Development and validation of a transcription factor regulatory network-based signature for individualized prognostic risk in lung adenocarcinoma

Abstract

Despite significant progress in diagnostic and therapeutic modalities, lung adenocarcinoma (LUAD) still exhibits a high recurrence risk and a low 5-year survival rate. Reliable prognostic signatures are imperative for risk stratification in LUAD patients. This study encompassed 2740 patients from 23 LUAD cohorts, including one single-cell RNA sequencing (scRNA-seq) dataset, five bulk RNA-seq datasets, and 17 microarray datasets. Using scRNA-seq dataset, we defined a group of epithelial-specific transcription factors significantly over-represented in the epithelial-to-mesenchymal transition (EMT) gene set (enrichment ratio [ER] = 5.80, Fisher's exact test p < .001), and the corresponding target genes were significantly enriched in the cancer driver gene set (ER = 2.74, p < .001), indicating of their crucial roles in the EMT process and tumor progression. We constructed a single-cell gene pairs (scGPS) signature, composed of 3521 gene pairs derived from the epithelial cell-specific transcription factor regulatory network, to predict overall survival (OS) of LUAD. High-risk patients identified by scGPS in the discovery cohort exhibited significantly worse OS compared to low-risk patients (Hazard ratio [HR] = 1.78, 95% CI: 1.29–2.46, log-rank p = 1.80 × 10⁻⁴). The scGPS outperformed other established gene signatures and demonstrated robust prognostic stratification across various independent datasets, including microarray data and even early-stage LUAD patients. It remained an independent prognostic factor after adjusting for clinical and pathologic factors. In addition, combining scGPS with tumor stage further enhanced prognostic accuracy compared to using stage alone. The scGPS signature offers individualized prognosis estimations, showing significant potential for practical application in clinical settings.