Establishment and analysis of a disease risk prediction model for the systemic lupus erythematosus with random forest

Abstract

Systemic lupus erythematosus (SLE) is a latent, insidious autoimmune disease, and with the development of gene sequencing in recent years, our study aims to develop a gene-based predictive model to explore the identification of SLE at the genetic level. First, gene expression datasets of SLE whole blood samples were collected from the Gene Expression Omnibus (GEO) database. After the datasets were merged, they were divided into training and validation datasets in the ratio of 7:3, where the SLE samples and healthy samples of the training dataset were 334 and 71, respectively, and the SLE samples and healthy samples of the validation dataset were 143 and 30, respectively. The training dataset was used to build the disease risk prediction model, and the validation dataset was used to verify the model identification ability. We first analyzed differentially expressed genes (DEGs) and then used Lasso and random forest (RF) to screen out six key genes (OAS3, USP18, RTP4, SPATS2L, IFI27 and OAS1), which are essential to distinguish SLE from healthy samples. With six key genes incorporated and five iterations of 10-fold cross-validation performed into the RF model, we finally determined the RF model with optimal mtry. The mean values of area under the curve (AUC) and accuracy of the models were over 0.95. The validation dataset was then used to evaluate the AUC performance and our model had an AUC of 0.948. An external validation dataset (GSE99967) with an AUC of 0.810, an accuracy of 0.836, and a sensitivity of 0.921 was used to assess the model’s performance. The external validation dataset (GSE185047) of all SLE patients yielded an SLE sensitivity of up to 0.954. The final high-throughput RF model had a mean value of AUC over 0.9, again showing good results. In conclusion, we identified key genetic biomarkers and successfully developed a novel disease risk prediction model for SLE that can be used as a new SLE disease risk prediction aid and contribute to the identification of SLE.

Development and evaluation of machine learning models and nomogram for the prediction of severe acute pancreatitis

“…Therefore, in future research on the SAP prediction model, more dimensional variables can be easily incorporated into the RF model, keeping the SAP prediction model up-to-date and improving its prediction performance. Similar to the amount of data in our study, Chen et al 32 established and analyzed an RF-based disease risk prediction model for the systemic lupus erythematosus, with the training and validation datasets 405 and 173, respectively. Taken together, the RF model can be used as a new auxiliary tool for disease risk prediction in clinical application and contribute to the early identification of diseases.…”

Use of Machine Learning for the Identification and Validation of Immunogenic Cell Death Biomarkers and Immunophenotypes in Coronary Artery Disease

“…The level of immune cell infiltration varies significantly between samples. 56 OAS3 is involved in immune and inflammatory responses in COVID-19, 57 , 58 tuberculosis, 59 , 60 psoriasis, 61 systemic lupus erythematosus, 62 , 63 and other diseases. OAS3 increases the IFNαβ signaling and the secretion of pro-inflammatory cytokines by inducing apoptosis, regulating immune cell receptors, and autophagy, primarily via the production of type I interferon, IL10, and CXCL2.…”

Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning

“…IFI27, in particular, has been associated with type I interferon-induced apoptosis [ 49 ], and may have potential as a classification marker or immunotherapeutic target for SLE [ 50 – 52 ]. FAM210B and LYRM7 have important roles in regulating mitochondrial energy metabolism and the stability of mitochondrial accessory factors, respectively [ 53 , 54 ].…”