{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T06:31:42Z","timestamp":1781850702039,"version":"3.54.5"},"reference-count":85,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T00:00:00Z","timestamp":1741305600000},"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. Immunol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Sepsis, a systemic inflammatory response syndrome triggered by infection, is associated with high mortality rates and an increasing global incidence. While <jats:italic>N<\/jats:italic><jats:sup>6<\/jats:sup>-methyladenosine (m<jats:sup>6<\/jats:sup>A) RNA methylation and ferroptosis are implicated in inflammatory diseases, their specific genes and mechanisms in sepsis remain unclear.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Transcriptomic datasets of sepsis, along with m<jats:sup>6<\/jats:sup>A-related genes (m<jats:sup>6<\/jats:sup>A-RGs) and ferroptosis-related genes (FRGs), were sourced from public databases. Differentially expressed genes (DEGs) were identified between the sepsis and control groups, and m<jats:sup>6<\/jats:sup>A-RGs were analyzed through weighted gene co-expression network analysis (WGCNA) to uncover m<jats:sup>6<\/jats:sup>A module genes. These were then intersected with DEGs and FRGs to identify candidate genes. Biomarkers were identified using two machine learning methods, receiver operating characteristic (ROC) curves, and expression validation, followed by the development of a nomogram. Further in-depth analyses of the biomarkers were performed, including functional enrichment, immune infiltration, drug prediction, and molecular docking. Single-cell analysis was conducted to identify distinct cell clusters and evaluate biomarker expression at the single-cell level. Finally, reverse transcription\u2013quantitative PCR (RT-qPCR) was employed to validate biomarker expression in clinical samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>DPP4 and TXN were identified as key biomarkers, showing higher expression in control and sepsis samples, respectively. The nomogram incorporating these biomarkers demonstrated strong diagnostic potential. Enrichment analysis highlighted their involvement in spliceosome function and antigen processing and presentation. Differential analysis of immune cell types revealed significant correlations between biomarkers and immune cells, such as macrophages and activated dendritic cells. Drug predictions identified gambogenic acid and valacyclovir as potential treatments, which were successfully docked with the biomarkers. Single-cell analysis revealed that the biomarkers were predominantly expressed in CD4<jats:sup>+<\/jats:sup> memory cells, and CD16<jats:sup>+<\/jats:sup> and CD14<jats:sup>+<\/jats:sup> monocytes. The expression of DPP4 was further validated in clinical samples.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>DPP4 and TXN were validated as biomarkers for sepsis, with insights into immune infiltration and therapeutic potential at the single-cell level, offering novel perspectives for sepsis treatment.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1543517","type":"journal-article","created":{"date-parts":[[2025,3,7]],"date-time":"2025-03-07T05:22:57Z","timestamp":1741324977000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Identification and validation of m6A RNA methylation and ferroptosis-related biomarkers in sepsis: transcriptome combined with single-cell RNA sequencing"],"prefix":"10.3389","volume":"16","author":[{"given":"Jinshuai","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kun","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxia","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyi","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoyu","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolan","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.0287","article-title":"The 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