Development and Assessment of a Novel Palmitoylation-Related lncRNA Signature for Prognosis and Immune Landscape in Hepatocellular Carcinoma.
The contribution of long non-coding RNAs (lncRNAs) associated with protein palmitoylation to the progression of hepatocellular carcinoma (HCC) remains largely unclear. This study sought to establish a prognostic signature based on palmitoylation-related lncRNAs and explore their functional implications in HCC.
RNA sequencing and clinical data for HCC and normal tissues were sourced from the Cancer Genome Atlas (TCGA). Pearson correlation analysis was used to identify lncRNAs that were co-expressed with palmitoylation-related genes. Univariate Cox regression was applied to select lncRNAs with prognostic value, followed by the construction of a predictive model using the least absolute shrinkage and selection operator (LASSO) regression. A focused analysis was performed on one key lncRNA, AC009403.1. Expression levels of the final nine lncRNAs included in the model were further validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR).
A prognostic model for HCC was developed using nine palmitoylation-associated lncRNAs: AC009403.1, AC010789.1, AC026402.2, AC107021.2, AC135050.6, AL353572.4, MKLN1-AS, PRRT3-AS1, and ZNF582-AS1. This model effectively stratified patients into high- and low-risk groups exhibiting significantly different overall survival (OS) and progression-free survival (PFS), with the low-risk group showing more favorable outcomes. The high-risk group was associated with an immunosuppressive microenvironment, higher tumor mutation burden (TMB), and increased sensitivity to certain chemotherapeutic drugs (e.g., Sorafenib). Finally, RT-qPCR validation revealed that all nine lncRNAs were significantly upregulated in HCC tissues.
The nine-lncRNA signature exhibits robust predictive power for HCC prognosis and provides novel insights into the mechanisms of lncRNA-regulated palmitoylation in HCC development.
RNA sequencing and clinical data for HCC and normal tissues were sourced from the Cancer Genome Atlas (TCGA). Pearson correlation analysis was used to identify lncRNAs that were co-expressed with palmitoylation-related genes. Univariate Cox regression was applied to select lncRNAs with prognostic value, followed by the construction of a predictive model using the least absolute shrinkage and selection operator (LASSO) regression. A focused analysis was performed on one key lncRNA, AC009403.1. Expression levels of the final nine lncRNAs included in the model were further validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR).
A prognostic model for HCC was developed using nine palmitoylation-associated lncRNAs: AC009403.1, AC010789.1, AC026402.2, AC107021.2, AC135050.6, AL353572.4, MKLN1-AS, PRRT3-AS1, and ZNF582-AS1. This model effectively stratified patients into high- and low-risk groups exhibiting significantly different overall survival (OS) and progression-free survival (PFS), with the low-risk group showing more favorable outcomes. The high-risk group was associated with an immunosuppressive microenvironment, higher tumor mutation burden (TMB), and increased sensitivity to certain chemotherapeutic drugs (e.g., Sorafenib). Finally, RT-qPCR validation revealed that all nine lncRNAs were significantly upregulated in HCC tissues.
The nine-lncRNA signature exhibits robust predictive power for HCC prognosis and provides novel insights into the mechanisms of lncRNA-regulated palmitoylation in HCC development.