Effects of RETN polymorphisms on treatment response in rheumatoid arthritis patients receiving TNF-α inhibitors and utilization of machine-learning algorithms

초록

This study was designed to investigate the effects of polymorphisms in RETN on remission in RA patients receiving TNF-alpha inhibitors. In addition, machine learning algorithms were trained to predict remission. Ten single-nucleotide polymorphisms were investigated. Univariate and multivariable analyses were performed to evaluate associations between genetic polymorphisms and the efficacy of TNF-alpha inhibitors. A random forest --based classification approach was used to assess the importance of different variables associated with the ef-ficacy of TNF-alpha inhibitors. Various machine learning methods were used for finding vital factors and prediction of remission. The eight most significant features included in the multivariable analysis were sex, age, hyper-tension, sulfasalazine, rs1862513, rs3219178, rs3219177, and rs3745369. T-allele carriers of rs3219177 and males showed approximately 6.0-and 3.6-fold higher remission rates compared to those with the CC genotype and females, respectively. The elastic net algorithm was the best machine-learning method for predicting remission of patients with RA treated with TNF-alpha inhibitors. On the basis of the results of this study, it may be possible to design individually tailored treatment regimens to predict the efficacy of TNF-alpha inhibitors.

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