Department of Radiology, Fourth Clinical Hospital of Hebei Medical University, Shijiazhuang, China.
J Gene Med. 2024 Jan;26(1):e3588. doi: 10.1002/jgm.3588. Epub 2023 Sep 16.
Liver cancer is a highly lethal and aggressive form of cancer that poses a significant threat to patient survival. Within this category, liver hepatocellular carcinoma (LIHC) represents the most common subtype of liver cancer. Despite decades of research and treatment, the overall survival rate for LIHC has not significantly improved. Improved models are necessary to differentiate high-risk cases and predict possible treatment options for LIHC patients. Recent studies have identified a set of genes associated with neutrophil extracellular traps (NETs) that may contribute to tumor growth and metastasis; however, their prognostic value in LIHC has yet to be established. This study aims to construct a prognostic signature based on a set of NET-related genes (NRGs) for patients diagnosed with LIHC.
The transcriptomic data and clinical information concerning LIHC patients were procured from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium LIHC (ICLIHC) databases, respectively. To determine the NRG subtypes, the k-means algorithm was employed, along with consensus clustering. The aforementioned analysis aided the construction of a prognostic signature utilizing the last absolute shrinkage and selection operator Cox analysis. To validate the prognostic model, an external dataset, receiver operating characteristic curve, and principal component analysis were utilized. Moreover, the immune microenvironment and the proportion of immune cells between high- and low-risk cases were scrutinized by ESTIMATE and CIBERSORT algorithms. Finally, gene set enrichment analysis was executed to investigate the potential mechanism of NRGs in the pathogenesis and prognosis of LIHC.
Two molecular subtypes of LIHC were identified based on the expression patterns of differentially expressed NRGs (DE-NRGs). The two subtypes demonstrated significant differences in survival rates and immune cell expression levels. The study results demonstrated the role of NRGs in antigen presentation, which led to the promotion of tumor immune escape. A risk model was developed and validated with strong overall survival prediction ability. The model, comprising 34 NRGs, showed a strong ability to predict prognosis.
We built a dependable prognostic signature based on NRGs for LIHC. We identified that NRGs could have a significant interaction in LIHC's immune microenvironment and therapeutic response. This finding offers insight into the molecular mechanisms and targeted therapy for LIHC.
肝癌是一种高度致命和侵袭性的癌症，对患者的生存构成重大威胁。在这一类别中，肝肝细胞癌（LIHC）是最常见的肝癌亚型。尽管经过几十年的研究和治疗，LIHC 的总体生存率并没有显著提高。需要建立更好的模型来区分高危病例，并为 LIHC 患者预测可能的治疗选择。最近的研究已经确定了一组与中性粒细胞胞外陷阱（NETs）相关的基因，这些基因可能与肿瘤生长和转移有关；然而，它们在 LIHC 中的预后价值尚未确定。本研究旨在构建基于一组 NET 相关基因（NRGs）的 LIHC 患者预后特征。
从癌症基因组图谱（TCGA）和国际癌症基因组联盟 LIHC（ICLIHC）数据库中分别获取 LIHC 患者的转录组数据和临床信息。为了确定 NRG 亚型，采用了 k-means 算法和共识聚类。上述分析有助于使用最后一个绝对收缩和选择算子 Cox 分析构建预后特征。为了验证预后模型，使用了外部数据集、接受者操作特征曲线和主成分分析。此外，通过 ESTIMATE 和 CIBERSORT 算法研究了高风险和低风险病例之间的免疫微环境和免疫细胞比例。最后，进行了基因集富集分析，以研究 NRGs 在 LIHC 发病机制和预后中的潜在机制。
根据差异表达 NRGs（DE-NRGs）的表达模式，确定了两种 LIHC 分子亚型。这两种亚型在生存率和免疫细胞表达水平上存在显著差异。研究结果表明，NRGs 在抗原呈递中的作用促进了肿瘤免疫逃逸。建立并验证了一个具有较强总体生存预测能力的风险模型。该模型由 34 个 NRGs 组成，具有很强的预后预测能力。
我们构建了一个基于 NRGs 的 LIHC 可靠预后特征。我们发现，NRGs 可能在 LIHC 的免疫微环境和治疗反应中具有显著的相互作用。这一发现为 LIHC 的分子机制和靶向治疗提供了新的见解。
