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Adaptive regression algorithm was defined by adaptive regression analysis applying ΔCq values of 4 target genes and 5 reference genes. Generally, while data is processed based on a median or average of values selected by an array, in an algorithm according to the adaptive regression technique, a point having the largest variance of separated average interval values obtained when an arbitrary point of the total data is determined as a reference point is determined as an adaptive regression value. That is, the threshold value is a reference point that distinguishes high expression and low expression of a corresponding gene, which are biologically significant, in normal and cancer tissue.
The prognosis and predictive classification are performed by a binary signal based two tier system. The first tier uses two immune classifier genes (GZMB and WARS) to identify immune high patients. The second tier uses a stem-like classifier gene (SFRP4) or an epithelial classifier gene (CDX1) to classify other patients as stem-like high patients or epithelial high patients independently by using ST and EP classifiers, respectively.
First Tier: Classifying a group as a Low risk group and no-benefit group when ΔCq values of GZMB and WARS are higher than threshold values.
Second Tier-Prognosis: At least one ΔCq value of GZMB and WARS is lower than the threshold value, classifying a group as an Intermediate risk group when the ΔCq value of SFRP4 is lower than threshold value and a group as a High risk group when the ΔCq value of SFPR4 is higher than the threshold value.
Second Tier-Prediction: At least one ΔCq value of GZMB and WARS is lower than the threshold value, classifying a group as a no-benefit group when the ΔCq value of CDX1 is lower than threshold value and a group as a chemotherapy-benefit group when the ΔCq value of CDX1 is higher than the threshold value.
This binary signal based two tier system for prognosis and chemotherapy response prediction in gastric cancer is Single Patient Classifier (SPC).
Sahoo D, Dill DL, Tibshirani R, Plevritis SK. Extracting binary signals from microarray time-course data. Nucleic Acids Res. 2007;35(11):3705-12.