Integrating Functional Genomic Screens and Multi-Omics Data to Construct a Prognostic Model for Lung Adenocarcinoma...

Background: Lung adenocarcinoma (LUAD) presents a significant clinical challenge due to its high heterogeneity and limited treatment efficacy, creating an urgent need for reliable prognostic biomarkers and novel therapeutic targets. Integrating functional genomic vulnerabilities with patient multi-omics data offers a promising approach. Methods: We identified genes essential for LUAD cell proliferation from genome-scale CRISPR-Cas9 screening data (DepMap). These were integrated with transcriptomic data from the TCGA-LUAD cohort to select candidate genes. A prognostic risk-score model was constructed using LASSO and multivariate Cox regression analyses and validated in independent GEO datasets. We analyzed the model's association with clinical features, signaling pathways, tumor immune microenvironment, and drug sensitivity. The predictive value for immunotherapy response was assessed using a real-world cohort. The core gene SPC25 was further validated through in vitro and in vivo experiments and single-cell RNA-seq analysis. Results: A robust 7-gene risk-score model was established. This model effectively stratified patient prognosis in training and validation sets and was an independent prognostic factor. A high-risk score correlated with advanced tumor stage and an immunosuppressive microenvironment. High expression of the signature genes predicted poor immunotherapy response. Functional experiments confirmed that SPC25 knockdown significantly inhibited LUAD cell proliferation, migration, and colony formation. Critically, in vivo xenograft experiments demonstrated that SPC25 depletion markedly suppressed tumor growth. Single-cell sequencing revealed high SPC25 expression in tumor cells and specific immunosuppressive T-cell subsets. Conclusions: We developed a potent prognostic model for LUAD and validated SPC25 as a key oncogene and promising therapeutic target.