Aberrant autophagy could promote cancer cells to survive and proliferate in prostate cancer (PCa). LncRNAs play key roles in autophagy regulatory network. We established a prognostic model, which autophagy-related lncRNAs (au-lncRNAs) were used as biomarkers to predict prognosis of individuals with PCa. Depending on au-lncRNAs from the Cancer Genome Atlas and the Human Autophagy Database, a risk score model was created. To evaluate the prediction accuracy, the calibration, Kaplan-Meier, and receiver operating characteristic curves were used. To clarify the biological function, gene set enrichment analyses (GSEA) were performed. Quantitative real-time PCR (qRT-PCR) was employed to determine the au-lncRNAs expression in PCa cell lines and healthy prostate cells for further confirmation. We identified five au-lncRNAs with prognostic significance (AC068580.6, AF131215.2, LINC00996, LINC01125 and LINC01547). The development of a risk scoring model required the utilization of multivariate Cox analysis. According to the model, we categorized PCa individuals into low- and high-risk cohorts. PCa subjects in the high-risk group had a worse disease-free survival rate than those in the low-risk group. The 1-, 3-, and 5-year periods had corresponding areas under curves (AUC) of 0.788, 0.794, and 0.818. The prognosis of individuals with PCa could be predicted by the model with accuracy. Further analysis with GSEA showed that the prognostic model was associated with the tumor microenvironment, including immunotherapy, cancer-related inflammation, and metabolic reprogramming. Four lncRNAs expression in PCa cell lines was greater than that in healthy prostate cells. The au-lncRNA prognostic model has significant clinical implications in prognosis of PCa patient.
Keywords: Long non-coding RNA; autophagy; bioinformatics; biomarkers; prognostic model; prostate cancer; tumor microenvironment.
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