A circadian rhythm-related thyroid cancer model highlighted COL18A1, DPP4, and APOE and predicted possible melatonin-linked targets.
Evidence
This bioinformatics and network pharmacology analysis used thyroid cancer gene expression, prognostic modeling, immune infiltration analysis, database target screening, topology analysis, and molecular docking.
Caveat
The melatonin intervention findings are computational target and docking predictions rather than clinical or experimental treatment evidence.
Simplified
Thyroid cancer (THCA) patients may be affected by (CRD). Since melatonin (MT) has both antitumor and regulatory effects on CRD, this study aimed to evaluate the functional role and potential mechanism of action of MT as a therapeutic agent against THCA and regulation of CRD. The intersection between of THCA and circadian rhythm-related genes (CRGs) was identified and hub genes were further screened to construct a prognostic model. The relationship between the expression of THCA/CRGs and immune infiltration was evaluated in the low-risk and high-risk groups. The molecular targets of MT acting on THCA/CRGs were screened and topological analysis was performed. Multivariate Cox regression analysis identified 3 core genes, COL18A1, DPP4, and APOE, to construct the prognostic model. The clinical information analysis and immunohistochemical analysis of core genes showed that in THCA, COL18A1 was downregulated (P < .05), while DPP4 and APOE were upregulated (P < .05). Subgroup analysis showed that COL18A1, DPP4, and APOE may be associated with different factors in different subgroups. The results of immune infiltration analysis showed that compared with the low-risk group, the high-risk group had higher stromal score (P < .001), lower immune score (P < .001), and ESTIMATE score (P < .001). Additionally, the expression of THCA/CRGs was closely associated with the infiltration of various immune cells. A total of 25 molecular targets of MT against THCA/CRD were identified by online databases. Topological analysis identified 6 molecular targets with the best performance, including LGALS3, MMP9, CTSB, DPP4, PPARG, and ALB, with binding energy <-5 kcal/mol for molecular docking with MT. The prediction model constructed in this study shows good diagnostic and prognostic performance. In addition, this study revealed the pharmacological targets of MT against THCA/CRD, providing a potential theoretical basis for exploring new clinical treatment options.
Key numbers
0.812
at 1 year
Area under the curve for the risk model at 1 year.
0.819
at 2 years
Area under the curve for the risk model at 2 years.
0.854
at 3 years
Area under the curve for the risk model at 3 years.
Full Text
We can’t show the full text here under this license.
