The high risk of postoperative mortality in lung adenocarcinoma (LUAD) patients is principally driven by cancer recurrence and low response rates to adjuvant treatment. Here

A combined cohort containing 1

026 stage I–III patients was divided into the learning (

n

 = 678) and validation datasets (

n

 = 348). The former was used to establish a 16-mRNA risk signature for recurrence prediction with multiple statistical algorithms

which was verified in the validation set. Univariate and multivariate analyses confirmed it as an independent indicator for both recurrence-free survival (RFS) and overall survival (OS). Distinct molecular characteristics between the two groups including genomic alterations

and hallmark pathways were comprehensively analyzed. Remarkably

the classifier was tightly linked to immune infiltrations

highlighting the critical role of immune surveillance in prolonging survival for LUAD. Moreover

the classifier was a valuable predictor for therapeutic responses in patients

and the low-risk group was more likely to yield clinical benefits from immunotherapy. A transcription factor regulatory protein–protein interaction network (TF-PPI-network) was constructed via weighted gene co-expression network analysis (WGCNA) concerning the hub genes of the signature. The constructed multidimensional nomogram dramatically increased the predictive accuracy. Therefore

our signature provides a forceful basis for individualized LUAD management with promising potential implications.