Fund programs: National Natural Science Foundation of China (No . 82074090) ; Natural Science Research Pro- ject of Anhui Educational Committee (Nos . 2024AH052061 , 2024AH040154)
Authors：Tang Ran1 , 2 , Jiang Gege1 , 2 , Meng Xiangwen1 , 2 , Cai Zheng1 , 2 , Jin Li3 , Xiang Nan3 , Zhang Min3 , Jia Xiaoyi 1 , 2
Keywords：systemic lupus erythematosus; machine learning; bioinformatics; HERC5; interferon pathway; biomarker;
DOI：10.19405/j.cnki.issn1000-1492.2025.12.022
〔Abstract〕 Abstract Objective To predict and screen potential biomarkers of systemic lupus eythematosus (SLE) based on machine learning algorithms and structural biology , and to reveal their mechanisms of action and to provide new tar⁃gets for disease diagnosis and treatment. Methods Four machine learning algorithms , random forest ( RF) , eX⁃treme gradient boosting (XGBoost) , support vector machine (SVM) , least absolute shrinkage and selection opera⁃tor (LASSO) , were used to analyze the gene expression data of SLE patients in GEO (datasets : GSE121239 and GSE11907) to analyze the gene expression data of SLE patients and screen key markers. Peripheral blood single nucleated cells (PBMCs) from SLE patients were collected and RT⁃qPCR was used to detect differential gene ex⁃pression levels. Subsequently , GSEA enrichment analysis was used to identify biomarker⁃related pathways. CIBER⁃SORT immune infiltration analysis and protein interactions network were applied to calculate the sample immune cell infiltration abundance. Single⁃cell data were analyzed for gene expression specificity in immune cells. Interac⁃tion relationships in combination with AlphaFold3 ( AF3 ) were predicted. Results Multiple algorithms were screened together to identify the unique marker gene HERC5 , and expression analysis of multiple datasets showed that HERC5 was highly expressed in SLE compared to the normal group (P < 0. 05) , and RT⁃qPCR verified the same trend (P = 0. 006 2) . Functional enrichment analysis identified the major pathway promoted by HERC5 in SLE as the interferon receptor signalling pathway (P < 0. 05) . Immune infiltration analysis showed that HERC5 was closely associated with immune cells (Neutrophils : r = 0. 39 , P < 0. 05 ; Memory B cells : r = 0. 33 , P < 0. 05 ; Ac⁃and potential transcription factors of HERC5 and its related genes were also significantly associated with immune re⁃sponses. Conclusion The HERC5 gene is an important biomarker for SLE , which upregulates the interferon path⁃way to promote SLE progression and provides a new target for SLE diagnosis and treatment.