The accumulation of aberrant lipids and abnormal lipid metabolism in silent corticotroph adenomas (SCAs) could contribute to changes in clinical phenotypes, especially sphenoid sinus invasion.To systematically investigate lipidomic and transcriptomic alterations associated with invasiveness and their potential molecular mechanisms in SCAs and to provide candidate biomarkers for predicting invasiveness and novel treatment options for invasive SCAs by targeting lipids.Fifty-four SCAs (34 invasive/20 noninvasive) were subjected to lipidomic analysis based on ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), and 42 clinically nonfunctioning pituitary adenomas (23 invasive/19 noninvasive) were subjected to transcriptomic analysis. Differential analysis was performed to determine differential lipids and genes between invasive and noninvasive tumors. A functionally connected network was constructed with the molecular pathways as cores. Multiple machine learning methods were applied to identify the most critical lipids, which were further used to construct a lipidomic signature to predict invasive SCAs by multivariate logistic regression, and its performance was evaluated by receiver operating characteristic analysis.Twenty-eight differential lipids were identified, and a functionally connected network was constructed with 2 lipids, 17 genes, and 4 molecular pathways. Connectivity Map (CMap) analysis further revealed 32 potential drugs targeting 4 genes and related pathways. Then, the four most critical lipids were identified as risk factors contributing to the invasive phenotype. A lipidomic signature was constructed and showed excellent performance in discriminating invasive and noninvasive SCAs.The lipidomic signature could serve as a promising predictor for the invasive SCA phenotype and provide potential therapeutic targets for SCAs.