单细胞和 bulk RNA-Seq 揭示血管生成异质性和微环境特征，以评估肺腺癌的预后和治疗反应。

BACKGROUND: Angiogenesis stands as a pivotal hallmark in lung adenocarcinoma (LUAD), intricately shaping the tumor microenvironment (TME) and influencing LUAD progression. It emerges as a promising therapeutic target for LUAD, affecting patients' prognosis. However, its role in TME, LUAD prognosis, and its clinical applicability remain shrouded in mystery. METHODS: We employed integrated single-cell and bulk transcriptome sequencing to unravel the heterogeneity of angiogenesis within LUAD cells. Through "consensus clustering", we delineated distinct angiogenic clusters and deciphered their TME features. "Monocle2" was used to unravel divergent trajectories within malignant cell subpopulations of LUAD. Additionally, regulon submodules and specific cellular communication patterns of cells in different angiogenic states were analyzed by "pyscenic" and "Cellchat" algorithms. The "univariate Cox" and "LASSO" algorithms were applied to build angiogenic prognostic models. Immunohistochemistry (IHC) on clinical samples validated the role of model factors in LUAD angiogenesis. We utilized CTRP 2.0 and PRISM databases for pinpointing sensitive drugs against lung adenocarcinoma. RESULTS: Two clusters for the activation of angiogenesis were identified, with Cluster 1 showing a poor prognosis and a pro-cancerous TME. Three differentiated states of malignant epithelial LUAD cells were identified, which had different degrees of angiogenic activation, were regulated by three different regulon submodules, and had completely different crosstalk from other cells in TME. The experiments validate that SLC2A1 promotes angiogenesis in LUAD. ARS (Angiogenesis related score) had a high prognostic value; low ARSs showed immunotherapy benefits, whereas high ARSs were sensitive to 15 chemotherapeutic agents. CONCLUSION: The assessment of angiogenic clusters helps to determine the prognostic and TME characteristics of LUAD. Angiogenic prognostic models can be used to assess the prognosis, immunotherapeutic response, and chemotherapeutic drug sensitivity of LUAD.

背景：血管生成是肺腺癌 (LUAD) 的一个关键标志，它错综复杂地塑造了肿瘤微环境 (TME)，并影响 LUAD 的进展。它是 LUAD 的一个有前途的治疗靶点，影响着患者的预后。然而，它在 TME、LUAD 预后及其临床应用中的作用仍然是个谜。

方法：我们采用整合的单细胞和批量转录组测序来揭示 LUAD 细胞内血管生成的异质性。通过“共识聚类”，我们描绘了不同的血管生成簇，并解析了它们的 TME 特征。“Monocle2”被用来揭示 LUAD 恶性细胞亚群内的不同轨迹。此外，通过“pyscenic”和“Cellchat”算法分析了不同血管生成状态下细胞的调节子亚模块和特定细胞间通讯模式。应用“单变量 Cox”和“LASSO”算法构建血管生成预后模型。对临床样本进行免疫组织化学 (IHC) 验证模型因素在 LUAD 血管生成中的作用。我们利用 CTRP 2.0 和 PRISM 数据库确定针对肺腺癌的敏感药物。

结果：确定了两种血管生成激活的聚类，其中聚类 1 表现出不良预后和促癌性 TME。鉴定出三种分化状态的恶性上皮 LUAD 细胞，它们具有不同程度的血管生成激活，由三个不同的调节子亚模块调控，与 TME 中的其他细胞有完全不同的相互作用。实验验证 SLC2A1 促进 LUAD 的血管生成。ARS（血管生成相关评分）具有较高的预后价值；低 ARS 显示免疫治疗获益，而高 ARS 对 15 种化疗药物敏感。

结论：评估血管生成簇有助于确定 LUAD 的预后和 TME 特征。血管生成预后模型可用于评估 LUAD 的预后、免疫治疗反应和化疗药物敏感性。