Tang Linlin, Jin Yangli, Wang Jinxu, Lu Xiuyan, Xu Mengque, Xiang Mingwei
Department of Gastroenterology, Zhuji People's Hospital, Shaoxing, China.
Department of Ultrasound, Ningbo Yinzhou No.2 Hospital, Ningbo, Zhejiang, China.
Discov Oncol. 2024 Nov 18;15(1):671. doi: 10.1007/s12672-024-01558-0.
Ferroptosis and inflammation are involved in cancer progression. The aim of this study was to identify inflammation-associated ferroptosis regulators in hepatocellular carcinoma (HCC).
FerrDb database was searched for ferroptosis-related genes. RNA sequencing data and clinicopathologic information of HCC patients were downloaded from the Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis was applied to obtain the genes probably involved in inflammation-associated ferroptosis. Univariate Cox regression analysis was conducted to screen prognostic genes, and 10 machine learning algorithms were combined to find the optimal strategy to evaluate the prognosis of the patients based on the prognosis-related genes. The patients were divided into high risk group and low risk group, and the differentially expressed genes were obtained. Thymosin beta 4 X-linked (TMSB4X) was overexpressed or knocked down in HCC cell lines, and then qPCR, CCK-8, Transwell, flow cytometery assays were performed to detect the change of HCC cells' phenotypes, and Western blot was used to detect the change of ferroptosis markers.
157 genes related to inflammation and ferroptosis in HCC were obtained by WGCNA. rLasso algorithm, with the highest C-index, screened out 29 hub genes, and this model showed good efficacy to predict the prognosis of HCC patients. The patients in high risk group and low risk groups showed distinct molecular characteristics. TMSB4X was the most important gene which dominated the classification, and it was highly expressed in HCC samples. TMSB4X promoted the viability, migration and invasion, and repressed ferroptosis of HCC cells.
The risk model constructed based on the inflammation-associated ferroptosis regulators is effective to predict the clinical outcome of HCC patients. TMSB4X, involved in inflammation-associated ferroptosis, is a potential biomarker and therapeutic target for HCC.
铁死亡和炎症参与癌症进展。本研究旨在鉴定肝细胞癌（HCC）中与炎症相关的铁死亡调节因子。
在FerrDb数据库中搜索铁死亡相关基因。从癌症基因组图谱（TCGA）数据库下载HCC患者的RNA测序数据和临床病理信息。应用加权基因共表达网络分析来获取可能参与炎症相关铁死亡的基因。进行单因素Cox回归分析以筛选预后基因，并结合10种机器学习算法找到基于预后相关基因评估患者预后的最佳策略。将患者分为高风险组和低风险组，获得差异表达基因。在HCC细胞系中过表达或敲低胸腺素β4 X连锁（TMSB4X），然后进行qPCR、CCK-8、Transwell、流式细胞术检测以检测HCC细胞表型的变化，并用蛋白质免疫印迹法检测铁死亡标志物的变化。
通过WGCNA获得了157个与HCC炎症和铁死亡相关的基因。C指数最高的rLasso算法筛选出29个核心基因，该模型对预测HCC患者的预后显示出良好的效果。高风险组和低风险组患者表现出明显的分子特征。TMSB4X是主导分类的最重要基因，在HCC样本中高表达。TMSB4X促进HCC细胞的活力、迁移和侵袭，并抑制铁死亡。
基于炎症相关铁死亡调节因子构建的风险模型对预测HCC患者的临床结局有效。参与炎症相关铁死亡的TMSB4X是HCC的潜在生物标志物和治疗靶点。
