Identification of pyroptosis-related lncRNA signature and AC005253.1 as a pyroptosis-related oncogene in prostate cancer - Yu, JiangFan; Tang, Rui; Li, JinYu (Frontiers in Oncology, 2022)
Background: Pyroptosis and prostate cancer (PCa) are closely related. The role of pyroptosis-related lncRNAs (PRL) in PCa remains elusive. This study aimed to explore the relationship between PRL and prostate cancer prognosis. Methods: Gene expression and clinical signatures were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A PRL risk prediction model was established by machine learning. Functional enrichment, immune status, immune checkpoints, genetic mutations, and drug susceptibility analyses related to risk scores were performed by ssGSEA, GSVA, GSEA, and CNV analysis. In addition, RT-qPCR analysis was applied to verify PRL expression in PCa cell lines. Results: Prognostic features based on six PRL (AC129507.1, AC005253.1, AC127502.2, AC068580.3, LIMD1−AS1, and LINC01852) were constructed, and patients in the high-score group had a worse prognosis than those in the low-score group. This feature was determined to be independent by Cox regression analysis, and obtained the area under the curve (AUC) of the 1-, 3-, and 5-year receiver operating characteristic (ROC) curves were 1, 0.93, and 0.92, respectively. There was a clear distinction between the immune status of the two groups. The expression of multiple immune checkpoints (HLA, CCL5, and TLR4) was also reduced in the high-score group. Gene mutations proportion in the high-score group increased, and the sensitivity to drugs increased significantly. Furthermore, RT-qPCR analysis confirmed that six PRL were upregulated in PCa cells. Conclusions: Overall, risk signatures based on six PRL were generated, providing a new perspective on PCa prognosis and setting the stage for developing individualized protocols.