Liu Xingchen, Pan Bo, Ding Jie, Zhai Xiaofeng, Hong Jing, Zheng Jianming
Department of Pathology, The First Affiliated Hospital of Naval Medical University, Shanghai, 200433, China.
Department of Integrative Oncology, The First Affiliated Hospital of Naval Medical University, Shanghai, 200433, China.
Immunol Res. 2025 Feb 4;73(1):46. doi: 10.1007/s12026-024-09585-3.
Hepatocellular carcinoma (HCC) is a malignant tumor regulated by the immune system. Immunotherapy using checkpoint inhibitors has shown encouraging outcomes in a subset of HCC patients. The main challenges in checkpoint immunotherapy for HCC are to expand treatment options and to broaden the beneficiary population. Therefore, the search for potential signatures of immune cells is meaningful in the development of immunotherapy for HCC. The HCC related datasets were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Differential expression analysis and functional analysis were performed first. Then support vector machine-recursive feature elimination (SVM-RFE), random forests (RF), least absolute shrinkage and selection operation (LASSO), and weighed gene co-expression network analysis (WGCNA) were employed to screen for critical genes, and receiver operating characteristic (ROC) analysis was performed to compare diagnostic performance. Subsequently, single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between signatures and immune cells. Finally, we validated the expression of these biomarkers in human HCC samples. 531 overlapping differentially expressed genes (DEGs) were identified. Furthermore, enrichment analysis revealed pathways associated with immune activation processes, immune cell involvement and inflammatory signaling. After using multiple machine-learning strategies, extracellular matrix protein 1 (ECM1), leukemia inhibitory factor receptor (LIFR), sushi repeat containing protein X-linked (SRPX), and thromboxane A2 receptor (TBXA2R) were identified as critical signatures, and exhibited high expression in tumor-adjacent normal tissues. According to the ssGSEA results, ECM1, LIFR, SRPX and TBXA2R were all significantly associated with diverse immune cells, such as monocytes and neutrophils. Moreover, immunostaining of human HCC samples showed that these critical signatures all colocalized with CD14-positive monocytes. Our findings report the potential signatures of immune cells in HCC and confirm that they localize in monocytes of tumor-adjacent normal tissues. ECM1, LIFR, SRPX and TBXA2R could become new potential targets for predictive diagnosis, early intervention and immunotherapy of HCC in the future.
肝细胞癌（HCC）是一种受免疫系统调节的恶性肿瘤。使用检查点抑制剂的免疫疗法在一部分HCC患者中显示出了令人鼓舞的效果。HCC检查点免疫疗法的主要挑战在于扩大治疗选择和拓宽受益人群。因此，寻找免疫细胞的潜在特征在HCC免疫疗法的发展中具有重要意义。从基因表达综合数据库（GEO）和癌症基因组图谱（TCGA）下载了与HCC相关的数据集。首先进行差异表达分析和功能分析。然后采用支持向量机递归特征消除（SVM-RFE）、随机森林（RF）、最小绝对收缩和选择算子（LASSO）以及加权基因共表达网络分析（WGCNA）来筛选关键基因，并进行受试者工作特征（ROC）分析以比较诊断性能。随后，使用单样本基因集富集分析（ssGSEA）来探索特征与免疫细胞之间的关系。最后，我们在人HCC样本中验证了这些生物标志物的表达。共鉴定出531个重叠的差异表达基因（DEG）。此外，富集分析揭示了与免疫激活过程、免疫细胞参与和炎症信号相关的通路。在使用多种机器学习策略后，细胞外基质蛋白1（ECM1）、白血病抑制因子受体（LIFR）、含寿司重复序列的X连锁蛋白（SRPX）和血栓素A2受体（TBXA2R）被确定为关键特征，并且在肿瘤邻近正常组织中表现出高表达。根据ssGSEA结果，ECM1、LIFR、SRPX和TBXA2R均与多种免疫细胞，如单核细胞和中性粒细胞显著相关。此外，人HCC样本的免疫染色显示，这些关键特征均与CD14阳性单核细胞共定位。我们的研究结果报道了HCC中免疫细胞的潜在特征，并证实它们定位于肿瘤邻近正常组织的单核细胞中。ECM1、LIFR、SRPX和TBXA2R未来可能成为HCC预测诊断、早期干预和免疫治疗的新潜在靶点。
