关键词: 强直性脊柱炎, 银屑病, 富集分析, 机器学习, 人工神经网络, 交叉验证, 列线图, 免疫细胞浸润, 孟德尔随机化, 药物预测

Abstract: BACKGROUND: Ankylosing spondylitis is closely related to the occurrence and development of psoriasis, but the key genes and regulatory mechanisms are still unclear.
OBJECTIVE: To establish an artificial neural network model of genes shared by ankylosing spondylitis and psoriasis based on the GEO database and evaluate its effect, and also to determine whether there is a causal relationship between the expression of key genes and the two diseases using Mendelian randomization. 
METHODS: Datasets GSE25101 (816 ankylosing spondylitis samples and 816 healthy control samples), GSE30999 (85 psoriasis samples and 85 healthy control samples), GSE73754 (52 ankylosing spondylitis samples and 20 healthy control samples), and GSE14905 (33 psoriasis samples and 49 healthy control samples) were downloaded from the GEO database. GSE25101 and GSE30999 were used as the training datasets of ankylosing spondylitis and psoriasis, respectively, and their respective differentially expressed genes were identified through difference analysis to obtain the common driver genes of the two diseases, and the key core genes were further screened out based on Mendelian randomization. The key core genes were further screened out, and artificial neural network models were constructed based on the key core genes and validated in external datasets GSE73754 and GSE14905, followed by the construction of the corresponding nomogram to predict the incidence rates of the diseases. Also, the results of immune infiltration in ankylosing spondylitis and psoriasis were analyzed. Finally, Mendelian randomization was used to assess causal relationships between key genes and diseases, and drug-gene interactions were analyzed using the Dgidb database to predict drug targets. 
RESULTS AND CONCLUSION: (1) A total of 61 differential genes were obtained in ankylosing spondylitis and 4 309 differential genes were obtained in psoriasis. Eight shared differential genes were obtained after intersection, and five key genes (DNMT1, GNG11, CDC25B, S100A8, and S100A12) were further screened by machine learning. The key genes were utilized to build artificial neural network models of ankylosing spondylitis and psoriasis, with the area under curve values of 0.979 and 0.989 in the training sets GSE25101 and GSE30999, respectively, and 0.818 and 0.874 in the external validation datasets GSE73754 and GSE14905, respectively. (2) Nomogram was constructed based on the five core genes, and the calibration curves showed that the predicted probabilities of the nomogram models were almost the same as that of the ideal model. Immune cell infiltration showed that the key genes were associated with activated B cells, natural killer cells, γδ T cells, follicular helper T cells, monocytes, plasma cell-like dendritic cells, and neutrophils. Mendelian randomization showed that S100A8 was a risk factor for the occurrence of ankylosing spondylitis and psoriasis. Finally, DGIdb screening was utilized to obtain 81 targeted drugs, only 16 of which, including methotrexate, atogepant, ubrogepant, rimegepant, eptinezumab, azacitidine, selenium, hydroxyurea, ifosfamide, floxuridine, curcumin, mitoxantrone, cisplatin, arsenic trioxide, diethylstilbestrol, and decitabine, were approved by the U.S. Food and Drug Administration. (3) A large number of successful cases have been accumulated in international databases, research results and data analysis of European groups, especially in genomics and disease phenotyping studies. These experiences provide valuable references for the epidemiological characterization of diseases in China, genetic diversity and their response to the environment and lifestyle. (4) An artificial neural network model of the common driver genes of ankylosing spondylitis and psoriasis was constructed and validated, the causal relationship between the key genes and the pathogenesis of the two diseases was discovered, and the targeted drugs for potential treatments were predicted, which hopefully provides a new perspective for exploring their pathogenesis and therapeutic directions.

Key words: ankylosing spondylitis, psoriasis, enrichment analysis, machine learning, artificial neural network, cross-validation, nomogram, immune cell infiltration, Mendelian randomization, drug prediction