Machine learning algorithms assisted identification of post-stroke depression associated biological features

Xintong Zhang; Xiangyu Wang; Shuwei Wang; Yingjie Zhang; Zeyu Wang; Qingyan Yang; Song Wang; Risheng Cao; Binbin Yu; Yu Zheng; Yini Dang

doi:10.3389/fnins.2023.1146620

Machine learning algorithms assisted identification of post-stroke depression associated biological features

Front Neurosci. 2023 Mar 8:17:1146620. doi: 10.3389/fnins.2023.1146620. eCollection 2023.

Authors

Xintong Zhang¹, Xiangyu Wang², Shuwei Wang³, Yingjie Zhang¹, Zeyu Wang⁴, Qingyan Yang¹, Song Wang⁵, Risheng Cao⁶, Binbin Yu¹, Yu Zheng¹, Yini Dang⁷

Affiliations

¹ Department of Rehabilitation Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
² Department of Rehabilitation Medicine, The Affiliated Lianyungang Oriental Hospital of Kangda College of Nanjing Medical University, Lianyungang, Jiangsu, China.
³ Department of Critical Care Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang, China.
⁴ Department of Rehabilitation Medicine, Shanghai Ruijin Rehabilitation Hospital, Shanghai, China.
⁵ Department of Neurological Rehabilitation, Wuxi Yihe Rehabilitation Hospital, Wuxi, Jiangsu, China.
⁶ Department of Science and Technology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
⁷ Department of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.

Abstract

Objectives: Post-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.

Methods: Expression 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.

Results: Functional 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.

Conclusion: A 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.

Keywords: GEO; WGCNA; machine learning algorithms; metabolism; post-stroke depression.

Grants and funding

This study was funded by the National Natural Science Foundation of China (82000529 and 82272595), the Natural Science Foundation of Jiangsu Province (SBK2020042595), and the National Key Research & Development Program of the Ministry of Science and Technology of the People’s Republic of China (2018YFC2002300 and 2018YFC2002301).