Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1
Xiangnan Zhou,1,2,* Jingyuan Ning,3,* Rui Cai,2 Jiayi Liu,2 Haoyu Yang,4 Yanping Bai1 1Department of Dermatology, China-Japan Friendship Hospital, National Center for Integrative Medicine, Beijing, 100029, People’s Republic of China; 2Beijing University of Chinese Medicine, China-Jap...
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Dove Medical Press
2024-12-01
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| author | Zhou X Ning J Cai R Liu J Yang H Bai Y |
| author_facet | Zhou X Ning J Cai R Liu J Yang H Bai Y |
| author_sort | Zhou X |
| collection | DOAJ |
| description | Xiangnan Zhou,1,2,* Jingyuan Ning,3,* Rui Cai,2 Jiayi Liu,2 Haoyu Yang,4 Yanping Bai1 1Department of Dermatology, China-Japan Friendship Hospital, National Center for Integrative Medicine, Beijing, 100029, People’s Republic of China; 2Beijing University of Chinese Medicine, China-Japan Friendship Clinical School of Medicine, Beijing, 100029, People’s Republic of China; 3State Key Laboratory of Medical Molecular Biology & Department of Medical Genetics, Institute of Basic Medical Sciences & School of Basic Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, People’s Republic of China; 4Department of Dermatology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, People’s Republic of China*These authors contributed equally to this work: Xiangnan Zhou, Jingyuan NingCorrespondence: Yanping Bai, Department of Dermatology, China-Japan Friendship Hospital, Beijing, 100029, People’s Republic of China, Email yanpbcjfh@163.comBackground: Psoriasis represents a persistent, immune-driven inflammatory condition affecting the skin, characterized by a lack of well-established biologic treatments without adverse events. Consequently, the identification of novel targets and therapeutic agents remains a pressing priority in the field of psoriasis research.Methods: We collected single-cell RNA sequencing (scRNA-seq) datasets and inferred T cell differentiation trajectories through pseudotime analysis. Bulk transcriptome and scRNA-seq data were integrated to identify differentially expressed genes (DEGs). Machine learning was employed to screen candidate genes. Correlation analysis was used to predict the interactions between cells expressing insulin-induced gene 1 (INSIG1) and other immune cells. Finally, drug docking was performed on INSIG1, and the expression levels of INSIG1 in psoriasis were verified through clinical and in vivo experiments, and further in vivo experiments established the efficacy of tetrandrine in the treatment of psoriasis.Results: T cells were initially categorized into seven states, with differentially expressed genes in T cells (TDEGs) identified and their functions and signaling pathways. INSIG1 emerged as a characteristic gene for psoriasis and was found to be downregulated in psoriasis and potentially negatively associated with T cells, influencing psoriasis fatty acid metabolism, as inferred from enrichment and immunoinfiltration analyses. In the cellular communication network, cells expressing INSIG1 exhibited close interactions with other immune cells through multiple signaling channels. Furthermore, drug sensitivity showed that tetrandrine stably binds to INSIG1, could be a potential therapeutic agent for psoriasis.Conclusion: INSIG1 emerges as a specific candidate gene potentially regulating the fatty acid metabolism of patients with psoriasis. In addition, tetrandrine shows promise as a potential treatment for the condition.Keywords: psoriasis, single-cell RNA sequencing, machine learning, INSIG1, pseudotime analysis |
| format | Article |
| id | doaj-art-a30713abe7da4bb4a99d94aa6ad64a44 |
| institution | DOAJ |
| issn | 1178-7031 |
| language | English |
| publishDate | 2024-12-01 |
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| spelling | doaj-art-a30713abe7da4bb4a99d94aa6ad64a442025-08-20T02:53:16ZengDove Medical PressJournal of Inflammation Research1178-70312024-12-01Volume 17114851150398694Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1Zhou XNing JCai RLiu JYang HBai YXiangnan Zhou,1,2,* Jingyuan Ning,3,* Rui Cai,2 Jiayi Liu,2 Haoyu Yang,4 Yanping Bai1 1Department of Dermatology, China-Japan Friendship Hospital, National Center for Integrative Medicine, Beijing, 100029, People’s Republic of China; 2Beijing University of Chinese Medicine, China-Japan Friendship Clinical School of Medicine, Beijing, 100029, People’s Republic of China; 3State Key Laboratory of Medical Molecular Biology & Department of Medical Genetics, Institute of Basic Medical Sciences & School of Basic Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100005, People’s Republic of China; 4Department of Dermatology, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, People’s Republic of China*These authors contributed equally to this work: Xiangnan Zhou, Jingyuan NingCorrespondence: Yanping Bai, Department of Dermatology, China-Japan Friendship Hospital, Beijing, 100029, People’s Republic of China, Email yanpbcjfh@163.comBackground: Psoriasis represents a persistent, immune-driven inflammatory condition affecting the skin, characterized by a lack of well-established biologic treatments without adverse events. Consequently, the identification of novel targets and therapeutic agents remains a pressing priority in the field of psoriasis research.Methods: We collected single-cell RNA sequencing (scRNA-seq) datasets and inferred T cell differentiation trajectories through pseudotime analysis. Bulk transcriptome and scRNA-seq data were integrated to identify differentially expressed genes (DEGs). Machine learning was employed to screen candidate genes. Correlation analysis was used to predict the interactions between cells expressing insulin-induced gene 1 (INSIG1) and other immune cells. Finally, drug docking was performed on INSIG1, and the expression levels of INSIG1 in psoriasis were verified through clinical and in vivo experiments, and further in vivo experiments established the efficacy of tetrandrine in the treatment of psoriasis.Results: T cells were initially categorized into seven states, with differentially expressed genes in T cells (TDEGs) identified and their functions and signaling pathways. INSIG1 emerged as a characteristic gene for psoriasis and was found to be downregulated in psoriasis and potentially negatively associated with T cells, influencing psoriasis fatty acid metabolism, as inferred from enrichment and immunoinfiltration analyses. In the cellular communication network, cells expressing INSIG1 exhibited close interactions with other immune cells through multiple signaling channels. Furthermore, drug sensitivity showed that tetrandrine stably binds to INSIG1, could be a potential therapeutic agent for psoriasis.Conclusion: INSIG1 emerges as a specific candidate gene potentially regulating the fatty acid metabolism of patients with psoriasis. In addition, tetrandrine shows promise as a potential treatment for the condition.Keywords: psoriasis, single-cell RNA sequencing, machine learning, INSIG1, pseudotime analysishttps://www.dovepress.com/single-cell-sequencing-and-machine-learning-integration-to-identify-ca-peer-reviewed-fulltext-article-JIRpsoriasissingle cell rna sequencingmachine learninginsig1pseudotime analysis |
| spellingShingle | Zhou X Ning J Cai R Liu J Yang H Bai Y Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1 Journal of Inflammation Research psoriasis single cell rna sequencing machine learning insig1 pseudotime analysis |
| title | Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1 |
| title_full | Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1 |
| title_fullStr | Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1 |
| title_full_unstemmed | Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1 |
| title_short | Single-Cell Sequencing and Machine Learning Integration to Identify Candidate Biomarkers in Psoriasis: INSIG1 |
| title_sort | single cell sequencing and machine learning integration to identify candidate biomarkers in psoriasis insig1 |
| topic | psoriasis single cell rna sequencing machine learning insig1 pseudotime analysis |
| url | https://www.dovepress.com/single-cell-sequencing-and-machine-learning-integration-to-identify-ca-peer-reviewed-fulltext-article-JIR |
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