METHODS: We conducted bioinformatics analysis to identify PRGs in The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) patients. Consensus clustering classified patients into different subtypes. We used LASSO regression to established a pyroptosis subtype-related score (PSRS) related to prognosis. OncoPredict identified potential pharmaceuticals based on PSRS.
RESULTS: We found 20 HCC-related PRGs in 335 TCGA-LIHC patients. Consensus clustering classified patients into two subtypes. Subtype I had better overall survival and higher response to anti-PD1 treatment. The prognostic model involving 20 genes predicted poorer prognosis for high-PSRS group. The model was validated in two external cohorts. OncoPredict identified 65 potential pharmaceuticals based on PSRS.
CONCLUSIONS: Our investigation revealed a correlation between pyroptosis and HCC. We established PSRS as independent risk factors for predicting prognosis. The study paves the way for using PRGs as prognostic biomarkers and exploring personalized therapy for HCC.
方法:我们进行了生物信息学分析,以鉴定癌症基因组图谱-肝细胞癌(TCGA-LIHC)患者中的PRG。共识聚类将患者分为不同的亚型。我们使用LASSO回归建立与预后相关的焦亡亚型相关评分(PSRS)。OncoPredict基于PSRS确定了潜在的药物。
结果:我们在335例TCGA-LIHC患者中发现20例HCC相关PRG。共识聚类将患者分为两种亚型。I亚型具有更好的总生存率和更高的抗PD1治疗反应。涉及20个基因的预后模型预测高PSRS组的预后较差。该模型在两个外部队列中进行了验证。OncoPredict根据PSRS确定了65种潜在药物。
结论:我们的研究揭示了焦亡与HCC之间的相关性。我们将PSRS作为预测预后的独立危险因素。该研究为使用PRGs作为预后生物标志物和探索肝癌个性化治疗铺平了道路。