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Identification of Co-diagnostic Genes for Heart Failure and Hepatocellular Carcinoma Through WGCNA and Machine Learning Algorithms

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Abstract

This research delves into the intricate relationship between hepatocellular carcinoma (HCC) and heart failure (HF) by exploring shared genetic characteristics and molecular processes. Employing advanced methodologies such as differential analysis, weighted correlation network analysis (WGCNA), and algorithms like Random Forest (RF), Least Absolute Shrinkage Selection (LASSO), and XGBoost, we meticulously identified modular differential genes (DEGs) associated with both HF and HCC. Gene Set Variation Analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA) were employed to unveil underlying biological mechanisms. The study revealed 88 core genes shared between HF and HCC, indicating a common mechanism. Enrichment analysis emphasized the roles of immune responses and inflammation in both diseases. Leveraging XGBoost, we crafted a robust multigene diagnostic model (including FCN3, MAP2K1, AP3M2, CDH19) with an area under the curve (AUC) > 0.9, showcasing exceptional predictive accuracy. GSVA and ssGSEA analyses unveiled the involvement of immune cells and metabolic pathways in the pathogenesis of HF and HCC. This research uncovers a pivotal interplay between HF and HCC, highlighting shared pathways and key genes, offering promising insights for future clinical treatments and experimental research endeavors.

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Data Availability

The datasets for this study can be found in the GEO (http://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). The core code of this study has been uploaded to GitHub: https://github.com/Lizhiyongjuan/Code.git

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Acknowledgements

The authors would like to acknowledge the Innovative Team of Intelligent Inspection and Active Health (ITIH) for supporting this study.

Funding

This work was financially supported by the Biomedical Science and Technology Support Special Project of Shanghai Science and Technology Innovation Action Plan in 2021 (21S11901700) and Shanghai Shanghai Pudong New Area Clinical Medicine Summit Fund Project (PWYgf2021-04).

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Author notes

  1. Lizhi Cao, Xiaoying Wang, and Xin Li contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China

    Lizhi Cao

  2. Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, 201318, China

    Xiaoying Wang, Linlin Ma & Yanfei Li

  3. Physical Examination Center, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China

    Xin Li

  4. University of Shanghai for Science and Technology, Shanghai, 200093, China

    Lizhi Cao, Linlin Ma & Yanfei Li

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  4. Linlin Ma
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  5. Yanfei Li
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Contributions

All authors contributed to the study conception and design. LC, YL, and LM designed the study. LC, XW, and XL wrote the initial version of the manuscript and revised the final version. LC, XW, and XL performed data collection and analyzed the data. YL and LM supervised the work. All authors contributed to the manuscript and approved the submitted version.

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Correspondence to Linlin Ma or Yanfei Li.

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Cao, L., Wang, X., Li, X. et al. Identification of Co-diagnostic Genes for Heart Failure and Hepatocellular Carcinoma Through WGCNA and Machine Learning Algorithms. Mol Biotechnol 66, 1229–1245 (2024). https://doi.org/10.1007/s12033-023-01025-1

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