ArticleCancer medicine2025
Multi-Omic Analysis Reveals a Lipid Metabolism Gene Signature and Predicts Prognosis and Chemotherapy Response in Thyroid Carcinoma.
Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- APOC1-Associated VLDL-Pathway Dysregulation in Papillary Thyroid Carcinoma: A Reproducible Cross-Cohort Transcriptomic Signature Associated With the Diagnostic-to-Treatment Interval.Chemical biology & drug design · 2026Article
- The molecular mechanisms and potential therapeutic implications of the crosstalk between DNA methylation and metabolic reprogramming in thyroid cancer.Cell death discovery · 2026Review
- Prognostic impact of the lipid metabolism gene AGPAT4 in the tumor immune microenvironment of thyroid cancer.Genomics & informatics · 2026Article
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Authors and funding
9 authors.
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Abstract
objectiveLipid 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.
methodsWe 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.
resultsOur 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.
conclusionThis multi-omic analysis underscores the potential utility of the lipid metabolism risk model in guiding clinical treatment and improving outcomes for TC patients.
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