Analysis and Validation of Critical Signatures and Immune Cell Infiltration Characteristics in Doxorubicin-Induced Cardiotoxicity by Integrating Bioinformatics and Machine Learning

Chao Huang; Jixiang Pei; Daisong Li; Tao Liu; Zhaoqing Li; Guoliang Zhang; Ruolan Chen; Xiaojian Xu; Bing Li; Zhexun Lian; Xian-Ming Chu

doi:10.2147/JIR.S444600

Analysis and Validation of Critical Signatures and Immune Cell Infiltration Characteristics in Doxorubicin-Induced Cardiotoxicity by Integrating Bioinformatics and Machine Learning

J Inflamm Res. 2024 Feb 2:17:669-685. doi: 10.2147/JIR.S444600. eCollection 2024.

Authors

Chao Huang^#¹, Jixiang Pei^#¹, Daisong Li¹, Tao Liu², Zhaoqing Li¹, Guoliang Zhang¹, Ruolan Chen¹, Xiaojian Xu¹, Bing Li^{3

4}, Zhexun Lian¹, Xian-Ming Chu^{1

5}

Affiliations

¹ Department of Cardiology, the Affiliated Hospital of Qingdao University, Qingdao, Shandong, 266100, People's Republic of China.
² The Affiliated Qingdao Central Hospital of Qingdao University, The Second Affiliated Hospital of Medical College of Qingdao University, Qingdao, Shandong, 266042, People's Republic of China.
³ Department of Genetics and Cell Biology, Basic Medical College, Qingdao University, Qingdao, 266000, People's Republic of China.
⁴ Department of Dermatology, The Affiliated Haici Hospital of Qingdao University, Qingdao, 266033, People's Republic of China.
⁵ The Affiliated Cardiovascular Hospital of Qingdao University, Qingdao, 266071, People's Republic of China.

^# Contributed equally.

Abstract

Purpose: Doxorubicin-induced cardiotoxicity (DIC) is a severe side reaction in cancer chemotherapy that greatly impacts the well-being of cancer patients. Currently, there is still an insufficiency of effective and reliable biomarkers in the field of clinical practice for the early detection of DIC. This study aimed to determine and validate the potential diagnostic and predictive values of critical signatures in DIC.

Methods: We obtained high-throughput sequencing data from the GEO database and performed data analysis and visualization using R software, GO, KEGG and Cytoscape. Machine learning methods and weighted gene coexpression network (WGCNA) were used to identify key genes for diagnostic model construction. Receiver operating characteristic (ROC) analysis and a nomogram were used to assess their diagnostic values. A multiregulatory network was built to reveal the possible regulatory relationships of critical signatures. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) analysis was used to investigate differential immune cell infiltration. Additionally, a cell and animal model were constructed to investigate the relationship between the identified genes and DIC.

Results: Among the 3713 differentially expressed genes, three key genes (CSGALNACT1, ZNF296 and FANCB) were identified. A nomogram and ROC curves based on three key genes showed excellent diagnostic predictive performance. The regulatory network analysis showed that the TFs CREB1, EP300, FLI1, FOXA1, MAX, and MAZ modulated three key genes. An analysis of immune cell infiltration indicated that many immune cells (activated NK cells, M0 macrophages, activated dendritic cells and neutrophils) might be related to the progression of DIC. Furthermore, there may be various degrees of correlation between the three critical signatures and immune cells. RT‒qPCR demonstrated that the mRNA expression of CSGALNACT1 and ZNF296 was significantly upregulated, while FANCB was significantly downregulated in DOX-treated cardiomyocytes in vitro and in vivo.

Conclusion: Our study suggested that the differential expression of CSGALNACT1, ZNF296 and FANCB is associated with cardiotoxicity and is also involved in immune cell infiltration in DIC. They might be potential biomarkers for the early occurrence of DIC.

Keywords: biomarker; cardiotoxicity; doxorubicin; immune infiltration; machine learning.

Grants and funding

This work was supported by The National Natural Science Foundation of China (no. 82172574, no. 81871231), The Natural Science Foundation of Shandong Province (no. ZR2020MH016, no. ZR202209280042), Research Planning Project of Shandong Higher Medical Education Research Center (no. YJKT202171), and Shandong Taishan Scholars Young Experts Program (No. tsqn202103056).