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<p>Cardiomyopathy is a type of cardiovascular disorder that is a primary cause of death globally, killing millions of people each year. Cardiomyopathy detection and early diagnosis are crucial in reducing negative health effects. Thus, this study aims to use single cell RNA sequencing, and bioinformatics analysis to uncover dendritic cell-specific biomarkers, gene ontology, pathways, regulatory interaction networks, and protein-chemical compounds related to the molecular mechanism of cardiomyopathy progression. Two RNAseq datasets GSE65446 and GSE155495 also were evaluated to identify significant biomarkers in cardiomyopathy, and 123 mutual DEGs appeared between scRNAseq and RNAseq datasets. In addition, the DAVID online platform and FunRich software were utilized to detect cell communication in innate immune responses, type 1 IFN, antigen processing and presentation, allograft rejection and viral infection significant gene ontology and metabolic pathways in cardiomyopathy. The protein-protein interaction (PPI) network revealed five key hub proteins (ITGAX, IRF7, MX1, HLA-B, and IRF1). Following that, several transcription factors (GATA2, FOXC1, SREBF1, STAT3, and NFKB1) as well as microRNA (hsa-mir-26a-5p, hsa-mir-129-2-3p, etc.) were predicted. Prospective chemical substances such as tretinoin, valproic acid, and arsenic trioxide have been predicted to be linked to cardiomyopathy treatment. The acceptable value of receiver operating characteristic (ROC) curve analysis revealed that biomarkers play critical roles in cardiomyopathy. This study identifies molecular indicators at the RNA and protein levels that may be useful in improving understanding of molecular causes, early diagnosis, and devising favorable cardiomyopathy treatment. More research will be needed to validate our predicted findings as future clinical biomarkers.</p>

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Uncovering dendritic cell specific biomarkers for diagnosis and prognosis of cardiomyopathy using single cell RNA sequencing and comprehensive bioinformatics analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月26日

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  • Md. Mizanur Rahman,
  • Md Habibur Rahman,
  • Md. Arju Hossain,
  • Kh Mujahidul Islam,
  • Prosenjit Saha Apu,
  • Mahfuj Khan,
  • Md Golam Kibria,
  • Siddique Akber Ansari,
  • Mahammad Humayoo

摘要

Cardiomyopathy is a type of cardiovascular disorder that is a primary cause of death globally, killing millions of people each year. Cardiomyopathy detection and early diagnosis are crucial in reducing negative health effects. Thus, this study aims to use single cell RNA sequencing, and bioinformatics analysis to uncover dendritic cell-specific biomarkers, gene ontology, pathways, regulatory interaction networks, and protein-chemical compounds related to the molecular mechanism of cardiomyopathy progression. Two RNAseq datasets GSE65446 and GSE155495 also were evaluated to identify significant biomarkers in cardiomyopathy, and 123 mutual DEGs appeared between scRNAseq and RNAseq datasets. In addition, the DAVID online platform and FunRich software were utilized to detect cell communication in innate immune responses, type 1 IFN, antigen processing and presentation, allograft rejection and viral infection significant gene ontology and metabolic pathways in cardiomyopathy. The protein-protein interaction (PPI) network revealed five key hub proteins (ITGAX, IRF7, MX1, HLA-B, and IRF1). Following that, several transcription factors (GATA2, FOXC1, SREBF1, STAT3, and NFKB1) as well as microRNA (hsa-mir-26a-5p, hsa-mir-129-2-3p, etc.) were predicted. Prospective chemical substances such as tretinoin, valproic acid, and arsenic trioxide have been predicted to be linked to cardiomyopathy treatment. The acceptable value of receiver operating characteristic (ROC) curve analysis revealed that biomarkers play critical roles in cardiomyopathy. This study identifies molecular indicators at the RNA and protein levels that may be useful in improving understanding of molecular causes, early diagnosis, and devising favorable cardiomyopathy treatment. More research will be needed to validate our predicted findings as future clinical biomarkers.