Department of Hepatobiliary and Pancreatic Surgery II, General Surgery Center, The First Hospital of Jilin University, Changchun, China.
Front Immunol. 2023 Feb 13;14:1029427. doi: 10.3389/fimmu.2023.1029427. eCollection 2023.
The past decade has witnessed unprecedented scientific breakthroughs, including immunotherapy, which has great potential in clinical applications for liver cancer.
Public data were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases and analyzed with R software.
The LASSO and SVM-RFE machine learning algorithms identified 16 differentially expressed genes (DEGs) related to immunotherapy, namely, GNG8, MYH1, CHRNA3, DPEP1, PRSS35, CKMT1B, CNKSR1, C14orf180, POU3F1, SAG, POU2AF1, IGFBPL1, CDCA7, ZNF492, ZDHHC22, and SFRP2. Moreover, a logistic model (CombinedScore) was established based on these DEGs, showing an excellent prediction performance for liver cancer immunotherapy. Patients with a low CombinedScore might respond better to immunotherapy. Gene Set Enrichment Analysis showed that many metabolism pathways were activated in patients with a high CombinedScore, including butanoate metabolism, bile acid metabolism, fatty acid metabolism, glycine serine and threonine metabolism, and propanoate metabolism. Our comprehensive analysis showed that the CombinedScore was negatively correlated with the levels of most tumor-infiltrating immune cells and the activities of key steps of cancer immunity cycles. Continually, the CombinedScore was negatively associated with the expression of most immune checkpoints and immunotherapy response-related pathways. Moreover, patients with a high and a low CombinedScore exhibited diverse genomic features. Furthermore, we found that CDCA7 was significantly correlated with patient survival. Further analysis showed that CDCA7 was positively associated with M0 macrophages and negatively associated with M2 macrophages, suggesting that CDCA7 could influence the progression of liver cancer cells by affecting macrophage polarization. Next, single-cell analysis showed that CDCA7 was mainly expressed in prolif T cells. Immunohistochemical results confirmed that the staining intensity of CDCA7 was prominently increased in the nucleus in primary liver cancer tissues compared to adjacent non-tumor tissues.
Our results provide novel insights into the DEGs and factors affecting liver cancer immunotherapy. Meanwhile, CDCA7 was identified as a potential therapeutic target in this patient population.
过去十年见证了前所未有的科学突破，包括免疫疗法，它在肝癌的临床应用中有很大的潜力。
从癌症基因组图谱（TCGA）和国际癌症基因组联合会（ICGC）数据库中获取公共数据，并使用 R 软件进行分析。
LASSO 和 SVM-RFE 机器学习算法鉴定出 16 个与免疫治疗相关的差异表达基因（DEGs），分别为 GNG8、MYH1、CHRNA3、DPEP1、PRSS35、CKMT1B、CNKSR1、C14orf180、POU3F1、SAG、POU2AF1、IGFBPL1、CDCA7、ZNF492、ZDHHC22 和 SFRP2。此外，基于这些 DEGs 建立了逻辑模型（组合评分），对肝癌免疫治疗具有出色的预测性能。组合评分低的患者可能对免疫治疗反应更好。基因集富集分析显示，组合评分高的患者中有许多代谢途径被激活，包括丁酸盐代谢、胆汁酸代谢、脂肪酸代谢、甘氨酸丝氨酸和苏氨酸代谢以及丙酸盐代谢。我们的综合分析表明，组合评分与大多数肿瘤浸润免疫细胞的水平和癌症免疫周期的关键步骤的活性呈负相关。持续地，组合评分与大多数免疫检查点和免疫治疗反应相关途径的表达呈负相关。此外，组合评分高和低的患者表现出不同的基因组特征。此外，我们发现 CDCA7 与患者的生存显著相关。进一步的分析表明，CDCA7 与 M0 巨噬细胞呈正相关，与 M2 巨噬细胞呈负相关，表明 CDCA7 可以通过影响巨噬细胞极化来影响肝癌细胞的进展。接下来，单细胞分析表明 CDCA7 主要在增殖 T 细胞中表达。免疫组织化学结果证实，与相邻非肿瘤组织相比，原发性肝癌组织中 CDCA7 的核染色强度明显增加。
我们的研究结果为肝癌免疫治疗的差异表达基因和影响因素提供了新的见解。同时，鉴定出 CDCA7 是该患者群体中的一个潜在治疗靶点。
