Biochemical recurrence (BCR) is common in prostate cancer (PCa), and patients with BCR usually have a poor prognosis. Cuproptosis is a unique type of cell death, and copper homeostasis is crucial to the occurrence and development of malignancies. The present study aimed to explore the prognostic value of cuproptosis-related long non-coding RNAs (lncRNAs; CRLs) in PCa and to develop a predictive signature for forecasting BCR in patients with PCa. Using The Cancer Genome Atlas database, transcriptomic, mutation and clinical data were collected from patients with PCa. A total of 121 CRLs were identified using Pearson's correlation coefficient. Subsequently, a 6-CRL signature consisting of AC087276.2, CNNM3-DT, AC090198.1, AC138207.5, METTL14-DT and LINC01515 was created to predict the BCR of patients with PCa through Cox and least absolute shrinkage and selection operator regression analyses. Kaplan-Meier curve analysis demonstrated that high-risk patients had a low BCR-free survival rate. In addition, there was a substantial difference between the high- and low-risk groups in the immune microenvironment, immune therapy, drug sensitivity and tumor mutational burden. A nomogram integrating the Gleason score, 6-CRL signature and clinical T-stage was established and evaluated. Finally, the expression of signature lncRNAs in PCa cells was verified through reverse transcription-quantitative PCR. In conclusion, the 6-CRL signature may be a potential tool for making predictions regarding BCR in patients with PCa, and the prognostic nomogram may be considered a practical tool for clinical decision-making.
Keyphrases
- poor prognosis
- long non coding rna
- prostate cancer
- acute lymphoblastic leukemia
- chronic myeloid leukemia
- tyrosine kinase
- free survival
- radical prostatectomy
- cell death
- decision making
- end stage renal disease
- induced apoptosis
- lymph node metastasis
- chronic kidney disease
- ejection fraction
- single cell
- newly diagnosed
- papillary thyroid
- squamous cell carcinoma
- cell cycle arrest
- oxidative stress
- electronic health record
- rna seq
- long noncoding rna
- machine learning
- adverse drug
- endoplasmic reticulum stress
- big data
- mass spectrometry
- magnetic resonance imaging
- drug induced
- data analysis
- network analysis
- transcription factor