Li Rumeng, Qiu Tingting, Zhou Qiangqiang, He Fajian, Jie Chen, Zheng Xinyu, Lu Zeguang, Wu Qiuji, Xie Conghua
Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Nanchang, China.
Front Immunol. 2023 Mar 17;14:1139599. doi: 10.3389/fimmu.2023.1139599. eCollection 2023.
Histone acetylation-related lncRNAs (HARlncRNAs) play significant roles in various cancers, but their impact on lung adenocarcinoma (LUAD) remains unclear. This study aimed to develop a new HARlncRNA-based prognostic model for LUAD and to explore its potential biological mechanisms.
We identified 77 histone acetylation genes based on previous studies. HARlncRNAs related to prognosis were screened by co-expression, univariate and multivariate analyses, and least absolute shrinkage selection operator regression (LASSO). Afterward, a prognostic model was established based on the screened HARlncRNAs. We analysed the relationship between the model and immune cell infiltration characteristics, immune checkpoint molecule expression, drug sensitivity, and tumour mutational burden (TMB). Finally, the entire sample was divided into three clusters to further distinguish between hot and cold tumours.
A seven-HARlncRNA-based prognostic model was established for LUAD. The area under the curve (AUC) of the risk score was the highest among all the analysed prognostic factors, indicating the accuracy and robustness of the model. The patients in the high-risk group were predicted to be more sensitive to chemotherapeutic, targeted, and immunotherapeutic drugs. It was worth noting that clusters could effectively identify hot and cold tumours. In our study, clusters 1 and 3 were considered hot tumours that were more sensitive to immunotherapy drugs.
We developed a risk-scoring model based on seven prognostic HARlncRNAs that promises to be a new tool for evaluating the prognosis and efficacy of immunotherapy in patients with LUAD.
组蛋白乙酰化相关长链非编码RNA(HARlncRNAs)在多种癌症中发挥重要作用,但其对肺腺癌(LUAD)的影响仍不清楚。本研究旨在开发一种基于HARlncRNAs的LUAD预后新模型,并探讨其潜在的生物学机制。
我们根据以往研究确定了77个组蛋白乙酰化基因。通过共表达、单变量和多变量分析以及最小绝对收缩选择算子回归(LASSO)筛选出与预后相关的HARlncRNAs。随后,基于筛选出的HARlncRNAs建立了预后模型。我们分析了该模型与免疫细胞浸润特征、免疫检查点分子表达、药物敏感性和肿瘤突变负荷(TMB)之间的关系。最后,将整个样本分为三个簇,以进一步区分热肿瘤和冷肿瘤。
建立了一种基于七个HARlncRNAs的LUAD预后模型。风险评分的曲线下面积(AUC)在所有分析的预后因素中最高,表明该模型的准确性和稳健性。预测高危组患者对化疗、靶向治疗和免疫治疗药物更敏感。值得注意的是,簇可以有效识别热肿瘤和冷肿瘤。在我们的研究中,簇1和簇3被认为是对免疫治疗药物更敏感的热肿瘤。
我们开发了一种基于七个预后性HARlncRNAs的风险评分模型,有望成为评估LUAD患者预后和免疫治疗疗效的新工具。