Feng Tingze, Wu Tianzhi, Zhang Yanxia, Zhou Lang, Liu Shanshan, Li Lin, Li Ming, Hu Erqiang, Wang Qianwen, Fu Xiaocong, Zhan Li, Xie Zijing, Xie Wenqin, Huang Xianying, Shang Xuan, Yu Guangchuang
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Department of Medical Genetics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.
Front Oncol. 2022 Jul 22;12:912694. doi: 10.3389/fonc.2022.912694. eCollection 2022.
Hepatocellular carcinoma (HCC) stem cells are regarded as an important part of individualized HCC treatment and sorafenib resistance. However, there is lacking systematic assessment of stem-like indices and associations with a response of sorafenib in HCC. Our study thus aimed to evaluate the status of tumor dedifferentiation for HCC and further identify the regulatory mechanisms under the condition of resistance to sorafenib. Datasets of HCC, including messenger RNAs (mRNAs) expression, somatic mutation, and clinical information were collected. The mRNA expression-based stemness index (mRNAsi), which can represent degrees of dedifferentiation of HCC samples, was calculated to predict drug response of sorafenib therapy and prognosis. Next, unsupervised cluster analysis was conducted to distinguish mRNAsi-based subgroups, and gene/geneset functional enrichment analysis was employed to identify key sorafenib resistance-related pathways. In addition, we analyzed and confirmed the regulation of key genes discovered in this study by combining other omics data. Finally, Luciferase reporter assays were performed to validate their regulation. Our study demonstrated that the stemness index obtained from transcriptomic is a promising biomarker to predict the response of sorafenib therapy and the prognosis in HCC. We revealed the peroxisome proliferator-activated receptor signaling pathway (the PPAR signaling pathway), related to fatty acid biosynthesis, that was a potential sorafenib resistance pathway that had not been reported before. By analyzing the core regulatory genes of the PPAR signaling pathway, we identified four candidate target genes, (), (), () and (), as a signature to distinguish the response of sorafenib. We proposed and validated that the and could directly regulate and , respectively. Our results suggest that the combined use of inhibitors and sorafenib may be a promising therapeutic approach.
肝细胞癌（HCC）干细胞被视为个体化HCC治疗和索拉非尼耐药的重要组成部分。然而，目前缺乏对HCC中干细胞样指标及其与索拉非尼反应相关性的系统评估。因此，我们的研究旨在评估HCC的肿瘤去分化状态，并进一步确定索拉非尼耐药条件下的调控机制。收集了包括信使核糖核酸（mRNA）表达、体细胞突变和临床信息在内的HCC数据集。计算基于mRNA表达的干性指数（mRNAsi），其可代表HCC样本的去分化程度，以预测索拉非尼治疗的药物反应和预后。接下来，进行无监督聚类分析以区分基于mRNAsi的亚组，并采用基因/基因集功能富集分析来识别与索拉非尼耐药相关的关键途径。此外，我们通过结合其他组学数据对本研究中发现的关键基因的调控进行了分析和确认。最后，进行荧光素酶报告基因测定以验证其调控作用。我们的研究表明，从转录组学获得的干性指数是预测索拉非尼治疗反应和HCC预后的有前景的生物标志物。我们揭示了与脂肪酸生物合成相关的过氧化物酶体增殖物激活受体信号通路（PPAR信号通路），这是一条此前未被报道的潜在索拉非尼耐药途径。通过分析PPAR信号通路的核心调控基因，我们确定了四个候选靶基因，即（）、（）、（）和（），作为区分索拉非尼反应的标志物。我们提出并验证了（）和（）可分别直接调控（）和（）。我们的结果表明，联合使用（）抑制剂和索拉非尼可能是一种有前景的治疗方法。
