Department of Ultrasound and Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Affiliated Hospital of Hebei University, Baoding, 071052, China.
Department of Hepatobiliary Surgery, Affiliated Hospital of Hebei University, Baoding, 071052, China.
PLoS One. 2024 Apr 18;19(4):e0298004. doi: 10.1371/journal.pone.0298004. eCollection 2024.
Liver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC.
The single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively. Then, we explored the association of stemness indicators (CytoTRACE score and stemness index, mRNAsi) with survival outcomes and clinical characteristics by combining clinical information and survival analyses. Subsequently, weighted co-expression network analysis (WGCNA) and Cox were applied to assess mRNAsi-related genes in bulk LIHC data and construct a prognostic model for LIHC patients. Single-sample gene-set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) analysis were employed for immune infiltration assessment. Finally, the potential immunotherapeutic response was predicted by the Tumor Immune Dysfunction and Exclusion (TIDE), and the tumor mutation burden (TMB). Additionally, pRRophetic package was applied to evaluate the sensitivity of high and low-risk groups to common chemotherapeutic drugs.
A total of four genes (including STIP1, H2AFZ, BRIX1, and TUBB) associated with stemness score (CytoTRACE score and mRNAsi) were identified and constructed a risk model that could predict prognosis in LIHC patients. It was observed that high stemness cells occurred predominantly in the late stages of LIHC and that poor overall survival in LIHC patients was also associated with high mRNAsi scores. In addition, pathway analysis confirmed the biological uniqueness of the two risk groups. Personalized treatment predictions suggest that patients with a low risk benefited more from immunotherapy, while those with a high risk group may be conducive to chemotherapeutic drugs.
The current study developed a novel prognostic risk signature with genes related to CSCs, which provides novel ideas for the diagnosis, prognosis and treatment of LIHC.
肝肝细胞癌（LIHC）是原发性肝癌的一种常见形式。研究表明，肿瘤干细胞在促进肿瘤复发、转移和治疗耐药方面发挥了作用。尽管如此，对于有效预测 LIHC 患者的预后和指导治疗策略，仍然缺乏已建立的癌症干细胞（CSC）相关基因特征。
首先使用 CytoTRACE 包和单类线性回归（OCLR）算法从公共数据集和计算机中获取单细胞 RNA 测序（scRNA-seq）和批量 RNA 转录组数据，分别评估干性水平。然后，我们通过结合临床信息和生存分析，探讨干性指标（CytoTRACE 评分和干性指数，mRNAsi）与生存结果和临床特征的关联。随后，应用加权共表达网络分析（WGCNA）和 Cox 评估批量 LIHC 数据中与 mRNAsi 相关的基因，并构建 LIHC 患者的预后模型。单样本基因集富集分析（ssGSEA）、通过估计相对 RNA 转录物子集的细胞类型鉴定（CIBERSORT）和肿瘤免疫估计资源（TIMER）分析用于免疫浸润评估。最后，通过肿瘤免疫功能障碍和排除（TIDE）和肿瘤突变负担（TMB）预测潜在的免疫治疗反应。此外，还应用 pRRophetic 包评估高风险和低风险组对常见化疗药物的敏感性。
共鉴定出 4 个与干性评分（CytoTRACE 评分和 mRNAsi）相关的基因（包括 STIP1、H2AFZ、BRIX1 和 TUBB），并构建了一个可预测 LIHC 患者预后的风险模型。观察到高干性细胞主要存在于 LIHC 的晚期，并且 LIHC 患者的总体生存不良与高 mRNAsi 评分相关。此外，通路分析证实了两个风险组的生物学独特性。个性化治疗预测表明，低风险患者从免疫治疗中获益更多，而高风险组可能有利于化疗药物。
本研究构建了一个新的与 CSC 相关基因的预后风险特征，为 LIHC 的诊断、预后和治疗提供了新的思路。
