AI Article Synopsis

  • * The researchers used data from The Cancer Genome Atlas to identify eRNAs linked to disease-free survival and developed a risk score model to predict patient prognosis.
  • * Key findings include a validated gene signature associated with eRNAs, insights into their influence on the tumor microenvironment, and variations in drug sensitivity based on patient risk levels.

Article Abstract

Prostate cancer (PCa) is the second most common malignancy in men, but its exact pathogenetic mechanisms remain unclear. This study explores the effect of enhancer RNAs (eRNAs) in PCa. Firstly, we screened eRNAs and eRNA -driven genes from The Cancer Genome Atlas (TCGA) database, which are related to the disease-free survival (DFS) of PCa patients;. screening methods included bootstrapping, Kaplan-Meier (KM) survival analysis, and Pearson correlation analysis. Then, a risk score model was established using multivariate Cox analysis, and the results were validated in three independent cohorts. Finally, we explored the function of eRNA-driven genes through enrichment analysis and analyzed drug sensitivity on datasets from the Genomics of Drug Sensitivity in Cancer database. We constructed and validated a robust prognostic gene signature involving three eRNA-driven genes namely , and . Moreover, we evaluated the function of eRNA-driven genes associated with tumor microenvironment (TME) and tumor mutational burden (TMB), and identified remarkable differences in drug sensitivity between high- and low-risk groups. This study identified a prognostic gene signature, which provides new insights into the role of eRNAs and eRNA-driven genes while assisting clinicians to determine the prognosis and appropriate treatment options for patients with PCa.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276043PMC
http://dx.doi.org/10.3389/fgene.2021.676845DOI Listing

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Article Synopsis
  • * The researchers used data from The Cancer Genome Atlas to identify eRNAs linked to disease-free survival and developed a risk score model to predict patient prognosis.
  • * Key findings include a validated gene signature associated with eRNAs, insights into their influence on the tumor microenvironment, and variations in drug sensitivity based on patient risk levels.
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