Adenylate Uridylate- (AU-) Rich Element Gene-Based Prognostic Signature and Molecular Subtypes of Prostate Adenocarcinoma: Implications for Prognosis and Immune Microenvironment.
Prostate adenocarcinoma (PRAD) is the most prevalent malignancy in men and frequently evades early detection. However, the role of genes containing adenylate uridylate- (AU-) rich elements (AREGs) in PRAD remains largely uncharacterized.
Publicly available PRAD datasets were analyzed through weighted gene co-expression network analysis (WGCNA) to identify co-expressed gene modules. Unsupervised clustering defined AREG-associated molecular subtypes. Prognostic genes were selected via univariate/multivariate Cox proportional hazards regression (Cox) regression and least absolute shrinkage and selection operator (LASSO) regularization. Tumor immune infiltration was profiled using CIBERSORT and other bioinformatic tools, with functional enrichment revealing associated mechanisms. Single-cell transcriptomics (TISCH2) and drug sensitivity predictions (CellMiner) were integrated. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) validated hub gene expression in PRAD.
We identified three AU-rich element-related prognostic genes: ACSM3, ACTG2, and DES. The low-risk group exhibited enhanced immune pathway activity and elevated tumor-infiltrating immune cell levels compared to high-risk patients. Functional analyses linked high-risk scores to pathways such as glycosylation and proteasome regulation. Single-cell transcriptomics revealed widespread expression of ACSM3, while ACTG2 and DES were fibroblast-enriched. Drug sensitivity predictions suggested Docetaxel as a potential therapeutic agent for high-risk PRAD patients.
In this study, we propose that an AREG-based signature comprising ACSM3, ACTG2, and DES effectively predicts prognosis and reflects immune microenvironment characteristics in PRAD. Through systematic analysis, we established a prognostic model utilizing these three AREGs, which demonstrates strong potential as a clinical predictor for PRAD patient outcomes.
Publicly available PRAD datasets were analyzed through weighted gene co-expression network analysis (WGCNA) to identify co-expressed gene modules. Unsupervised clustering defined AREG-associated molecular subtypes. Prognostic genes were selected via univariate/multivariate Cox proportional hazards regression (Cox) regression and least absolute shrinkage and selection operator (LASSO) regularization. Tumor immune infiltration was profiled using CIBERSORT and other bioinformatic tools, with functional enrichment revealing associated mechanisms. Single-cell transcriptomics (TISCH2) and drug sensitivity predictions (CellMiner) were integrated. Finally, quantitative reverse transcription polymerase chain reaction (qRT-PCR) validated hub gene expression in PRAD.
We identified three AU-rich element-related prognostic genes: ACSM3, ACTG2, and DES. The low-risk group exhibited enhanced immune pathway activity and elevated tumor-infiltrating immune cell levels compared to high-risk patients. Functional analyses linked high-risk scores to pathways such as glycosylation and proteasome regulation. Single-cell transcriptomics revealed widespread expression of ACSM3, while ACTG2 and DES were fibroblast-enriched. Drug sensitivity predictions suggested Docetaxel as a potential therapeutic agent for high-risk PRAD patients.
In this study, we propose that an AREG-based signature comprising ACSM3, ACTG2, and DES effectively predicts prognosis and reflects immune microenvironment characteristics in PRAD. Through systematic analysis, we established a prognostic model utilizing these three AREGs, which demonstrates strong potential as a clinical predictor for PRAD patient outcomes.