Integrative multi-omics reveals a prognostic and therapeutic landscape of synthetic lethality-associated signatures in lung adenocarcinoma
TL;DR
This work integrated single-cell RNA sequencing, spatial transcriptomics, and machine learning to characterize synthetic lethality (SL)-associated transcriptional activity in Lung adenocarcinoma and identified 13 high-SL signature genes, with IFRD2 (interferon-related developmental regulator 2) among the top contributors.
Abstract
Summary Lung adenocarcinoma (LUAD) exhibits considerable heterogeneity and therapeutic resistance. Here, we integrated single-cell RNA sequencing, spatial transcriptomics, and machine learning to characterize synthetic lethality (SL)-associated transcriptional activity in LUAD. We quantified SL activity across malignant epithelial cells and stratified them into high-, dominant-, and low-SL groups. High-SL cells were enriched in advanced-stage and metastatic samples and showed reduced differentiation potential. Through multiple machine learning algorithms, we identified 13 high-SL signature genes, with IFRD2 (interferon-related developmental regulator 2) among the top contributors. Experimental validation confirmed IFRD2 upregulation in high-malignancy cell lines, and IFRD2 knockdown modulated sensitivity to the PARP inhibitor niraparib. The predicted compound prostaglandin A1 exhibited selective cytotoxicity against LUAD cells and induced DNA damage, consistent with an SL-related mechanism. Our findings establish a framework for identifying SL-associated vulnerabilities and provide a resource of candidate genes and compounds for further mechanistic investigation in LUAD precision therapy.