Identify novel molecular subtypes of lung adenocarcinoma to predict treatment response and prognosis.

[UNLABELLED] This study aimed to establish a novel quantification system of anoikis and angiogenesis related genes (AAGs) and comprehensively analyze the relationship between AAG signature score (AAGscore) and the prognosis, tumor immune microenvironment, and therapeutic response in LUAD. Univariate Cox regression analysis was used to screen prognosis-related AAGs. A consensus clustering algorithm was applied for AAG subtypes identification on 750 LUAD samples from TCGA and GEO databases. The differences in prognosis, immune infiltration, and therapeutic response were evaluated among the subtypes. The AAG signature scoring system was constructed by a principal component analysis algorithm. Cluster-A demonstrated a high gene expression and stromal-score with a poor prognosis. Our results showed that there were significant differences in survival time, mutation frequency, expression of chemokines and receptors, expression of immune checkpoint related genes, immunotherapy efficacy and antitumor drug sensitivity between high- and low-AAGscore groups. Survival analysis revealed that patients in the high-AAGscore group had better prognosis. We also discovered that the AAGcluster-B and geneCluster-2 subtype showed higher AAGscores. In addition, a number of conventional antitumor drugs were selected to test the sensitivity of high- and low-AAGscore groups to drug therapy. In summary, we constructed molecular subtypes and AAGscores of LUAD based on AAGs. The risk score model can be used to predict the prognosis of LUAD patients and the efficacy and sensitivity of antitumor drugs.

[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1038/s41598-025-32262-w.