Chen Chen, Wang Shunyi, Tang Yuhong, Liu Huanxiang, Tu Daoyuan, Su Bingbing, Peng Rui, Jin Shengjie, Jiang Guoqing, Cao Jun, Zhang Chi, Bai Dousheng
Department of Hepatobiliary Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, China.
Department of Hepatobiliary Surgery, Northern Jiangsu People's Hospital, Yangzhou, China.
Transl Cancer Res. 2024 Aug 31;13(8):4257-4277. doi: 10.21037/tcr-24-521. Epub 2024 Aug 22.
Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction.
Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts.
We developed a prognostic model predicated on the expression profiles of eight signature genes, namely , , , , , , , and , aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions.
This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.
肝细胞癌（HCC）仍然是全球最致命的癌症之一。晚期HCC患者预后往往较差，生存期缩短。最近，批量RNA测序数据已被用于发现各种癌症的预后标志物。然而，它们在精确识别肿瘤细胞内的核心分子和细胞活动方面存在不足。在我们目前的研究中，我们将批量RNA测序（bulk RNA-seq）数据与单细胞RNA测序（scRNA-seq）相结合，以开发一种HCC预后模型。我们研究的目标是发现新的生物标志物并提高HCC预后预测的准确性。
将单细胞测序数据与转录组学相结合，以识别与HCC进展相关的上皮-间质转化（EMT）相关基因（ERGs），并阐明其临床意义。利用来自核心细胞和ERGs的标记基因，我们通过单变量Cox分析构建了一个预后模型，探索了多种算法组合，并通过多变量Cox分析进一步优化。此外，我们深入研究了高风险和低风险患者队列在临床病理特征、免疫微环境组成、免疫检查点表达和化疗药物敏感性谱方面的差异。
我们基于八个特征基因的表达谱开发了一个预后模型，即 、 、 、 、 、 、 和 ，旨在预测总生存期（OS）结果。值得注意的是，高风险评分的患者表现出OS率降低、III-IV期疾病频率增加、肿瘤突变负担（TMB）增加、免疫细胞浸润增加以及对免疫治疗干预反应降低的倾向。
本研究通过整合scRNA-seq和批量RNA-seq数据，提出了一种预测HCC患者生存的新型预后模型。风险评分是一个有前景的独立预后因素，与免疫微环境和临床病理特征相关。它为预测预后提供了新的临床工具，并有助于未来对HCC发病机制的研究。
