An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosus
Abstract Background Childhood systemic lupus erythematosus (cSLE) is a multisystemic, life-threatening autoimmune disease. Compared to adults, SLE in childhood is more active, can cause multisystem involvement including renal, neurological and hematological, and can cause cumulative damage across sy...
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| Format: | Article |
| Language: | English |
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BMC
2022-09-01
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| Series: | Hereditas |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41065-022-00248-7 |
| _version_ | 1811267686754156544 |
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| author | Jinting Wu Wenxian Yang Huihui Li |
| author_facet | Jinting Wu Wenxian Yang Huihui Li |
| author_sort | Jinting Wu |
| collection | DOAJ |
| description | Abstract Background Childhood systemic lupus erythematosus (cSLE) is a multisystemic, life-threatening autoimmune disease. Compared to adults, SLE in childhood is more active, can cause multisystem involvement including renal, neurological and hematological, and can cause cumulative damage across systems more rapidly. Autophagy, one of the core functions of cells, is involved in almost every process of the immune response and has been shown to be associated with many autoimmune diseases, being a key factor in the interplay between innate and adaptive immunity. Autophagy influences the onset, progression and severity of SLE. This paper identifies new biomarkers for the diagnosis and treatment of childhood SLE based on an artificial neural network of autophagy-related genes. Methods We downloaded dataset GSE100163 from the Gene Expression Omnibus database and used Protein–protein Interaction Network (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO) to screen the signature genes of autophagy-related genes in cSLE. A new artificial neural network model for cSLE diagnosis was constructed using the signature genes. The predictive efficiency of the model was also validated using the dataset GSE65391. Finally, "CIBERSORT" was used to calculate the infiltration of immune cells in cSLE and to analyze the relationship between the signature genes and the infiltration of immune cells. Results We identified 37 autophagy-related genes that differed in cSLE and normal samples, and finally obtained the seven most relevant signature genes for cSLE (DDIT3, GNB2L1, CTSD, HSPA8, ULK1, DNAJB1, CANX) by PPI and LASOO regression screening, and constructed an artificial neural network diagnostic model for cSLE. Using this model, we plotted the ROC curves for the training and validation group diagnoses with the area under the curve of 0.976 and 0.783, respectively. Finally, we performed immunoassays on cSLE samples, and the results showed that Plasma cells, Macrophages M0, Dendritic cells activated and Neutrophils were significantly infiltrated in cSLE. Conclusion We constructed an artificial neural network diagnostic model of seven autophagy-related genes that can be used for the diagnosis of cSLE. Meanwhile, the characteristic genes affect the immune infiltration of cSLE, which may provide new perspectives for the exploration of cSLE treatment and related mechanisms. |
| first_indexed | 2024-04-12T21:07:03Z |
| format | Article |
| id | doaj.art-4be61cac371a49b791f99d0f7189f99e |
| institution | Directory Open Access Journal |
| issn | 1601-5223 |
| language | English |
| last_indexed | 2024-04-12T21:07:03Z |
| publishDate | 2022-09-01 |
| publisher | BMC |
| record_format | Article |
| series | Hereditas |
| spelling | doaj.art-4be61cac371a49b791f99d0f7189f99e2022-12-22T03:16:40ZengBMCHereditas1601-52232022-09-01159111110.1186/s41065-022-00248-7An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosusJinting Wu0Wenxian Yang1Huihui Li2Department of Oncology, The First Affiliated Hospital of Wenzhou Medical UniversityPediatrics, Shaoxing University School of MedicineDepartment of Breast Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityAbstract Background Childhood systemic lupus erythematosus (cSLE) is a multisystemic, life-threatening autoimmune disease. Compared to adults, SLE in childhood is more active, can cause multisystem involvement including renal, neurological and hematological, and can cause cumulative damage across systems more rapidly. Autophagy, one of the core functions of cells, is involved in almost every process of the immune response and has been shown to be associated with many autoimmune diseases, being a key factor in the interplay between innate and adaptive immunity. Autophagy influences the onset, progression and severity of SLE. This paper identifies new biomarkers for the diagnosis and treatment of childhood SLE based on an artificial neural network of autophagy-related genes. Methods We downloaded dataset GSE100163 from the Gene Expression Omnibus database and used Protein–protein Interaction Network (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO) to screen the signature genes of autophagy-related genes in cSLE. A new artificial neural network model for cSLE diagnosis was constructed using the signature genes. The predictive efficiency of the model was also validated using the dataset GSE65391. Finally, "CIBERSORT" was used to calculate the infiltration of immune cells in cSLE and to analyze the relationship between the signature genes and the infiltration of immune cells. Results We identified 37 autophagy-related genes that differed in cSLE and normal samples, and finally obtained the seven most relevant signature genes for cSLE (DDIT3, GNB2L1, CTSD, HSPA8, ULK1, DNAJB1, CANX) by PPI and LASOO regression screening, and constructed an artificial neural network diagnostic model for cSLE. Using this model, we plotted the ROC curves for the training and validation group diagnoses with the area under the curve of 0.976 and 0.783, respectively. Finally, we performed immunoassays on cSLE samples, and the results showed that Plasma cells, Macrophages M0, Dendritic cells activated and Neutrophils were significantly infiltrated in cSLE. Conclusion We constructed an artificial neural network diagnostic model of seven autophagy-related genes that can be used for the diagnosis of cSLE. Meanwhile, the characteristic genes affect the immune infiltration of cSLE, which may provide new perspectives for the exploration of cSLE treatment and related mechanisms.https://doi.org/10.1186/s41065-022-00248-7AutophagyArtificial neural networkcSLEImmune cell infiltration |
| spellingShingle | Jinting Wu Wenxian Yang Huihui Li An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosus Hereditas Autophagy Artificial neural network cSLE Immune cell infiltration |
| title | An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosus |
| title_full | An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosus |
| title_fullStr | An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosus |
| title_full_unstemmed | An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosus |
| title_short | An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosus |
| title_sort | artificial neural network model based on autophagy related genes in childhood systemic lupus erythematosus |
| topic | Autophagy Artificial neural network cSLE Immune cell infiltration |
| url | https://doi.org/10.1186/s41065-022-00248-7 |
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