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Updated: Aug 1, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
Published on: April 4, 2018
Identification of biomarkers, pathways, and potential therapeutic targets for heart failure using next-generation
Prashanth Ganekal1, Basavaraj Vastrad2, Chanabasayya Vastrad3
1Department of General Medicine, Basaveshwara Medical College, Chitradurga, India.
Insights
Researchers identified 10 key genes as potential biomarkers for heart failure (HF) diagnosis and treatment. This study offers new insights into HF, but further research is needed to confirm the functional roles of these identified genes.
Area of Science:
- Cardiovascular Diseases
- Molecular Biology
- Genomics
Background:
- Heart failure (HF) is a prevalent cardiovascular disease and a leading cause of related deaths.
- Despite advances, novel biomarkers for HF prognosis and therapy are urgently needed.
- This study aimed to identify potential biomarkers for HF diagnosis and treatment.
Purpose of the Study:
- To identify novel biomarkers for heart failure (HF) diagnosis and treatment.
- To evaluate the diagnostic effectiveness of identified hub genes using ROC curve analysis.
Main Methods:
- Utilized next-generation sequencing (NGS) data (GSE161472) to identify differentially expressed genes (DEGs) between HF and normal samples.
- Performed Gene Ontology (GO) and pathway enrichment analyses, constructed protein-protein interaction (PPI) and regulatory gene networks.
- Selected 10 hub genes based on integrated network analyses and predicted diagnostic effectiveness via ROC curve analysis.
Main Results:
- Identified 930 DEGs (464 upregulated, 466 downregulated) in HF patients.
- Enrichment analyses revealed DEGs involved in localization, metabolic processes, and the citric acid cycle.
- Selected 10 hub genes, including HSP90AA1, ARRB2, MYH9, HSP90AB1, FLNA, EGFR, PIK3R1, CUL4A, YEATS4, and KAT2B.
Conclusions:
- The identified hub genes may offer novel insights for HF diagnosis and treatment.
- Further experimental validation is required to elucidate the functional roles of these genes in HF pathogenesis.
Background:
Heart failure (HF) is the most common cardiovascular diseases and the leading cause of cardiovascular diseases related deaths. Increasing molecular targets have been discovered for HF prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might aid the diagnosis and treatment of HF.
Methods:
We searched next-generation sequencing (NGS) dataset (GSE161472) and identified differentially expressed genes (DEGs) by comparing 47 HF samples and 37 normal control samples using limma in R package. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g: Profiler database. The protein-protein interaction (PPI) network was plotted with Human Integrated Protein-Protein Interaction rEference (HiPPIE) and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. Then, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed by Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis to predict the diagnostic effectiveness of the hub genes.
Results:
A total of 930 DEGs, 464 upregulated genes and 466 downregulated genes, were identified in HF. GO and REACTOME pathway enrichment results showed that DEGs mainly enriched in localization, small molecule metabolic process, SARS-CoV infections, and the citric acid tricarboxylic acid (TCA) cycle and respiratory electron transport. After combining the results of the PPI network miRNA-hub gene regulatory network and TF-hub gene regulatory network, 10 hub genes were selected, including heat shock protein 90 alpha family class A member 1 (HSP90AA1), arrestin beta 2 (ARRB2), myosin heavy chain 9 (MYH9), heat shock protein 90 alpha family class B member 1 (HSP90AB1), filamin A (FLNA), epidermal growth factor receptor (EGFR), phosphoinositide-3-kinase regulatory subunit 1 (PIK3R1), cullin 4A (CUL4A), YEATS domain containing 4 (YEATS4), and lysine acetyltransferase 2B (KAT2B).
Conclusions:
This discovery-driven study might be useful to provide a novel insight into the diagnosis and treatment of HF. However, more experiments are needed in the future to investigate the functional roles of these genes in HF.
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