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
Estrogen receptor-α (ESR1) single nucleotide polymorphisms (SNPs) have been related to breast cancer (BC) susceptibility. In this retrospective study we investigated ESR1 SNPs in association with survival and treatment response in BC patients. Seven ESR1 SNPs were genotyped using TaqMan probe assay in 100 formalin-fixed paraffin embedded blocks of Egyptian ERBC patients. Log-binomial regression was used to assess the association of 5 ESR1 SNPs with relative risk of non-response to adjuvant-hormonal treatment. We compared the performance of five machine learning classification models for prediction of treatment response. Predictive models were developed using rs1801132, rs2228480, and rs9322354 that were significantly associated with increased risk for non-response along with the relevant clinical features. Survival analysis was performed to detect prognostic significance of ESR1 SNPs in ESRBC patients. rs1801132 (C), rs2228480 (A), and rs9322354 (G) minor alleles significantly increased the risk of non-response to tamoxifen by more than 81, 84, and 117%, respectively, in ERBC patients on anthracycline/anthracycline-taxanes-based chemotherapy. Multivariate Cox regression survival analysis revealed that rs1801132 (C) and large tumor size were independent predictors for poor survival outcome in ERBC. The best response predictive model was a combination random forest, K-nearest neighbor, and decision tree having an area under the curve of 0.94 and an accuracy of 90.8%. Our proposed predictive model based on ESR1 rs1801132, rs2228480, and rs9322354 SNPs represents a promising genetic risk stratification for selection patients who could benefit from tamoxifen therapy in such a way that might facilitate personalized medicine required to improve ERBC patients' outcome.
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http://dx.doi.org/10.1007/s10528-022-10199-3 | DOI Listing |
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