Abstract
The presence of a poly(A) tail is indispensable for the post-transcriptional regulation of gene expression in cancer. This dynamic and modifiable feature of transcripts is under the control of various nuclear and cytoplasmic proteins. This study aimed to develop a novel cytoplasmic poly(A)-related signature for predicting prognosis, clinical attributes, tumor immune microenvironment (TIME), and treatment response in hepatocellular carcinoma (HCC). Utilizing RNA-seq data from The Cancer Genome Atlas (TCGA), non-negative matrix factorization (NMF) and principal component analysis (PCA) were employed to categorize HCC patients into three clusters, thus demonstrating the pivotal prognostic role of cytoplasmic poly(A) tail regulators. Furthermore, machine learning algorithms such as least absolute shrinkage and selection operator (LASSO), survival analysis, and Cox proportional hazards modeling were able to distinguish distinct cytoplasmic poly(A) subtypes. As a result, a 5-gene signature derived from TCGA was developed and validated using International Cancer Genome Consortium (ICGC) HCC datasets. This novel classification based on cytoplasmic poly(A) regulators has the potential to improve prognostic predictions and provide guidance for chemotherapy, immunotherapy, and transarterial chemoembolization (TACE) in HCC.
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