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Zhang X, Wang X, Wang S, Zhang Y, Wang Z, Yang Q, Wang S, Cao R, Yu B, Zheng Y, Dang Y. Machine learning algorithms assisted identification of post-stroke depression associated biological features. Front Neurosci 2023; 17:1146620. [PMID: 36968495 PMCID: PMC10030717 DOI: 10.3389/fnins.2023.1146620] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
ObjectivesPost-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.MethodsExpression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.ResultsFunctional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P < 0.001) and FERM3 (r = 0.728, P < 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.ConclusionA total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.
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Affiliation(s)
- Xintong Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Xiangyu Wang
- Department of Rehabilitation Medicine, The Affiliated Lianyungang Oriental Hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, China
| | - Shuwei Wang
- Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China
| | - Yingjie Zhang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Zeyu Wang
- Department of Rehabilitation Medicine, Shanghai Ruijin Rehabilitation Hospital, Shanghai, China
| | - Qingyan Yang
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
| | - Song Wang
- Department of Neurological Rehabilitation, Wuxi Yihe Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Risheng Cao
- Department of Science and Technology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Risheng Cao,
| | - Binbin Yu
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Binbin Yu,
| | - Yu Zheng
- Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- Yu Zheng,
| | - Yini Dang
- Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
- *Correspondence: Yini Dang,
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Liu X, Bai M, Li H, Ye P, Duan X, Wu C, Huang Z, Lu S, Zhang J, Zhao Z, Guo F, You R, Qin W, Wang W, Han A, Shen L, Wang Y, Zhao Z, Luo H, Wu J. Single-cell RNA-sequencing uncovers compound kushen injection synergistically improves the efficacy of chemotherapy by modulating the tumor environment of breast cancer. Front Immunol 2022; 13:965342. [DOI: 10.3389/fimmu.2022.965342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundDue to lack of enough specific targets and the immunosuppressive tumor microenvironment (TME) of triple-negative breast cancer (TNBC), TNBC patients often cannot benefit from a single treatment option. This study aims to explore the regulatory effects of Compound kushen injection (CKI) plus chemotherapy on the TME of TNBC from a single cell level.MethodsA mouse TNBC model in BALB/c mice was established to evaluate the antitumor efficacy and toxicity of CKI combined with chemotherapy. Flow cytometry was used to observe the influence of CKI on the lymphocyte populations in the tumor bearing mice. Both bulk RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) were applied to portray the modulation of CKI combined with chemotherapy on the TME of TNBC mice.ResultsCKI significantly enhanced the anticancer activity of chemotherapy in vivo with no obvious side effects. Flow cytometry results revealed a significantly higher activation of CD8+ T lymphocytes in the spleens and tumors of the mice with combination therapy. Bulk RNA-seq indicated that CKI could promote the cytotoxic immune cell infiltrating into tumor tissues. Meanwhile, scRNA-seq further revealed that CKI combined with chemotherapy could enhance the percentage of tumor-infiltrating CD8+ T cells, inhibit tumor-promoting signaling pathways, and promote T cell activation and positive regulation of immune response. In addition, CKI showed obvious anticancer activity against MDA-MB-231 breast tumor cells in vitro.ConclusionsThe combination of CKI and chemotherapy might provide a higher efficiency and lower toxicity strategy than a single chemotherapy drug for TNBC. CKI potentiates the anti-TNBC effects of chemotherapy by activating anti-tumor immune response in mice.
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