Shao Jingjing, Zhao Tianye, Liu Jibin, Kang Peipei
Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University & Nantong Tumor Hospital, Nantong, China.
Department of Anesthesiology, Affiliated Tumor Hospital of Nantong University & Nantong Tumor Hospital, Nantong, China.
Front Immunol. 2024 Dec 17;15:1519324. doi: 10.3389/fimmu.2024.1519324. eCollection 2024.
Liver hepatocellular carcinoma (LIHC) ranks as the foremost cause of cancer-related deaths worldwide, and its early detection poses considerable challenges. Current prognostic indicators, including alpha-fetoprotein, have notable limitations in their clinical utility, thereby underscoring the necessity for discovering new biomarkers to improve early diagnosis and enable personalized treatment options.
This investigation employed single-cell analysis techniques to identify stem cell-associated genes and assess their prognostic significance for LIHC patients, as well as the efficacy of immunotherapy, utilizing nonnegative matrix factorization (NMF) cluster analysis. A diagnostic model for LIHC was developed and validated through multiple datasets and various machine learning clustering methods. The XGBOOST algorithm identified MRPL17 as the most significant prognostic gene among those associated with stem cells. Additionally, the research explores the relationship between MRPL17 expression and immune cell infiltration. Immunofluorescence staining of LIHC tissue samples was conducted to evaluate the expression and prognostic value of MRPL17, as well as its correlation with KI67.
Through single-cell analysis, this study identified 14 essential stem cell-related genes, highlighting their significance in the diagnosis, prognostication, and potential treatment strategies for LIHC patients. Various machine learning algorithms indicated that MRPL17 is particularly associated with patient prognosis and responses to immunotherapy. Furthermore, experimental results demonstrate that MRPL17 is upregulated in LIHC and correlates with poor prognosis, as well as positively correlating with KI67.
Cancer stem cells are pivotal in the mechanisms of immune evasion within the tumor microenvironment and have a substantial impact on treatment results. This study experimentally validated MRPL17 as a promising prognostic biomarker, emphasizing the need to target liver cancer stem cells to improve patient prognosis and enhance treatment effectiveness.
肝细胞癌(LIHC)是全球癌症相关死亡的首要原因,其早期检测面临巨大挑战。目前的预后指标,包括甲胎蛋白,在临床应用中存在显著局限性,因此凸显了发现新生物标志物以改善早期诊断并实现个性化治疗方案的必要性。
本研究采用单细胞分析技术,利用非负矩阵分解(NMF)聚类分析来识别干细胞相关基因,并评估其对LIHC患者的预后意义以及免疫治疗的疗效。通过多个数据集和各种机器学习聚类方法开发并验证了LIHC的诊断模型。XGBOOST算法确定MRPL17是与干细胞相关的最显著预后基因。此外,该研究还探讨了MRPL17表达与免疫细胞浸润之间的关系。对LIHC组织样本进行免疫荧光染色,以评估MRPL17的表达、预后价值及其与KI67的相关性。
通过单细胞分析,本研究确定了14个关键的干细胞相关基因,突出了它们在LIHC患者诊断、预后及潜在治疗策略中的重要性。各种机器学习算法表明,MRPL17与患者预后及免疫治疗反应特别相关。此外,实验结果表明,MRPL17在LIHC中上调,与不良预后相关,且与KI67呈正相关。
癌症干细胞在肿瘤微环境中的免疫逃逸机制中起关键作用,并对治疗结果有重大影响。本研究通过实验验证了MRPL17是一种有前景的预后生物标志物,强调了靶向肝癌干细胞以改善患者预后和提高治疗效果的必要性。