Lei Qing, Fan Biao-Feng, Zhu Xi-Rui, Cui Bo-Wen, Kong Jin-Xiang, Ma Zhong-Rui, Xie Xi-Qi, Wang Wei-Wei
Department of Thoracic Surgery, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Xishan District, Kunming, China.
Medicine (Baltimore). 2025 Jul 4;104(27):e42958. doi: 10.1097/MD.0000000000042958.
Lung adenocarcinoma (LUAD) is one of the most lethal tumors and is characterized by high mortality and worse prognosis. Long noncoding RNA (lncRNA) plays an important function in tumor formation. Targeting tumor angiogenesis is a crucial cancer treatment strategy. So, this study aims to explore the effects of angiogenesis-related lncRNA (ARlncRNAs) signatures in the prediction of clinical prospects, immunotherapy, and their association with drug sensitivity.
The Cancer Genome Atlas (TCGA) database was used to get the genetic and clinical information. Co-expression analysis and Cox regression analysis were used to create prognostic profiles. To assess and validate the model's predictive efficacy, principal component analysis, survival analysis, and receiver operating characteristic curves were used. To enhance the model's prediction, a nomogram of forecasts was created, and calibration curves were used. Last, we studied variations in tumor mutational burden, immune-related function, and antiangiogenic medication sensitivity between high- and low-risk cohorts.
We successfully constructed an angiogenesis-related prognostic signature for LUAD, including 6 lncRNAs (AL157388.1, AL590428.1, LINC02057, AC245041.1, AC068228.1, and AL365181.2). Independent predictive analysis, receiver operating characteristic curve, C-index, and nomogram diagnostic results showed that ARlncRNAs accurately predicted outcome and 1-, 3-, and 5-year overall survival. According to the analysis of the differences in immune-related pathways between high- and low-risk cohorts, the low-risk cohort had a more active immune function. An analysis of immune checkpoints showed that low-risk patients had higher expression of immune checkpoints, which means that low-risk LUAD patients had a more active immune function; these patients may benefit from checkpoint blocking immunotherapy. Screening for sensitive drugs by predicting the IC50 of antivascular drugs, the high-risk cohort has lower IC50 values and is less likely to be resistant than the low-risk cohort.
Overall, the new predictive features constructed based on ARlncRNAs can effectively predict the outcome of patients and offer a fresh perspective for LUAD diagnosis and therapy.
肺腺癌（LUAD）是最致命的肿瘤之一，具有高死亡率和较差的预后特征。长链非编码RNA（lncRNA）在肿瘤形成中发挥重要作用。靶向肿瘤血管生成是一种关键的癌症治疗策略。因此，本研究旨在探讨血管生成相关lncRNA（ARlncRNAs）特征在预测临床前景、免疫治疗及其与药物敏感性关联方面的作用。
使用癌症基因组图谱（TCGA）数据库获取遗传和临床信息。采用共表达分析和Cox回归分析创建预后模型。为评估和验证模型的预测效能，使用主成分分析、生存分析和受试者工作特征曲线。为增强模型的预测能力，创建了预测列线图并使用校准曲线。最后，我们研究了高风险和低风险队列之间肿瘤突变负担、免疫相关功能和抗血管生成药物敏感性的差异。
我们成功构建了一个用于LUAD的血管生成相关预后特征，包括6个lncRNA（AL157388.1、AL590428.1、LINC02057、AC245041.1、AC068228.1和AL365181.2）。独立预测分析、受试者工作特征曲线、C指数和列线图诊断结果表明，ARlncRNAs能够准确预测结局以及1年、3年和5年总生存率。根据对高风险和低风险队列之间免疫相关通路差异的分析，低风险队列具有更活跃的免疫功能。免疫检查点分析表明，低风险患者免疫检查点表达较高，这意味着低风险LUAD患者具有更活跃的免疫功能；这些患者可能从检查点阻断免疫治疗中获益。通过预测抗血管生成药物的半数抑制浓度（IC50）筛选敏感药物，高风险队列的IC50值较低，与低风险队列相比耐药可能性较小。
总体而言，基于ARlncRNAs构建的新预测特征能够有效预测患者结局，并为LUAD的诊断和治疗提供新的视角。
