Identification of a telomere-aging-related gene as a novel prognostic factor for intrahepatic cholangiocarcinoma: machine learning-aided biomarker discovery and experimental verification

Key Points

• To identify a telomere-aging-related gene serving as a prognostic factor for intrahepatic cholangiocarcinoma.
• Used machine learning for biomarker discovery
• Evaluated expression levels of BET1L, RAD50, ANXA1, and AURKA in ICC
• Conducted experimental verification on HUCCT1 cells
• High expression of BET1L correlates with poor prognosis in ICC patients
• Prognostic model shows high accuracy
• Low expression of BET1L reduces proliferation of HUCCT1 cells