Tian Lijun, Sang Yujie, Han Bing, Sun Yujing, Li Xueyan, Feng Yuemin, Qin Chengyong, Qi Jianni
Department of Gastroenterology, Shandong Provincial Hospital, Shandong University, Jinan, 250021, China.
Department of Gastroenterology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, China.
Heliyon. 2024 Jul 18;10(14):e34704. doi: 10.1016/j.heliyon.2024.e34704. eCollection 2024 Jul 30.
The prognosis and therapeutic response of patients with liver hepatocellular carcinoma (LIHC) can be predicted based on programmed cell death (PCD) as PCD plays a crucial role during tumor progression. We developed a PCD-related gene signature to evaluate the therapeutic response and prognosis for patients with LIHC.
Molecular subtypes of LIHC were classified using ConsensusClusterPlus according to the gene biomarkers related to PCD. To predict the prognosis of high- and low-risk LIHC patients, a risk model was established by LASSO regression analysis based on the prognostic genes. Functional enrichment analysis was conducted using clusterProfiler package, and relative abundance of immune cells was quantified applying CIBERSORT package. Finally, to determine drug sensitivity, oncoPredict package was employed.
PCD was correlated with the clinicopathologic features of LIHC. Then, we defined four molecular subtypes (C1-C4) of LIHC using PCD-related prognostic genes. Specifically, subtype C1 had the worst prognosis with enriched T cells regulatory (Tregs) and Macrophage_M0 and higher expression of T cell exhaustion markers, meanwhile, C1 also had a relatively higher TIDE score and metastasis potential. A risk model was established using 5 prognostic genes. High-risk patients tended to have higher expression of T cell exhaustion markers and TIDE score and unfavorable outcomes, and they were more sensitive to small molecule drug 5.Fluorouracil.
A PCD-related gene signature was developed and verified to be able to accurately predict the prognosis and drug sensitivity of LIHC patients.
肝细胞癌（LIHC）患者的预后和治疗反应可基于程序性细胞死亡（PCD）进行预测，因为PCD在肿瘤进展过程中起着关键作用。我们开发了一种与PCD相关的基因特征来评估LIHC患者的治疗反应和预后。
根据与PCD相关的基因生物标志物，使用ConsensusClusterPlus对LIHC的分子亚型进行分类。为了预测高危和低危LIHC患者的预后，基于预后基因通过LASSO回归分析建立了风险模型。使用clusterProfiler软件包进行功能富集分析，并应用CIBERSORT软件包定量免疫细胞的相对丰度。最后，为了确定药物敏感性，使用了oncoPredict软件包。
PCD与LIHC的临床病理特征相关。然后，我们使用与PCD相关的预后基因定义了LIHC的四种分子亚型（C1-C4）。具体而言，C1亚型预后最差，T细胞调节性细胞（Tregs）和巨噬细胞_M0富集，T细胞耗竭标志物表达较高，同时，C1亚型的TIDE评分和转移潜能也相对较高。使用5个预后基因建立了风险模型。高危患者往往具有较高的T细胞耗竭标志物表达和TIDE评分，预后不良，并且他们对小分子药物5-氟尿嘧啶更敏感。
开发并验证了一种与PCD相关的基因特征，能够准确预测LIHC患者的预后和药物敏感性。
