Identification of a polycomb group-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma.

Background: Several studies have reported the role of polycomb group (PcG) genes in human cancers; however, their role in lung adenocarcinoma (LUAD) is unknown.

Methods: Firstly, consensus clustering analysis was used to identify PcG patterns among the 633 LUAD samples in the training dataset. The PcG patterns were then compared in terms of the overall survival (OS), signaling pathway activation, and immune cell infiltration. The PcG-related gene score (PcGScore) was developed using Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to estimate the prognostic value and treatment sensitivity of LUAD. Finally, the prognostic ability of the model was validated using a validation dataset.

Results: Two PcG patterns were obtained by consensus clustering analysis, and the two patterns showed significant differences in prognosis, immune cell infiltration, and signaling pathways. Both the univariate and multivariate Cox regression analyses confirmed that the PcGScore was a reliable and independent predictor of LUAD (P<0.001). The high- and low-PCGScore groups showed significant differences in the prognosis, clinical outcomes, genetic variation, immune cell infiltration, and immunotherapeutic and chemotherapeutic effects. Lastly, the PcGScore demonstrated exceptional accuracy in predicting the OS of the LUAD patients in a validation dataset (P<0.001).

Conclusions: The study indicated that the PcGScore could serve as a novel biomarker to predict prognosis, clinical outcomes, and treatment sensitivity for LUAD patients.