Zinc Finger Protein-Based Prognostic Model in Lung Adenocarcinoma Highlights FGD3 as a Potential Marker for Lorlatinib Resistance

Background Lung adenocarcinoma is the most common type of lung cancer and a major cause of cancer death. Zinc finger proteins are involved in tumor progression and may serve as diagnostic and therapeutic targets. Methods RNA-seq and clinical data were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognosis-associated ZNF genes were identified through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses. An eight-gene prognostic model was established and evaluated using Kaplan–Meier survival curves and time-dependent receiver operating characteristic (ROC) analyses, with external validation in the GSE50081 and GSE26939 datasets. A nomogram integrating independent prognostic factors was constructed and assessed by calibration plots. Immune infiltration, mutation profiles, and drug response were analyzed. Finally, we focused on a key molecule FGD3, examining its expression in LUAD cells and tissues, including lorlatinib-resistant models. Results The signature consisted of TRIM6, TRIM29, CTCFL, FGD3, GATA4, CASZ1, TRAF2 , and ZNF322 . T stage, N stage, and the risk score emerged as independent predictors of overall survival. The nomogram showed robust predictive value. High-risk patients exhibited an immune “desert” phenotype, higher somatic mutation rates, and elevated tumor mutational burden. Immunotherapy prediction and drug sensitivity analysis suggested that the model could potentially predict responses to immunotherapy, chemotherapy, and targeted therapy. FGD3 expression was downregulated in LUAD tissues and lorlatinib-resistant cells, decreasing with prolonged lorlatinib exposure. Restoration of FGD3 suppressed H3122LR proliferation and partially reversed resistance. Conclusion We developed and validated a novel ZNF-based prognostic model for LUAD that may serve as a valuable tool for risk stratification and guiding personalized treatment strategies.