Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous clinical phenotypes. Patients often experience abnormal sensory perception, which may further affect the ASD core phenotype, significantly and adversely affecting their quality of life. However, biomarkers...
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| Format: | Article |
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MDPI AG
2023-01-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/3/2367 |
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| author | Zhe Ma Lisha Xu Qi Li Xiang Li Yaxin Shi Xirui Zhang Yuan Yang Jia Wang Lili Fan Lijie Wu |
| author_facet | Zhe Ma Lisha Xu Qi Li Xiang Li Yaxin Shi Xirui Zhang Yuan Yang Jia Wang Lili Fan Lijie Wu |
| author_sort | Zhe Ma |
| collection | DOAJ |
| description | Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous clinical phenotypes. Patients often experience abnormal sensory perception, which may further affect the ASD core phenotype, significantly and adversely affecting their quality of life. However, biomarkers for the diagnosis of ASD sensory perception abnormality are currently elusive. We sought to identify potential biomarkers related to ASD sensory perception abnormality to construct a prediction model that could facilitate the early identification of and screening for ASD. Differentially expressed genes in ASD were obtained from the Gene Expression Omnibus database and were screened for genes related to sensory perception abnormality. After enrichment analysis, the random forest method was used to identify disease-characteristic genes. A prediction model was constructed with an artificial neural network. Finally, the results were validated using data from the dorsal root ganglion, cerebral cortex, and striatum of the BTBR T+ Itpr3tf/J (BTBR) ASD mouse model. A total of 1869 differentially expressed genes in ASD were screened, among which 16 genes related to sensory perception abnormality were identified. According to enrichment analysis, these 16 genes were mainly related to actin, cholesterol metabolism, and tight junctions. Using random forest, 15 disease-characteristic genes were screened for model construction. The area under the curve of the training set validation result was 0.999, and for the model function validation, the result was 0.711, indicating high accuracy. The validation of BTBR mice confirmed the reliability of using these disease-characteristic genes for prediction of ASD. In conclusion, we developed a highly accurate model for predicting ASD sensory perception abnormality from 15 disease-characteristic genes. This model provides a new method for the early identification and diagnosis of ASD sensory perception abnormality. |
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| id | doaj.art-0f4d6e648e1149b0b04f9b9f55cae47e |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T09:41:30Z |
| publishDate | 2023-01-01 |
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| series | International Journal of Molecular Sciences |
| spelling | doaj.art-0f4d6e648e1149b0b04f9b9f55cae47e2023-11-16T16:56:13ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-01-01243236710.3390/ijms24032367Prediction Model for Sensory Perception Abnormality in Autism Spectrum DisorderZhe Ma0Lisha Xu1Qi Li2Xiang Li3Yaxin Shi4Xirui Zhang5Yuan Yang6Jia Wang7Lili Fan8Lijie Wu9Department of Children’s and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, ChinaDepartment of Children’s and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, ChinaDepartment of Children’s and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, ChinaDepartment of Children’s and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, ChinaDepartment of Children’s and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, ChinaDepartment of Children’s and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, ChinaDepartment of Children’s and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, ChinaDepartment of Children’s and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, ChinaDepartment of Children’s and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, ChinaDepartment of Children’s and Adolescent Health, Public Health College, Harbin Medical University, Harbin 150081, ChinaAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous clinical phenotypes. Patients often experience abnormal sensory perception, which may further affect the ASD core phenotype, significantly and adversely affecting their quality of life. However, biomarkers for the diagnosis of ASD sensory perception abnormality are currently elusive. We sought to identify potential biomarkers related to ASD sensory perception abnormality to construct a prediction model that could facilitate the early identification of and screening for ASD. Differentially expressed genes in ASD were obtained from the Gene Expression Omnibus database and were screened for genes related to sensory perception abnormality. After enrichment analysis, the random forest method was used to identify disease-characteristic genes. A prediction model was constructed with an artificial neural network. Finally, the results were validated using data from the dorsal root ganglion, cerebral cortex, and striatum of the BTBR T+ Itpr3tf/J (BTBR) ASD mouse model. A total of 1869 differentially expressed genes in ASD were screened, among which 16 genes related to sensory perception abnormality were identified. According to enrichment analysis, these 16 genes were mainly related to actin, cholesterol metabolism, and tight junctions. Using random forest, 15 disease-characteristic genes were screened for model construction. The area under the curve of the training set validation result was 0.999, and for the model function validation, the result was 0.711, indicating high accuracy. The validation of BTBR mice confirmed the reliability of using these disease-characteristic genes for prediction of ASD. In conclusion, we developed a highly accurate model for predicting ASD sensory perception abnormality from 15 disease-characteristic genes. This model provides a new method for the early identification and diagnosis of ASD sensory perception abnormality.https://www.mdpi.com/1422-0067/24/3/2367autism spectrum disorder (ASD)bioinformatical analysissensory perceptiondifferentially expressed genes (DEGs)prediction model |
| spellingShingle | Zhe Ma Lisha Xu Qi Li Xiang Li Yaxin Shi Xirui Zhang Yuan Yang Jia Wang Lili Fan Lijie Wu Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder International Journal of Molecular Sciences autism spectrum disorder (ASD) bioinformatical analysis sensory perception differentially expressed genes (DEGs) prediction model |
| title | Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder |
| title_full | Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder |
| title_fullStr | Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder |
| title_full_unstemmed | Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder |
| title_short | Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder |
| title_sort | prediction model for sensory perception abnormality in autism spectrum disorder |
| topic | autism spectrum disorder (ASD) bioinformatical analysis sensory perception differentially expressed genes (DEGs) prediction model |
| url | https://www.mdpi.com/1422-0067/24/3/2367 |
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