Wang Wenjuan, Ye Yingquan, Zhang Xuede, Sun Weijie, Bao Lingling
Department of Hematology and Oncology, Beilun District People's Hospital, Ningbo, China.
The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Heliyon. 2023 Feb 23;9(3):e13989. doi: 10.1016/j.heliyon.2023.e13989. eCollection 2023 Mar.
The tumour microenvironment is a key determinant of the efficacy of immunotherapy. Angiogenesis is closely linked to tumour immunity. We aimed to screen long non-coding ribonucleic acids (lncRNAs) associated with angiogenesis to predict the prognosis of individuals with hepatocellular carcinoma (HCC) and characterise the tumour immune microenvironment (TIME). Patient data, including transcriptome and clinicopathological parameters, were retrieved from The Cancer Genome Atlas database. Moreover, co-expression algorithm was utilized to obtain angiogenesis-related lncRNAs. Additionally, survival-related lncRNAs were identified using Cox regression and the least absolute shrinkage and selection operator algorithm, which aided in constructing an angiogenesis-related lncRNA signature (ARLs). The ARLs was validated using Kaplan-Meier method, time-dependent receiver operating characteristic analyses, and Cox regression. Additionally, an independent external HCC dataset was used for further validation. Then, gene set enrichment analysis, immune landscape, and drug sensitivity analyses were implemented to explore the role of the ARLs. Finally, cluster analysis divided the entire HCC dataset into two clusters to distinguish different subtypes of TIME. This study provides insight into the involvement of angiogenesis-associated lncRNAs in predicting the TIME characteristics and prognosis for individuals with HCC. Furthermore, the developed ARLs and clusters can predict the prognosis and TIME characteristics in HCC, thereby aiding in selecting the appropriate therapeutic strategies involving immune checkpoint inhibitors and targeted drugs.
肿瘤微环境是免疫治疗疗效的关键决定因素。血管生成与肿瘤免疫密切相关。我们旨在筛选与血管生成相关的长链非编码核糖核酸（lncRNAs），以预测肝细胞癌（HCC）患者的预后，并描绘肿瘤免疫微环境（TIME）。从癌症基因组图谱数据库中检索了包括转录组和临床病理参数在内的患者数据。此外，利用共表达算法获得与血管生成相关的lncRNAs。另外，使用Cox回归和最小绝对收缩和选择算子算法鉴定与生存相关的lncRNAs，这有助于构建与血管生成相关的lncRNA特征（ARLs）。使用Kaplan-Meier方法、时间依赖性受试者工作特征分析和Cox回归对ARLs进行验证。此外，使用独立的外部HCC数据集进行进一步验证。然后，进行基因集富集分析、免疫景观分析和药物敏感性分析，以探索ARLs的作用。最后，聚类分析将整个HCC数据集分为两个簇，以区分不同的TIME亚型。本研究深入探讨了血管生成相关lncRNAs在预测HCC患者的TIME特征和预后中的作用。此外，所开发的ARLs和簇可以预测HCC的预后和TIME特征，从而有助于选择涉及免疫检查点抑制剂和靶向药物的合适治疗策略。
