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.
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.
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.
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.