Li Lixing, Shen Lu, Ma Jingsong, Zhou Qiang, Li Mo, Wu Hao, Wei Muyun, Zhang Di, Wang Ting, Qin Shengying, Xing Tonghai
Department of General Surgery, School of Medicine, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China.
Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University, Shanghai, China.
Front Med (Lausanne). 2020 Sep 17;7:451. doi: 10.3389/fmed.2020.00451. eCollection 2020.
Hepatocellular carcinoma (HCC) is a commonly diagnosed cancer with high mortality rates. The immune response plays an important role in the progression of HCC. Immunotherapies are becoming an increasingly promising tool for treating cancers. Advancements in scRNA-seq (single-cell RNA sequencing) have allowed us to identify new subsets in the immune microenvironment of HCC. Yet, distribution of these new cell types and their potential prognostic value in bulk samples from large cohorts remained unclear. This study aimed to investigate the tumor-infiltration and prognostic value of new cell subsets identified by a previous scRNA-seq study in a TCGA HCC cohort using CIBERSORTx, a machine learning method to estimate cell proportion and infer cell-type-specific gene expression profiles. We observed different distributions of tumor-infiltrating lymphocytes between tumor and normal cells. Among these, the CD4-GZMA cell subset showed association with prognosis (log-rank test, < 0.05). We further analyzed CD4-GZMA cell specific gene expression with CIBERSORTx, and found 19 prognostic genes (univariable cox regression, < 0.05). Finally, we applied Least absolute shrinkage and selection operator (LASSO) Cox regression to construct an immune risk score model and performed a prognostic assessment of our model in TCGA and ICGC cohorts. Taken together, the immune landscape in HCC bulk samples may be more complex than assumed, with heterogeneity and different tumor-infiltration relative to scRNA-seq results. Additionally, CD4-GZMA cells and their characteristics may yield therapeutic benefits in the immune treatment of HCC.
肝细胞癌（HCC）是一种常见的诊断癌症，死亡率很高。免疫反应在HCC的进展中起着重要作用。免疫疗法正成为治疗癌症越来越有前景的工具。单细胞RNA测序（scRNA-seq）的进展使我们能够在HCC的免疫微环境中识别新的亚群。然而，这些新细胞类型在来自大型队列的大量样本中的分布及其潜在的预后价值仍不清楚。本研究旨在使用CIBERSORTx（一种估计细胞比例和推断细胞类型特异性基因表达谱的机器学习方法），研究先前scRNA-seq研究在TCGA HCC队列中鉴定的新细胞亚群的肿瘤浸润和预后价值。我们观察到肿瘤细胞和正常细胞之间肿瘤浸润淋巴细胞的分布不同。其中，CD4-GZMA细胞亚群与预后相关（对数秩检验，<0.05）。我们进一步用CIBERSORTx分析了CD4-GZMA细胞特异性基因表达，发现了19个预后基因（单变量cox回归，<0.05）。最后，我们应用最小绝对收缩和选择算子（LASSO）Cox回归构建免疫风险评分模型，并在TCGA和ICGC队列中对我们的模型进行预后评估。综上所述，HCC大量样本中的免疫格局可能比假设的更复杂，相对于scRNA-seq结果存在异质性和不同的肿瘤浸润。此外，CD4-GZMA细胞及其特征可能在HCC的免疫治疗中产生治疗益处。
