Abstract
Background:
Prostate cancer (PCa) is the second most common malignant neoplasm in males and is the fifth leading cause of cancer-related mortality. Due to the use of prostate-specific antigen (PSA) screening and improved biopsy techniques, persons identified with early-stage prostate cancer often have a positive prognosis after comprehensive treatment. Nonetheless, prostate cancer is a latent illness that may present as an asymptomatic tumor in individuals aged 20-30. The overall survival (OS) of men with advanced PCa is significantly diminished. Consequently, there is an immediate want for innovative, accurate biomarkers to detect early prostate cancer.
Methods:
This research analyzed the interaction network of differentially expressed genes (DEGs) related to metabolite interconversion enzymes in PCa by gene expression microarray data, single-cell RNA sequencing, oncogenes, and tumor suppressor genes (TSGs) utilizing bioinformatics techniques. This kind of analysis has not been documented in prior studies.
Results:
We then used a dataset acquired by the Cancer Genome Atlas (TCGA) to confirm our findings. Genes including CYP3A5, PDE8B, AOX1, BNIPL, FADS2, RRM2, ALDH3B2, and GSTM2 may be significant in the diagnosis and treatment of PCa.
Conclusion:
Our objective was to provide new perspectives on the molecular properties and pathways of DEGs in PCa and to uncover potential biomarkers that play a crucial role in the genesis and progression of PCa.
Keywords:
Cell–cell communication; Metabolite interconversion enzyme; Microarray; Prostate cancer.