Ting Xiong ¹ Quhuan Li ^1* Yifan Wang ¹ Ying Kong ¹ Hailin Li ¹ Jie Liu ¹ Yueheng Wu ^2* Zhang Fengxia ^3*

• ¹ South China University of Technology, Guangzhou, China
• ² Department of Cardiovascular Surgery, Guangdong Provincial People's Hospital, Guangzhou, China
• ³ Department of Nephrology, First Affiliated Hospital of Gannan Medical University, Ganzhou, Ganzhou, Jiangxi Province, China

Heart failure represents the terminal stage of various cardiac diseases and is a leading cause of mortality globally. It is characterized by rapid progression, severe symptoms, and poor clinical outcomes. Ischemic heart failure (IHF), primarily driven by coronary artery disease, involves intricate mechanisms that are not fully understood. Immune responses play a pivotal role in this context, as hypoxia-triggered inflammation contributes to cardiac muscle remodeling. Remodeling in areas unaffected directly by infarction can exacerbate disease outcomes, emphasizing the need for specific biomarkers to prevent cardiac fibrosis and disease progression. In this study, we utilized multi-omics data to elucidate the intricate immune landscape of heart failure at various regulatory levels. Given the substantial size of our transcriptomic dataset, we used diverse machine learning techniques to identify key mRNAs. For smaller datasets such as our proteomic dataset, we applied multilevel data cleansing and enhancement using principles from network biology. The research culminated in the development of a scalable, integrated -omics analysis pipeline, through which pleiotrophin (PTN) was identified as a potential biomarker for preventing cardiac fibrosis. The present study not only provides a comprehensive perspective on immune dynamics in IHF but also offers valuable insights for the identification of biomarkers, discovery of therapeutic targets, and development of drugs.