Background The preliminary study found that mitochondrial metabolism and structure are abnormal in thyroid cancer (THCA) patients. Therefore, this study systematically investigates the relationship between mitochondrial metabolism-related genes (MMRGs) and the prognosis of THCA patients, while establishing a prognostic model for THCA. Methods This study utilized THCA transcriptome data from the UCSC Xena database, performed differential expression analysis using the “limma” package, and intersected differentially expressed genes (DEGs) with MMRG to identify differentially expressed MMRGs (DEMMRGs). THCA prognostic genes were identified using the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis, and a prognostic model was constructed. Using ssGSEA, CIBERSORT and Immune Phenotype Score methods, we compared differences in immune cell infiltration levels and anti-tumor immune response capacity between distinct risk groups. Furthermore, molecular subtypes of THCA were identified through consensus clustering analysis. Results This study systematically identified nine MMRGs to construct a robust prognostic prediction model for THCA. Enrichment analysis revealed that patients in the low-risk group exhibited significant enrichment in multiple immune-related pathways, such as T cell-mediated immune responses to tumor cells, and demonstrated stronger responsiveness to anti-CTLA-4 and anti-PD-1 immunotherapies compared to the high-risk group. Further analysis identified two distinct molecular subtypes of THCA: Group 2 exhibited upregulation of immune checkpoint molecules, elevated ESTIMATEScore and StromalScore, and lower TumorPurity. Conclusion This study adopts the unique perspective of MMRGs to elucidate their pivotal role and molecular basis within the THCA tumor microenvironment, offering novel insights for deepening our understanding of the disease’s pathogenesis and developing innovative therapeutic strategies.

Development of a prognostic model for thyroid cancer based on mitochondrial metabolism-related genes and immune profiling

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2026年1月6日

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Guorui Wang,
Qinchao Feng,
Guohua Zhu

摘要

Background

The preliminary study found that mitochondrial metabolism and structure are abnormal in thyroid cancer (THCA) patients. Therefore, this study systematically investigates the relationship between mitochondrial metabolism-related genes (MMRGs) and the prognosis of THCA patients, while establishing a prognostic model for THCA.

Methods

This study utilized THCA transcriptome data from the UCSC Xena database, performed differential expression analysis using the “limma” package, and intersected differentially expressed genes (DEGs) with MMRG to identify differentially expressed MMRGs (DEMMRGs). THCA prognostic genes were identified using the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis, and a prognostic model was constructed. Using ssGSEA, CIBERSORT and Immune Phenotype Score methods, we compared differences in immune cell infiltration levels and anti-tumor immune response capacity between distinct risk groups. Furthermore, molecular subtypes of THCA were identified through consensus clustering analysis.

Results

This study systematically identified nine MMRGs to construct a robust prognostic prediction model for THCA. Enrichment analysis revealed that patients in the low-risk group exhibited significant enrichment in multiple immune-related pathways, such as T cell-mediated immune responses to tumor cells, and demonstrated stronger responsiveness to anti-CTLA-4 and anti-PD-1 immunotherapies compared to the high-risk group. Further analysis identified two distinct molecular subtypes of THCA: Group 2 exhibited upregulation of immune checkpoint molecules, elevated ESTIMATEScore and StromalScore, and lower TumorPurity.

Conclusion

This study adopts the unique perspective of MMRGs to elucidate their pivotal role and molecular basis within the THCA tumor microenvironment, offering novel insights for deepening our understanding of the disease’s pathogenesis and developing innovative therapeutic strategies.