Hui Hongliang, Li Dan, Lin Yangui, Miao Haoran, Zhang Yiqian, Li Huaming, Qiu Fan, Jiang Bo
Department of Thoracic Surgery, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
Community Health Center, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China.
J Thorac Dis. 2023 Jul 31;15(7):3919-3933. doi: 10.21037/jtd-23-952. Epub 2023 Jul 28.
Studies have shown that long non-coding RNAs (lncRNAs) are found to be hypoxia-regulated lncRNAs in cancer. Lung adenocarcinoma (LUAD) is the leading cause of cancer death worldwide, and despite early surgical removal, has a poor prognosis and a high recurrence rate. Thus, we aimed to identify subtype classifiers and construct a prognostic risk model using hypoxia-associated long noncoding RNAs (hypolncRNAs) for LUAD.
Clinical data of LUAD samples with prognosis information obtained from the Gene Expression Omnibus (GEO), acted as validation dataset, and The Cancer Genome Atlas (TCGA) databases, served as training dataset, were used to screen hypolncRNAs in each dataset by univariate Cox regression analysis; the intersection set was used for subsequent analyses. Unsupervised clustering analysis was performed based on the expression of hypolncRNAs using the 'ConsensuClusterPlus' package. The tumor microenvironment (TME) was compared between LUAD subgroups by analyzing the expression of immune cell infiltration, immune components, stromal components, immune checkpoints, and chemokine secretion. To identify robust prognostically associated hypolncRNAs and construct a risk score model, multivariate Cox regression analysis was performed.
A total of 14 hypolncRNAs were identified. Based on the expression of these hypolncRNAs, patients with LUAD were classified into three hypolncRNA-regulated subtypes. The three subtypes differed significantly in immune cell infiltration, stromal score, specific immune checkpoints, and secretion of chemokines and their receptors. The Tumor Immune Dysfunction and Exclusion (TIDE) scores and myeloid-derived suppressor cell (MDSC) scores were also found to differ significantly among the three hypolncRNA-regulated subtypes. Four of the 14 hypolncRNAs were used to construct a signature to distinguish the overall survival (OS) in TCGA dataset (P<0.0001) and GEO dataset (P=0.0032) and sensitivity to targeted drugs in patients at different risks of LUAD.
We characterized three regulatory subtypes of hypolncRNAs with different TMEs. We developed a signature based on hypolncRNAs, contributing to the development of personalized therapy and representing a new potential therapeutic target for LUAD.
研究表明，长链非编码RNA（lncRNAs）在癌症中是缺氧调节的lncRNAs。肺腺癌（LUAD）是全球癌症死亡的主要原因，尽管早期进行了手术切除，但预后较差且复发率高。因此，我们旨在使用缺氧相关长链非编码RNA（hypolncRNAs）来识别肺腺癌的亚型分类器并构建预后风险模型。
从基因表达综合数据库（GEO）获取的具有预后信息的肺腺癌样本临床数据作为验证数据集，癌症基因组图谱（TCGA）数据库作为训练数据集，通过单变量Cox回归分析在每个数据集中筛选hypolncRNAs；交集集用于后续分析。使用“ConsensuClusterPlus”软件包基于hypolncRNAs的表达进行无监督聚类分析。通过分析免疫细胞浸润、免疫成分、基质成分、免疫检查点和趋化因子分泌的表达，比较肺腺癌亚组之间的肿瘤微环境（TME）。为了识别与预后密切相关的可靠hypolncRNAs并构建风险评分模型，进行了多变量Cox回归分析。
共鉴定出14种hypolncRNAs。根据这些hypolncRNAs的表达，将肺腺癌患者分为三种hypolncRNA调节亚型。这三种亚型在免疫细胞浸润、基质评分、特定免疫检查点以及趋化因子及其受体的分泌方面存在显著差异。还发现肿瘤免疫功能障碍与排除（TIDE）评分和髓源性抑制细胞（MDSC）评分在三种hypolncRNA调节亚型之间也存在显著差异。在14种hypolncRNAs中，有4种用于构建一个特征，以区分TCGA数据集（P<0.0001）和GEO数据集（P=0.0032）中的总生存期（OS）以及不同风险的肺腺癌患者对靶向药物的敏感性。
我们对具有不同肿瘤微环境的三种hypolncRNA调节亚型进行了特征描述。我们基于hypolncRNAs开发了一个特征，有助于个性化治疗的发展，并代表了肺腺癌的一个新的潜在治疗靶点。
