Regulatory Role of Fatty Acid Metabolism-Related Long Noncoding RNA in Prostate Cancer: A Computational Biology Study Analysis

Abstract

In elderly men, prostate cancer is a leading cause of death. Tumor cells require more energy to progress than normal cells, and this energy is mainly dependent on the large amount of ATP support generated by lipid metabolism. Therefore, in this study, we focused on long noncoding RNAs related to lipid metabolism in prostate cancer to discover the biological mechanisms of lipid metabolism regulation. The TCGA-PRAD cohort was used in this study for computational biology analysis. In lipid metabolism biological pathways, 1959 long noncoding RNAs were identified by Pearson correlation coefficient analysis of protein-coding genes, then univariate regression with $P$ values fewer than 0.05. We further identified 784 lncRNAs that were lipid metabolism-related lncRNAs considered to have prognostic value for disease-free survival. Subsequently, we constructed two lncRNA expression patterns of lipid metabolism based on these lncRNAs by nonnegative matrix dimensionality reduction. These two expression patterns showed significant differences in disease-free survival curves for those diagnosed with prostate cancer. We found significant differences in mRNA surveillance pathway and mRNA processing between C1 and C2 groups based on the WGCNA method to explore the biological characteristics of these two expression patterns. Finally, we constructed a disease-free survival (PFS) model based on these lncRNAs. The results identified lncRNAs involved in lipid metabolism and revealed differences in their expression patterns. Additionally, the results offer candidate ideas and approaches concerning the precision treatment of prostate cancer by studying lipid metabolism by candidate long noncoding RNAs.