A Six-Gene Mitochondrial Signature Predicts Prognosis in Dedifferentiated Thyroid Cancer.

Abstract

Background: Dedifferentiated thyroid cancer (DDTC) is a highly malignant, infiltrative neoplasm. Studies confirm mitochondrial (MD) dysfunction links to DDTC's aggressiveness and treatment resistance via regulating energy metabolism reprogramming, exacerbating oxidative stress, and mediating apoptotic resistance. This study explored the role of MD related genes (MDRGs) in DDTC prognosis to inform prognostic evaluation and targeted therapy.

Methods: In this study, DDTC prognostic genes were identified through integrated analyses, including differential analysis, weighted gene co-expression network analysis (WGCNA), univariate Cox regression, and machine Learning. Subsequently, risk model and nomogram were constructed. Functional enrichment analysis explored prognosis-related pathways, while immune infiltration analysis revealed distinct immune cell infiltration patterns between high- and low-risk groups. Additionally, drug sensitivity analysis and drug prediction identified potential therapeutic targets for DDTC. Finally, prognostic gene expression was validated using quantitative real-time polymerase chain reaction (qRT-PCR).

Results: A total of 6 MDRGs were obtained. Both risk model and nomogram demonstrated robust predictive performance. Furthermore, high-risk group was closely associated with DDTC prognosis, particularly involving pathways such as cytokine receptor interaction and biological processes like cytokine activity, which were related to immune regulation and emerged as critical drivers of DDTC progression. The 4 immune cells between the high- and low-risk groups were markedly different, such as activated CD4 memory T cells and plasma cells. Drug sensitivity analysis indicated that 40 and 27 medications sensitive to high- and low-risk groups, respectively. A total of 41 drugs were predicted to have potential therapeutic effects, including omarigliptin, bepridil and bortezomib. Finally, qRT-PCR validation demonstrated that SLC26A4, KCNQ1, PMAIP1, DPP4, and NOX4 had expression trends consistent with public database results.

Conclusion: This study identified 6 MDRGs (SLC26A4, SLC25A37, KCNQ1, PMAIP1, DPP4 and NOX4) associated with the prognosis of DDTC, providing valuable scientific insights and references for the targeted therapy and patient stratification of DDTC.