Construction of a prognostic model based on palmitoylation-related lncRNAs for assessing drug benefits in breast cancer.
Wang. Yan Y; Zhang. Mengsi M; Zhou. Yuqin Y; Li. Zaozhuo Z; Yi. Xinglin X; Ren. Lin L; Zhang. Yi Y
Key Findings
- A prognostic model based on 592 palmitoylation‑related lncRNAs can separate breast‑cancer patients into high‑ and low‑risk groups.
- Two lncRNAs, AC016394.2 and AC022150.4, are consistently over‑expressed in breast‑cancer datasets and correlate with tumor growth and migration.
- These lncRNAs may regulate proteins SEC24C and ZNF611 and increase their palmitoylation, influencing cancer progression.
Practical Outcomes
- For the biohacker community, the study offers no direct, actionable guidance on using palmitoyl‑dipeptide‑6 or related interventions for health optimization. It is primarily a cancer‑research tool for prognosis and does not translate into protocols for longevity, metabolism, or performance.
Summary
Scientists built a gene‑expression model that predicts breast‑cancer outcomes by looking at long non‑coding RNAs linked to a protein modification called palmitoylation. They found two RNAs, AC016394.2 and AC022150.4, that are high in tumors and seem to help cancer cells grow and spread, possibly by boosting palmitoylation of certain proteins.
Abstract
The lncRNAs associated with protein palmitoylation in breast cancer (BC) remain largely unexplored. We 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. We 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. The 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.
Study Information
pubmed
2025
2025-10-27T00:00:00.000Z
10.3389/fimmu.2025.1656593
16