Novel cuproptosis-related long non-coding RNA signature to predict prognosis in prostate carcinoma

Abstract

Background Cuproptosis, an emerging form of programmed cell death, has recently been identified. However, the association between cuproptosis-related long non-coding RNA (lncRNA) signature and the prognosis in prostate carcinoma remains elusive. This study aims to develop the novel cuproptosis-related lncRNA signature in prostate cancer and explore its latent molecular function. Methods RNA-seq data and clinical information were downloaded from the TCGA datasets. Then, cuproptosis-related gene was identified from the previous literature and further applied to screen the cuproptosis-related differentially expressed lncRNAs. Patients were randomly assigned to the training cohort or the validation cohort with a 1:1 ratio. Subsequently, the machine learning algorithms (Lasso and stepwise Cox (direction = both)) were used to construct a novel prognostic signature in the training cohorts, which was validated by the validation and the entire TCGA cohorts. The nomogram base on the lncRNA signature and several clinicopathological traits were constructed to predict the prognosis. Functional enrichment and immune analysis were performed to evaluate its potential mechanism. Furthermore, differences in the landscape of gene mutation, tumour mutational burden (TMB), microsatellite instability (MSI), drug sensitivity between both risk groups were also assessed to explicit their relationships. Results The cuproptosis-related lncRNA signature was constructed based on the differentially expressed cuproptosis-related lncRNAs, including AC005790.1, AC011472.4, AC099791.2, AC144450.1, LIPE-AS1, and STPG3-AS1. Kaplan–Meier survival and ROC curves demonstrate that the prognosis signature as an independent risk indicator had excellent potential to predict the prognosis in prostate cancer. The signature was closely associated with age, T stage, N stage, and the Gleason score. Immune analysis shows that the high-risk group was in an immunosuppressive microenvironment. Additionally, the significant difference in landscape of gene mutation, tumour mutational burden, microsatellite instability, and drug sensitivity between both risk groups was observed. Conclusions A novel cuproptosis-related lncRNA signature was constructed using machine learning algorithms to predict the prognosis of prostate cancer. It was closely with associated with several common clinical traits, immune cell infiltration, immune-related functions, immune checkpoints, gene mutation, TMB, MSI, and the drug sensitivity, which may be useful to improve the clinical outcome.

Identification of novel molecular subtypes and a signature to predict prognosis and therapeutic response based on cuproptosis-related genes in prostate cancer

“…However, in PCa, studies related to cuproptosis are still in the preliminary stage and most of them have focused on cuproptosis-related long non-coding RNA (lncRNA). Several studies have now reported that cuproptosis-related lncRNA have a better prognostic role in predicting PCa ( 43 , 44 ). However, studies on cuproptosis-related coding genes in PCa are rarely reported, and the role of cuproptosis in PCa remains unknown.…”

Crafting a Personalized Prognostic Model for Malignant Prostate Cancer Patients Using Risk Gene Signatures Discovered through TCGA-PRAD Mining, Machine Learning, and Single-Cell RNA-Sequencing

“…The hallmarks of this tumor were characterized by uncontrolled cell proliferation [ 24 , 25 ], making the establishment of prognostic models based on molecular markers of programmed cell death and physiological process a hot topic in current risk model explorations. For example, predicting models based on crucial regulatory molecules of cuproptosis [ 26 ], steroid hormone biosynthesis [ 27 ], and m6A methylation [ 28 ] are well established, and their correlation with immune cell infiltration can also be analyzed. Disulfidptosis is a newly discovered form of programmed cell death, which is characterized by the inhibition of intracellular disulfide reductants and the significant enhancement of cell death caused by SLC7A11 under glucose starvation conditions, in contrast to the significant increase in cell death induced by thiol oxidation reagents such as diamide [ 29 ].…”

Cuproptosis in cancer: biological implications and therapeutic opportunities

“…Contrarily, in glioma, FDX1 expression levels were upregulated compared with normal tissues. FDX1 knockdown notably hindered aerobic glycolysis and glioma cell proliferation [134]. Li et al further highlighted that the FDX1 expression is promoted by the c-Myc-YTHDF1 signaling pathway in glioma cells and demonstrated that the c-Myc-YTHDF1/FDX1 axis inhibited the mitophagy and promoted the malignant phenotype of glioma cells [161].…”