ArticleScientific reports2025
Machine learning-based identification of co-expressed genes in prostate cancer and CRPC and construction of prognostic models.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Machine learning approaches for predicting progression in hormone-sensitive prostate cancer patients.Frontiers in oncology · 2026Article
- Unravelling TPX2-centered co-expression networks as key drivers of aggressive prostate cancer.Scientific reports · 2025Article
- KIF4A in disease pathogenesis and therapeutics: from molecular mechanisms to clinical translation.Biology direct · 2025Review
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Spotlight onGenes · 2025Article
- Artificial intelligence driven platform for rapid catalytic performance assessment of nanozymes.Scientific reports · 2025Article
- Transcriptomic validation and clinical translation of CCDC78 as a prognostic biomarker in colorectal cancer.American journal of cancer research · 2025Article
- PLZF promotes compensatory lung growth by increasing HPMEC proliferation and angiogenesis.PloS one · 2025Article
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Authors and funding
8 authors.
Funding
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Abstract
The objective of this study was to employ machine learning to identify shared differentially expressed genes (DEGs) in prostate cancer (PCa) initiation and castration resistance, aiming to establish a robust prognostic model and enhance understanding of patient prognosis for personalized treatment strategies. mRNA transcriptome data associated with Castration-Resistant Prostate Cancer (CRPC) were obtained from the GEO database. Differential expression analysis was conducted using the limma R package to compare normal prostate samples with PCa samples, and PCa samples with CRPC samples. Next, we applied LASSO regression, univariate, and multivariate COX regression analyses to pinpoint genes linked to prognosis and build prognostic models. Validation was performed using the TCGA_PRAD dataset to confirm expression differences of hub genes and explore their correlation with clinical variables and prognostic significance. We successfully established a prostate cancer risk prognostic model containing seven genes (KIF4A, UBE2C, FAM72D, CCDC78, HOXD9, LIX1 and SLC5A8) and verified its accuracy on an independent data set. The results of calibration curve and decision curve show that the model has potential clinical application value. The nomogram can accurately predict the prognosis of patients. Additionally, elevated expression of KIF4A, UBE2C, and FAM72D, or reduced expression of LIX1, correlated with advanced pathological T and N stages, clinical T stage, prostate-specific antigen (PSA) level, age at diagnosis, Gleason score, and shorter progression-free interval (PFI) (P < 0.05). By integrating bioinformatics analysis and clinical data, we not only established a reliable prognostic model for prostate cancer but also identified key genes pivotal in disease progression and treatment resistance. These findings provide novel insights and methodologies for assessing prognosis and tailoring treatment strategies for prostate cancer patients.
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