我们基于结肠腺癌（COAD）筛选了关键的血管生成相关lncRNA，构建了预测COAD预后的RiskS-core模型，有助于揭示COAD的发病机制以及优化临床治疗。 lncRNA在肿瘤进展和预后中的调节作用已被证实，但很少有研究探讨血管生成相关的lncRNA在COAD中的作用。识别关键的血管生成相关的lncRNA并建立RiskS-core模型来预测COAD患者的生存概率，帮助优化临床治疗。样本数据为收集自癌症基因组图谱 (TCGA) 和基因表达综合 (GEO) 数据库。使用单样本基因集富集分析（ssGSEA）方法计算样本中的 HALLMARK 通路评分。通过集成管道算法过滤与血管生成相关的 LncRNA。基于 LncRNA 的亚型由 ConsensusClusterPlus 进行分类，然后与其他已建立的亚型进行比较。基于单变量 Cox、最小绝对收缩和选择算子 (LASSO) 回归和逐步回归分析创建了 RiskS-core 模型。通过应用 R 包生存来绘制 Kaplan-Meier 曲线。时间相关的 ROC 曲线由 timeROC 包绘制。最后，利用肿瘤免疫功能障碍与排除（TIDE）软件和pRRophetic软件包分析免疫治疗获益和药物敏感性。通路分析显示血管生成通路是影响COAD患者预后的危险因素。共筛选出66个与血管生成相关的lncRNA，获得3个分子亚型（S1、S2、S3）。 S1和S2的预后优于S3。与现有亚型相比，S3亚型与其他两种亚型存在显着差异。免疫分析显示，S2亚型的免疫细胞评分低于S1和S3亚型，而S1和S3亚型的TIDE评分也最高。我们招募了 8 个关键的 lncRNA 来开发 RiskS 核心模型。预计生存率较差且 TIDE 评分较高的高 RiskS 核心组从免疫治疗中获益有限，但可能对化疗更敏感。由RiskS-core签名和其他临床病理特征设计的列线图揭示了COAD治疗的合理预测能力。我们构建了基于血管生成相关lncRNA的RiskS-core模型，该模型可以作为COAD患者的潜在预后预测因子，并可能提供线索用于抗血管生成干预的应用。我们的结果可能有助于评估 COAD 的预后并提供更好的治疗策略。版权所有© Bentham Science Publishers；如有任何疑问，请发送电子邮件至 epub@benthamscience.net。

We screened key angiogenesis-related lncRNAs based on colon adenocarcinoma (COAD) to construct a RiskS-core model for predicting COAD prognosis and help reveal the pathogenesis of the COAD as well as optimize clinical treatment.Regulatory roles of lncRNAs in tumor progression and prognosis have been confirmed, but few studies have probed into the role of angiogenesis-related lncRNAs in COAD.To identify key angiogenesis-related lncRNAs and build a RiskS-core model to predict the survival probability of COAD patients and help optimize clinical treatment.Sample data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The HALLMARK pathway score in the samples was calculated using the single sample gene set enrichment analysis (ssGSEA) method. LncRNAs associated with angiogenesis were filtered by an integrated pipeline algorithm. LncRNA-based subtypes were classified by ConsensusClusterPlus and then compared with other established subtypes. A RiskS-core model was created based on univariate Cox, least absolute shrinkage and selection operator (LASSO) regression and stepwise regression analysis. The Kaplan-Meier curve was drawn by applying R package survival. The time-dependent ROC curves were drawn by the timeROC package. Finally, immunotherapy benefits and drug sensitivity were analyzed using tumor immune dysfunction and exclusion (TIDE) software and pRRophetic package.Pathway analysis showed that the angiogenesis pathway was a risk factor affecting the prognosis of COAD patients. A total of 66 lncRNAs associated with angiogenesis were screened, and three molecular subtypes (S1, S2, S3) were obtained. The prognosis of S1 and S2 was better than that of S3. Compared with the existing subtypes, the S3 subtype was significantly different from the other two subtypes. Immunoassay showed that immune cell scores of the S2 subtype were lower than those of the S1 and S3 subtypes, which also had the highest TIDE scores. We recruited 8 key lncRNAs to develop a RiskS-core model. The high RiskS-core group with inferior survival and higher TIDE scores was predicted to benefit limitedly from immunotherapy, but it may be more sensitive to chemotherapeutics. A nomogram designed by RiskS-core signature and other clinicopathological characteristics shed light on rational predictive power for COAD treatment.We constructed a RiskS-core model based on angiogenesis-related lncRNAs, which could serve as potential prognostic predictors for COAD patients and may offer clues for the intervention of anti-angiogenic application. Our results may help evaluate the prognosis of COAD and provide better treatment strategies.Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.