Multi-Omic Analysis Reveals a Lipid Metabolism Gene Signature and Predicts Prognosis and Chemotherapy Response in Thyroid Carcinoma.

OBJECTIVE

Lipid metabolic reprogramming is closely intertwined with the development and progression of thyroid carcinoma (TC); however, its specific mechanism remains elusive. This study aimed to investigate the association between lipid metabolism and TC progression.

METHODS

We employed liquid chromatography-mass spectrometry (LC/MS) for an untargeted metabolomics analysis, comparing 12 TC patients and 12 healthy controls (HC). Additionally, we conducted the screening of differentially expressed genes (DEGs) and identified differentially expressed lipid metabolism genes (LMGs). Multi-omic findings related to lipid metabolism were integrated to establish a prognostic risk model. The resulting risk score stratified TC patients into high- and low-risk groups. Overall survival (O.S.) was assessed using Kaplan-Meier (K-M) analysis. The immune landscape was evaluated using the CIBERSORT algorithm, and chemotherapeutic response was predicted utilizing the "pRRophetic" R package.

RESULTS

Our metabolomic analysis revealed heightened lipid metabolic activity in TC, corroborated by similar findings in transcriptomic analysis. Multi-omic analysis identified key LMGs (FABP4, PPARGC1A, AGPAT4, ALDH1A1, TGFA, and GPAT3) associated with fatty acids and glycerophospholipids metabolism. A novel risk model, incorporating these LMGs, confirmed significantly worse O.S. (p = 0.0045) in the high-risk group based on TCGA_THCA. Furthermore, high-risk TC patients exhibited lower immune cell infiltration, and predictive outcomes indicated the efficacy of potential therapeutic drugs across risk groups.

CONCLUSION

This multi-omic analysis underscores the potential utility of the lipid metabolism risk model in guiding clinical treatment and improving outcomes for TC patients.