Table 2_Construction of a prognostic model based on palmitoylation-related lncRNAs for assessing drug benefits in breast cancer.xlsx

BackgroundThe lncRNAs associated with protein palmitoylation in breast cancer (BC) remain largely unexplored.
MethodsWe retrieved transcriptome, proteome, and mutation data from TCGA-BRCA (BC), identified 592 palmitoylation-related lncRNAs (PRLs), constructed a prognostic model (PmPRLs) based on their characteristics. According to the score of the median risk, the “High-”and “Low” risk groups were distinguished. The predictive potential of PmPRLs for the prognosis of BC was determined through Kaplan-Meier (KM) survival analysis, ROC curve analysis, and risk scoring verification using the training set and validation set. The differences of PmPRLs in different risk groups were illustrated by using gene mutation frequency, immune function, tumour immune dysfunction and rejection (TIDE) score and drug sensitivity analysis. Based on this model, key feature LncRNAs were screened out. After the identified LncRNAs were verified by the external dataset TANRIC, a series of tumour phenotypic experiments were conducted to comprehensively demonstrate their role in tumourigenesis and development.
ResultsWe identified 2 key feature lncRNAs, AC016394.2 and AC022150.4, as the most significant prognostic factors. Both of these lncRNAs exhibited high expression levels in the TCGA and TANRIC datasets and were closely associated with tumour cell growth, proliferation, and migration. More importantly, based on co-expression analysis, we proposed that AC016394.2 and AC022150.4 may respectively regulate SEC24C and ZNF611. Furthermore, these two lncRNAs enhanced the palmitoylation modification of these proteins.
ConclusionThe insights regarding the potential roles of AC016394.2 and AC022150.4 can enhance our understanding of the mechanisms towards the pathogenesis and progression of BC.