ArticleDiscover oncology2024
Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis.
Article in Discover oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Evaluation of lncRNA PVT1 rs13255292 variant and serum E-cadherin levels in breast cancer.Journal, genetic engineering & biotechnology · 2026Article
- NLRX1 Drives Prostate Cancer Progression Through Activation of AKT and ERK Signaling Pathways.International journal of biological sciences · 2026Article
- Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies.Cell communication and signaling : CCS · 2025Review
- Artificial intelligence in prostate cancer.Chinese medical journal · 2025Review
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Authors and funding
5 authors.
Funding
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Abstract
Prostate cancer remains a complex and challenging disease, necessitating innovative approaches for prognosis and therapeutic guidance. This study integrates machine learning techniques to develop a novel mitophagy-related long non-coding RNA (lncRNA) signature for predicting the progression of prostate cancer. Leveraging the TCGA-PRAD dataset, we identify a set of four key lncRNAs and formulate a riskscore, revealing its potential as a prognostic indicator. Subsequent analyses unravel the intricate connections between riskscore, immune cell infiltration, mutational landscapes, and treatment outcomes. Notably, the pan-cancer exploration of YEATS2-AS1 highlights its pervasive impact, demonstrating elevated expression across various malignancies. Furthermore, drug sensitivity predictions based on riskscore guide personalized chemotherapy strategies, with drugs like Carmustine and Entinostat showing distinct suitability for high and low-risk group patients. Regression analysis exposes significant correlations between the mitophagy-related lncRNAs, riskscore, and key mitophagy-related genes. Molecular docking analyses reveal promising interactions between Cyclophosphamide and proteins encoded by these genes, suggesting potential therapeutic avenues. This comprehensive study not only introduces a robust prognostic tool but also provides valuable insights into the molecular intricacies and potential therapeutic interventions in prostate cancer, paving the way for more personalized and effective clinical approaches.
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