{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T05:00:59Z","timestamp":1776747659656,"version":"3.51.2"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> Heart failure (HF) is a complex and heterogeneous manifestation of multiple cardiovascular diseases that usually occurs in the advanced stages of disease progression. The role of neutrophil extracellular traps (NETs) in the pathogenesis of HF remains to be explored.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> Bioinformatics analysis was employed to investigate general and single-cell transcriptome sequencing data downloaded from the GEO datasets. Differentially expressed genes (DEGs) associated with NETs in HF patients and healthy controls were identified using transcriptome sequencing datasets and were subsequently subjected to functional enrichment analysis. To identify potential diagnostic biomarkers, the random forest algorithm (RF) and the least absolute shrinkage and selection operator (LASSO) were applied, followed by the construction of receiver operating characteristic (ROC) curves to assess accuracy. Additionally, single-cell transcriptome sequencing data analysis identified key immune cell subpopulations in TAC (transverse aortic constriction) mice potentially involved in NETs regulation. Cell-cell communication analysis and trajectory analysis was then performed on these key cell subpopulations.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> We identified thirteen differentially expressed genes (DEGs) associated with NET through differential analysis of transcriptome sequencing data from HF (heart failure) samples. Utilizing the Random Forest and Lasso algorithms, along with experimental validation, we successfully pinpointed four diagnostic markers (CXCR2, FCGR3B, VNN3, and FPR2) capable of predicting HF risk. Furthermore, our analysis of intercellular communication, leveraging single-cell sequencing data, highlighted macrophages and T cells as the immune cell subpopulations with the closest interactions with neutrophils. Pseudo-trajectory analysis sheds light on the differentiation states of distinct neutrophil subpopulations.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> In this study, we conducted an in-depth investigation into the functions of neutrophil subpopulations that infiltrate cardiac tissue in TAC mice. Additionally, we identified four biomarkers (CXCR2, FCGR3B, VNN3, and FPR2) associated with NETs in HF. Our findings enhance the understanding of immunology in HF.<\/jats:p>","DOI":"10.3389\/fcell.2023.1258959","type":"journal-article","created":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T23:38:35Z","timestamp":1698190715000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Exploring key genes associated with neutrophil function and neutrophil extracellular traps in heart failure: a comprehensive analysis of single-cell and bulk sequencing data"],"prefix":"10.3389","volume":"11","author":[{"given":"Xudong","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changhao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiaoqiao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingxiang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Long","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,10,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat. 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