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Published on: December 16, 2022
A novel predictive model constructed based on the combination of SIX3OS1, miR-511-3p and RBP4 for stroke-prost
Te Wang1, Rui Wang2, Junsheng Zeng1
1Department of Neurology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha 410004 Hunan, China.
Background:
The incidence of stroke is increasing year by year. Post-stroke cognitive impairment (PSCI) is one of the most serious complications of stroke, which lacks effective early prediction tools.
Methods:
A total of 147 S patients and 80 healthy individuals were enrolled, with corresponding clinical data and serum samples collected. The expression of SIX3OS1, miR-511-3p and retinol binding protein 4 (RBP4) were detected by reverse transcription-quantitative PCR (RT-qPCR). These data were then used to build a logistic regression model, and receiver operating characteristic (ROC) curves were drawn to evaluate the clinical value of SIX3OS1, miR-511-3p and RBP4.
Results:
Our study found that SIX3OS1, miR-511-3p and RBP4 abnormally expressed in stroke and the area under the curve (AUC) of the combined detection of these was 0.965. Additionally, ROC curve showed that the AUC of SIX3OS1, miR-511-3p and RBP4 combined was 0.955 for the prediction of PSCI. Based on SIX3OS1 (X1), miR-511-3p (X2) and RBP4 (X3), we developed multivariate logistic regression predictive model, p = 1/ [1 + e (7.190-5.400X1 + 11.109X2-3.585X3)].
Conclusion:
Serum SIX3OS1, miR-511-3p and RBP4 are candidate diagnostic biomarker in stroke and PSCI patients, which achieve good diagnostic performance when used in combination with other factors, and may have the potential to be novel therapeutic targets for PSCI.
