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Mechanism of action for Troxerutin targeting the sialylation-related gene EGLN3 for the treatment of LUAD
Mechanism of action for Troxerutin targeting the sialylation-related gene EGLN3 for the treatment of LUAD
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Mechanism of action for Troxerutin targeting the sialylation-related gene EGLN3 for the treatment of LUAD
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Mechanism of action for Troxerutin targeting the sialylation-related gene EGLN3 for the treatment of LUAD
Mechanism of action for Troxerutin targeting the sialylation-related gene EGLN3 for the treatment of LUAD

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Mechanism of action for Troxerutin targeting the sialylation-related gene EGLN3 for the treatment of LUAD
Mechanism of action for Troxerutin targeting the sialylation-related gene EGLN3 for the treatment of LUAD
Journal Article

Mechanism of action for Troxerutin targeting the sialylation-related gene EGLN3 for the treatment of LUAD

2025
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Overview
Studies have demonstrated that sialylation changes play a vital part in lung adenocarcinoma (LUAD), yet the specific mechanism is uncertain. Hence, in the present research, we screened sialylation-related biomarkers in LUAD using the bioinformatic strategy, predicted the drugs and performed relevant experiments to explore their role in regulating LUAD. The TCGA-LUAD, GSE31210, and GSE13213 datasets were combined to form LUAD ensemble. The sialylation-related genes (SRGs) linked with LUAD prognosis were determined by univariate Cox regression analysis, and their expressions and mutations in LUAD were analyzed in GSCA database. Then, depending on the consistent clustering of prognostic SRGs, LUAD patients were divided into sialylation-related subgroups, followed by the investigation of survival, immunity, and clinical characteristics in the subgroups. LASSO regression analysis was further employed to identify prognostic gene signatures and to build a sialylation-related model to predict the prognosis of LUAD patients. The gene signature were validated using RT-qPCR and used for predicting target medicines using molecular docking to further investigate the potential therapies for LUAD patients. A total of 26 SRGs in LUAD ensemble were associated with prognosis, and LUAD samples were classified into two sialylation-related subgroups based on these SRGs. Intergroup comparisons revealed that patients in Cluster A had greater survival rates, as well as higher immune infiltration. The risk prognostic model built based on 6 prognostic gene signature was able to effectively predict the survival of LUAD patients. Finally, the experimental findings indicated that Troxerutin exhibits a strong binding energy to the sialylation-related gene EGLN3, which could greatly reduce the growth of LUAD by inhibiting the expression of EGLN3, thus limiting the capacity of LUAD cells in the proliferation, migration, and invasion. Troxerutin could target and lower the expression of sialylation-related gene EGLN3, reducing LUAD cells’ ability to proliferate, migrate, and invade, making it an essential reference for LUAD prevention and treatment.