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: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
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
Background: M6A RNA methylation and the tumor microenvironment (TME) have been reported to play important roles in the progression and prognosis of clear cell renal cell carcinoma (ccRCC). However, whether m6A RNA methylation regulators affect the TME in ccRCC remains unknown. Thus, we aimed to evaluate comprehensively the effect of m6A RNA methylation regulators on the TME in ccRCC.
Methods: Transcriptome data of ccRCC were obtained from TCGA database. Consensus clustering analysis was conducted based on the expression of m6A RNA methylation regulators. Survival differences were evaluated by Kaplan-Meier analysis between the clusters. The DESeq2 package was used to analyze the differentially expressed genes (DEGs) between the clusters. GO and KEGG pathway analyses were performed by the ClusterProfiler R package. The CIBERSORT algorithm was used to evaluate immune infiltration.
Results: The expression of 15 m6A regulators significantly differed between ccRCC and normal kidney tissues. Based on the expression of these 15 m6A regulators, two clusters were identified by consensus clustering, in which cluster 1 had better overall survival (OS). Overall, 4,429 DEGs were identified between the two clusters and were enriched in immune-related biological processes. Cluster 1 had lower immune and ESTIMATE scores, higher expression of HLA and lower expression of immune checkpoint molecules. Moreover, immune infiltration and expressions of Th1/IFNγ gene signature were also significantly different between the two clusters.
Conclusion: Our study revealed m6A regulators were important participants in the development of ccRCC, with a close relationship with the TME.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11607164 | PMC |
http://dx.doi.org/10.18502/ijph.v53i11.16957 | DOI Listing |
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