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
Background: Immune-based therapies are commonly employed to combat hepatocellular carcinoma (HCC). However, the presence of immune-regulating elements, especially regulatory T cells (Tregs), can dramatically impact the treatment efficacy. A deeper examination of the immune-regulation mechanisms linked to these inhibitory factors and their impact on HCC patient outcomes is warranted.
Methods: We employed multicolor fluorescence immunohistochemistry (mIHC) to stain Foxp3, cytokeratin, and nuclei on an HCC tissue microarray (TMA). Leveraging liver cancer transcriptome data from TCGA, we built a prognostic model focused on Treg-associated gene sets and represented it with a nomogram. We then sourced liver cancer single-cell RNA sequencing data (GSE140228) from the GEO database, selectively focusing on Treg subsets, and conducted further analyses, including cell-to-cell communication and pseudo-time trajectory examination.
Results: Our mIHC results revealed a more substantial presence of Foxp3Tregs in HCC samples than in adjacent normal tissue samples ( < .001). An increased presence of Foxp3Tregs in HCC samples correlated with unfavorable patient outcomes ( = 1.722, 95% :1.023-2.899, = .041). The multi-factorial prognosis model we built from TCGA liver cancer data highlighted Tregs as a standalone risk determinant for predicting outcomes ( = 3.84, 95% 2.52-5.83, < .001). Re-analyzing the scRNA-seq dataset (GSE140228) showcased distinctive gene expression patterns in Tregs from varying tissues. Interactions between Tregs and other CD4T cell types were predominantly governed by the CXCL13/CXCR3 signaling pathway. Communication pathways between Tregs and macrophages primarily involved MIF-CD74/CXCR4, LGALS9/CD45, and PTPRC/MRC1. Additionally, macrophages could influence Tregs via HLA-class II and CD4 interactions.
Conclusion: An elevated presence of Tregs in HCC samples correlated with negative patient outcomes. Elucidating the interplay between Tregs and other immune cells in HCC could provide insights into the modulatory role of Tregs within HCC tissues.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11075595 | PMC |
http://dx.doi.org/10.1177/10732748241251580 | DOI Listing |
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