventional gene-wise approaches by taking putative functional interactions between genes into account.
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Source |
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http://dx.doi.org/10.1007/s13402-016-0303-7 | DOI Listing |
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