Prognostic value of anoikis-related genes revealed using multi-omics analysis and machine learning based on lower-grade glioma features and tumor immune microenvironment

Background: This study investigates the role of anoikis-related genes (ARGs) in lower-grade glioma (LGG), aiming to provide novel insights into the disease’s mechanisms and to identify potential therapeutic targets.
Methods: We utilized unsupervised clustering approaches to classify LGG patients into distinct molecular subtypes based on ARGs with prognostic value. Several machine learning algorithms were then applied to identify genes most strongly associated with patient outcomes, which were used to develop and evaluate risk profiles.
Results: Our analysis identified two molecular subtypes of LGG with significantly different prognoses. Patients in Cluster 2 exhibited a median survival of 2.036 years, considerably shorter than the 7.994 years observed in Cluster 1 (P < 0.001). We also developed a six-gene ARG signature that effectively classified patients into high- and low-risk categories. The high-risk group had a median survival of 4.084 years, while the low-risk group had a median survival of 10.304 years (P < 0.001). Notably, immune profiles, tumor mutation characteristics, and drug sensitivities differed substantially between these risk groups. The high-risk group was characterized by a "cold" tumor microenvironment (TME), a lower IDH1 mutation rate (61.7% vs. 91.4%), a higher TP53 mutation rate (53.7% vs. 38.9%), and greater sensitivity to targeted therapies such as QS11 and PF-562271. Additionally, a nomogram integrating risk scores with clinicopathological features demonstrated strong predictive accuracy for clinical outcomes in LGG patients, with an AUC of 0.903 at one year. The robustness of this prognostic model was further confirmed through internal cross-validation and testing across three external cohorts.
Conclusions: Our findings suggest that ARGs could serve as reliable biomarkers for predicting clinical outcomes and assessing the effectiveness of immunotherapy in LGG patients.