Identification of the Hub Genes Involved in Stem Cell Treatment for Intervertebral Disc Degeneration: A Conjoint Analysis of Single-Cell and Machine Learning
Intervertebral disc degeneration (IDD), which is distinguished by a variety of pathologic alterations, is the major cause of low back pain (LBP). Nonetheless, preventative measures or therapies that may delay IDD are scarcely available. In this study, we sought to identify new diagnostic biological...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Stem Cells International |
| Online Access: | http://dx.doi.org/10.1155/2023/7055264 |
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| author | Jianfeng Chen Fuwei Qi Guanshen Li Qiaosong Deng Chenlin Zhang Xiaojun Li Yafeng Zhang |
| author_facet | Jianfeng Chen Fuwei Qi Guanshen Li Qiaosong Deng Chenlin Zhang Xiaojun Li Yafeng Zhang |
| author_sort | Jianfeng Chen |
| collection | DOAJ |
| description | Intervertebral disc degeneration (IDD), which is distinguished by a variety of pathologic alterations, is the major cause of low back pain (LBP). Nonetheless, preventative measures or therapies that may delay IDD are scarcely available. In this study, we sought to identify new diagnostic biological markers for IDD. In this first-of-a-kind study combining machine learning, stem cell treatment samples and single-cell sequencing data were collected. Differentially expressed genes (DEGs) were detected from the treatment group and clusters. To filter potential markers, support vector machine analysis and LASSO were performed. LAPTM5 was found to be the hub gene for IDD. In addition, the results of single-cell sequencing demonstrated the critical function of stem cells in IDD. Finally, we found that aging is significantly associated with the rate of stem cells. In general, our results may offer fresh insights that may be used in the investigation of innovative markers for diagnosing IDD. The critical genes identified by the machine learning algorithm could provide new perspectives on IDD. |
| format | Article |
| id | doaj-art-2c6c4b15b94a434ab878a719c2958f09 |
| institution | Kabale University |
| issn | 1687-9678 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Stem Cells International |
| spelling | doaj-art-2c6c4b15b94a434ab878a719c2958f092025-02-03T06:08:39ZengWileyStem Cells International1687-96782023-01-01202310.1155/2023/7055264Identification of the Hub Genes Involved in Stem Cell Treatment for Intervertebral Disc Degeneration: A Conjoint Analysis of Single-Cell and Machine LearningJianfeng Chen0Fuwei Qi1Guanshen Li2Qiaosong Deng3Chenlin Zhang4Xiaojun Li5Yafeng Zhang6Department of SpineDepartment of AnesthesiologyDepartment of SpineDepartment of SpineDepartment of OrthopaedicsDepartment of SpineDepartment of SpineIntervertebral disc degeneration (IDD), which is distinguished by a variety of pathologic alterations, is the major cause of low back pain (LBP). Nonetheless, preventative measures or therapies that may delay IDD are scarcely available. In this study, we sought to identify new diagnostic biological markers for IDD. In this first-of-a-kind study combining machine learning, stem cell treatment samples and single-cell sequencing data were collected. Differentially expressed genes (DEGs) were detected from the treatment group and clusters. To filter potential markers, support vector machine analysis and LASSO were performed. LAPTM5 was found to be the hub gene for IDD. In addition, the results of single-cell sequencing demonstrated the critical function of stem cells in IDD. Finally, we found that aging is significantly associated with the rate of stem cells. In general, our results may offer fresh insights that may be used in the investigation of innovative markers for diagnosing IDD. The critical genes identified by the machine learning algorithm could provide new perspectives on IDD.http://dx.doi.org/10.1155/2023/7055264 |
| spellingShingle | Jianfeng Chen Fuwei Qi Guanshen Li Qiaosong Deng Chenlin Zhang Xiaojun Li Yafeng Zhang Identification of the Hub Genes Involved in Stem Cell Treatment for Intervertebral Disc Degeneration: A Conjoint Analysis of Single-Cell and Machine Learning Stem Cells International |
| title | Identification of the Hub Genes Involved in Stem Cell Treatment for Intervertebral Disc Degeneration: A Conjoint Analysis of Single-Cell and Machine Learning |
| title_full | Identification of the Hub Genes Involved in Stem Cell Treatment for Intervertebral Disc Degeneration: A Conjoint Analysis of Single-Cell and Machine Learning |
| title_fullStr | Identification of the Hub Genes Involved in Stem Cell Treatment for Intervertebral Disc Degeneration: A Conjoint Analysis of Single-Cell and Machine Learning |
| title_full_unstemmed | Identification of the Hub Genes Involved in Stem Cell Treatment for Intervertebral Disc Degeneration: A Conjoint Analysis of Single-Cell and Machine Learning |
| title_short | Identification of the Hub Genes Involved in Stem Cell Treatment for Intervertebral Disc Degeneration: A Conjoint Analysis of Single-Cell and Machine Learning |
| title_sort | identification of the hub genes involved in stem cell treatment for intervertebral disc degeneration a conjoint analysis of single cell and machine learning |
| url | http://dx.doi.org/10.1155/2023/7055264 |
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