Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis

• DOI: 10.1007/s12672-024-01189-5
• Publication Date: 2024-07-29T13:01:15Z
• Authors: Caixia Dai; Xiangju Zeng; Xiuhong Zhang; Ziqi Liu; Shunhua Cheng
• Keywords: 0303 health sciences; 03 medical and health sciences; Prostate cancer; lncRNA; Progression; Bioinformatic analysis; Machine learning; Neoplasms. Tumors. Oncology. Including cancer and carcinogens; RC254-282; Analysis

Abstract Prostate cancer remains a complex and challenging disease, necessitating innovative approaches for prognosis therapeutic guidance. This study integrates machine learning techniques to develop novel mitophagy-related long non-coding RNA (lncRNA) signature predicting the progression of prostate cancer. Leveraging TCGA-PRAD dataset, we identify set four key lncRNAs formulate riskscore, revealing its potential as prognostic indicator. Subsequent analyses unravel intricate connections between immune cell infiltration, mutational landscapes, treatment outcomes. Notably, pan-cancer exploration YEATS2-AS1 highlights pervasive impact, demonstrating elevated expression across various malignancies. Furthermore, drug sensitivity predictions based on riskscore guide personalized chemotherapy strategies, with drugs like Carmustine Entinostat showing distinct suitability high low-risk group patients. Regression analysis exposes significant correlations lncRNAs, genes. Molecular docking reveal promising interactions Cyclophosphamide proteins encoded by these genes, suggesting avenues. comprehensive not only introduces robust tool but also provides valuable insights into molecular intricacies interventions in cancer, paving way more effective clinical approaches.