Abstract：
BACKGROUND: This study conducted molecular subtyping of biliary tract cancer patients based on 19 PANoptosis-related gene signatures.
METHODS: Through consensus clustering, patients were categorized into two subtypes, A and B. By integrating multi-omics data and clinical information from different cohorts, we elucidated the association between different subtypes of biliary tract cancer and patient prognosis, which correlated with the immune infiltration characteristics of patients.
RESULTS: LASSO regression analysis was performed on the 19 gene signatures, and we constructed and validated a 9-gene risk score prognostic model that accurately predicts the overall survival rate of different biliary tract cancer patients. Additionally, we developed a predictive nomogram demonstrating the clinical utility and robustness of our model. Further analysis of the risk score-based immune landscape highlighted potential associations with immune cell infiltration, chemotherapy, and immune therapy response.
CONCLUSIONS: Our study provides valuable insights into personalized treatment strategies for biliary tract cancer, which are crucial for improving patient prognosis and guiding treatment decisions in clinical practice.
METHODS: Through consensus clustering, patients were categorized into two subtypes, A and B. By integrating multi-omics data and clinical information from different cohorts, we elucidated the association between different subtypes of biliary tract cancer and patient prognosis, which correlated with the immune infiltration characteristics of patients.
RESULTS: LASSO regression analysis was performed on the 19 gene signatures, and we constructed and validated a 9-gene risk score prognostic model that accurately predicts the overall survival rate of different biliary tract cancer patients. Additionally, we developed a predictive nomogram demonstrating the clinical utility and robustness of our model. Further analysis of the risk score-based immune landscape highlighted potential associations with immune cell infiltration, chemotherapy, and immune therapy response.
CONCLUSIONS: Our study provides valuable insights into personalized treatment strategies for biliary tract cancer, which are crucial for improving patient prognosis and guiding treatment decisions in clinical practice.
摘要：
背景：这项研究基于19个PANoptesis相关基因特征对胆道癌症患者进行了分子分型。
方法：通过共识聚类，患者分为两种亚型，A和B.通过整合来自不同队列的多组数据和临床信息，我们阐明了胆道癌的不同亚型与患者预后之间的关系，与患者的免疫浸润特征有关。
结果：对19个基因特征进行了LASSO回归分析，我们构建并验证了一个9基因风险评分预后模型，该模型可以准确预测不同胆道肿瘤患者的总体生存率。此外,我们开发了一个预测列线图,证明了我们模型的临床实用性和稳健性.基于风险评分的免疫景观的进一步分析突出了与免疫细胞浸润的潜在关联，化疗,和免疫治疗反应。
结论：我们的研究为胆道癌的个性化治疗策略提供了有价值的见解，这对于改善患者预后和指导临床实践中的治疗决策至关重要。
方法：通过共识聚类，患者分为两种亚型，A和B.通过整合来自不同队列的多组数据和临床信息，我们阐明了胆道癌的不同亚型与患者预后之间的关系，与患者的免疫浸润特征有关。
结果：对19个基因特征进行了LASSO回归分析，我们构建并验证了一个9基因风险评分预后模型，该模型可以准确预测不同胆道肿瘤患者的总体生存率。此外,我们开发了一个预测列线图,证明了我们模型的临床实用性和稳健性.基于风险评分的免疫景观的进一步分析突出了与免疫细胞浸润的潜在关联，化疗,和免疫治疗反应。
结论：我们的研究为胆道癌的个性化治疗策略提供了有价值的见解，这对于改善患者预后和指导临床实践中的治疗决策至关重要。
