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Machine Learning for the Evaluation and Detection of Key Markers in Dilated Cardiomyopathy

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Medical Imaging and Computer-Aided Diagnosis (MICAD 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 810))

Abstract

Objective: Screening for dilated cardiomyopathy (DCM) core genes and letter immune infiltration by bioinformatic methods to find new strategies for prevention and treatment. Methods: Gene expression compilation database (GEO) GSE3585 and GSE17800 gene microarray sets were extracted and differentially expressed genes (DEGs) were obtained from DCM and normal control myocardial biopsies using R language. The DEGs were tested for gene ontology (GO) functional analysis, Kyoto Gene and Genome Encyclopedia (KEGG) pathway analysis and gene probe (GSEA) enrichment. The Lasso algorithm was subsequently used to identify key DCM-related genes in the practice set and to authenticate them against the test set. Prospective mechanisms for DCM development included: differences in key gene expression between normal and DCM samples, variations in western blot coverage, correlates of clinical relevance to exempt cells, and key gene and immune cell correlation. Results: The final screening identified 2 key genes, NPPA and NPPB. The variation in expression of the key genes between normal and DCM samples can be regarded as a diagnostic factor for patients. Also, There is a striking divergence in vaccine levels of immuinia among normal and DCM samples, and the critical gene expression was closely related to the richness of immune cell infiltration. Conclusion: Based on bioinformatics analysis and review of relevant literature, two candidate genes, NPPA and NPPB, were screened and strongly associated with the progression of DCM, providing meaningful clues and suggestions used to prevent and heal DCM.

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Acknowledgements

This study was funded by the S&T Innovation Project for Universities in Shanxi Province (2019L0683), the Graduate Education Innovation Project in Shanxi Province (2022Y37) and the Provincial Science and Technology Grant in Shanxi Province (20210302124588).

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Authors and Affiliations

  1. Changzhi Medical College, Changzhi, Shanxi, China

    Xiaodan Bi, Zhenrun Zhan, Jinpeng Yang, Xu Tang & Tingting Zhao

  2. Heping Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China

    Xiaodan Bi, Zhenrun Zhan, Jinpeng Yang, Xu Tang & Tingting Zhao

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  1. Xiaodan Bi
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  2. Zhenrun Zhan
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  3. Jinpeng Yang
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  4. Xu Tang
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  5. Tingting Zhao
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Corresponding author

Correspondence to Tingting Zhao .

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Editors and Affiliations

  1. Department of Computer Sciences and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Ruidan Su

  2. Department of Informatics, University of Leicester, Leicester, UK

    Yudong Zhang

  3. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Han Liu

  4. Computational Medicine, University of Manchester, Manchester, UK

    Alejandro F Frangi

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Bi, X., Zhan, Z., Yang, J., Tang, X., Zhao, T. (2023). Machine Learning for the Evaluation and Detection of Key Markers in Dilated Cardiomyopathy. In: Su, R., Zhang, Y., Liu, H., F Frangi, A. (eds) Medical Imaging and Computer-Aided Diagnosis. MICAD 2022. Lecture Notes in Electrical Engineering, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-16-6775-6_42

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  • DOI: https://doi.org/10.1007/978-981-16-6775-6_42

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  • Print ISBN: 978-981-16-6774-9

  • Online ISBN: 978-981-16-6775-6

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