Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks

Abstract

Lung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes and proteomes between sexes. We used TIGER to analyze TCGA-LUAD expression data and found sex-biased activity of transcription factors (TFs); we used PTM-SEA with CPTAC-LUAD proteomics data and found sex-biased kinase activity. We combined these to construct a kinase-TF signaling network and discovered druggable pathways linked to cancer-related processes. We also found significant sex biases in clinically relevant TFs and kinases, including NR3C1, AR, and AURKA. Using the PRISM drug screening database, we identified potential sex-specific drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors. Our findings emphasize the importance of considering sex and using multi-omics network methods to discover personalized cancer therapies.