Wang Longxing, Zhao Zhiming, Shu Kunxian, Ma Mingyue
Chongqing Key Laboratory of Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
College of Pharmacy, Chongqing Medical University, Chongqing 400016, China.
Int J Mol Sci. 2024 Dec 26;26(1):118. doi: 10.3390/ijms26010118.
Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer with a poor prognosis. During the development of cancer cells, mitochondria influence various cell death patterns by regulating metabolic pathways such as oxidative phosphorylation. However, the relationship between mitochondrial function and cell death patterns in HCC remains unclear. In this study, we used a comprehensive machine learning framework to construct a mitochondrial functional activity-associated programmed cell death index (MPCDI) based on scRNA-seq and RNA-seq data from TCGA, GEO, and ICGC datasets. The index signature was used to classify HCC patients, and studied the multi-omics features, immune microenvironment, and drug sensitivity of the subtypes. Finally, we constructed the MPCDI signature consisting of four genes (S100A9, FYN, LGALS3, and HMOX1), which was one of the independent risk factors for the prognosis of HCC patients. The HCC patients were divided into high- and low-MPCDI groups, and the immune status was different between the two groups. Patients with a high MPCDI had higher TIDE scores and poorer responses to immunotherapy, suggesting that high-MPCDI patients might not be suitable for immunotherapy. By analyzing the drug sensitivity data of CTRP, GDSC, and PRISM databases, it was found that staurosporine has potential therapeutic significance for patients with a high MPCDI. In summary, based on the characteristics of mitochondria function and PCD patterns, we used single-cell and transcriptome data to identify four genes and construct the MPCDI signature, which provided new perspectives and directions for the clinical diagnosis and personalized treatment of HCC patients.
肝细胞癌（HCC）是一种高度异质性的癌症，预后较差。在癌细胞的发展过程中，线粒体通过调节氧化磷酸化等代谢途径影响各种细胞死亡模式。然而，HCC中线粒体功能与细胞死亡模式之间的关系仍不清楚。在本研究中，我们使用了一个综合的机器学习框架，基于来自TCGA、GEO和ICGC数据集的单细胞RNA测序（scRNA-seq）和RNA测序（RNA-seq）数据构建了一个与线粒体功能活性相关的程序性细胞死亡指数（MPCDI）。该指数特征用于对HCC患者进行分类，并研究各亚型的多组学特征、免疫微环境和药物敏感性。最后，我们构建了由四个基因（S100A9、FYN、LGALS3和HMOX1）组成的MPCDI特征，这是HCC患者预后的独立危险因素之一。将HCC患者分为高MPCDI组和低MPCDI组，两组的免疫状态不同。高MPCDI患者的TIDE评分较高，对免疫治疗的反应较差，这表明高MPCDI患者可能不适合免疫治疗。通过分析CTRP、GDSC和PRISM数据库的药物敏感性数据，发现星形孢菌素对高MPCDI患者具有潜在的治疗意义。总之，基于线粒体功能和程序性细胞死亡模式的特征，我们利用单细胞和转录组数据鉴定了四个基因并构建了MPCDI特征，为HCC患者的临床诊断和个性化治疗提供了新的视角和方向。
