{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T17:55:51Z","timestamp":1782496551097,"version":"3.54.5"},"reference-count":22,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T00:00:00Z","timestamp":1782432000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Glioblastoma (GBM) is the most aggressive type of intracranial malignant tumor, known for its extremely poor prognosis. Lactylation, a newly identified post-translational modification, has been linked to tumorigenesis, though its specific role in GBM remains unclear. This study aims to integrate single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data to create a novel prognostic model for GBM, focusing on lactylation-related factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We studied lactate metabolism genes as markers in GBM. We obtained bulk transcriptomic data from TCGA and the GSE141383 and GSE162631 cohorts in the GEO databases. We used the R package Seurat to analyze scRNA-seq data, CellChat for cell communication analysis, and AUCell to assess lactate metabolism gene set scores across cell types. We developed a prognostic model using machine learning algorithms and tested its efficacy across multiple cohorts. Additionally, we investigated differences in immune infiltration, predicted sensitivity, and other factors between high and low-risk groups. We validated the function of the key gene CD93 at the cellular level.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The scRNA-seq data identified nine major cell types in GBM, with FCGBP+ macrophages showing the highest score in the lactate metabolism gene set. Authors designed a model informed by machine learning pinpointed three key genes\uff08CD93, FCER1G, and GRB2\uff09and developed a model with optimal prognostic value across cohorts.The high-risk group presented significantly poorer clinical outcomes. Immune-related bioinformatic analysis revealed significant differences in immune cell infiltration and checkpoint gene expression between risk groups. High-risk patients demonstrated lower immune infiltration and higher immunosuppression, rendering them less suitable for immunotherapy. Predictive algorithms indicated that axitinib and imatinib could be potential therapeutic drugs for these high-risk patients. In GBM tissue and cells, CD93 expression was significantly elevated, identifying it as a key risk gene in this model. Inhibition of CD93 expression via siRNA significantly reduced the proliferation, invasion, and migration of U87 and U251 cells.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In summary, we developed a novel characterization of lactylation-related clusters using single-cell sequencing technology. This study provided insights into the prognostic significance of lactate metabolism-related genes in GBM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0351849","type":"journal-article","created":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T17:43:27Z","timestamp":1782495807000},"page":"e0351849","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrated single-cell RNA sequencing and Bulk-RNA technologies reveal the immunological characteristics of lactylation related-genes in glioblastoma"],"prefix":"10.1371","volume":"21","author":[{"given":"Biao","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangfang","family":"An","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuansen","family":"Shu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"XuXiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiezhuo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3474-1161","authenticated-orcid":true,"given":"Zhaorui","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,6,26]]},"reference":[{"issue":"2","key":"pone.0351849.ref001","doi-asserted-by":"crossref","first-page":"547","DOI":"10.3390\/v15020547","article-title":"Recent developments in glioblastoma therapy: oncolytic viruses and emerging future strategies","volume":"15","author":"A Hamad","year":"2023","journal-title":"Viruses"},{"issue":"12","key":"pone.0351849.ref002","doi-asserted-by":"crossref","first-page":"3529","DOI":"10.3390\/cells10123529","article-title":"Glioblastoma: relationship between metabolism and immunosuppressive microenvironment","volume":"10","author":"A Hern\u00e1ndez","year":"2021","journal-title":"Cells"},{"issue":"6","key":"pone.0351849.ref003","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1055\/s-0037-1601858","article-title":"Congenital glioblastoma multiforme: an unusual and challenging tumor","volume":"48","author":"DM Anestis","year":"2017","journal-title":"Neuropediatrics"},{"issue":"3","key":"pone.0351849.ref004","doi-asserted-by":"crossref","first-page":"186","DOI":"10.2174\/1389200221666200408083950","article-title":"Therapeutics and research related to glioblastoma: advancements and future targets","volume":"21","author":"V Chavda","year":"2020","journal-title":"Curr Drug Metab"},{"key":"pone.0351849.ref005","doi-asserted-by":"crossref","first-page":"123227","DOI":"10.1016\/j.lfs.2024.123227","article-title":"Glioblastoma therapy: state of the field and future prospects","volume":"359","author":"NA Khiabani","year":"2024","journal-title":"Life Sci"},{"issue":"3","key":"pone.0351849.ref006","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s10555-024-10183-w","article-title":"Glioblastoma stem cell metabolism and immunity","volume":"43","author":"J Hawly","year":"2024","journal-title":"Cancer Metastasis Rev"},{"issue":"16","key":"pone.0351849.ref007","doi-asserted-by":"crossref","first-page":"8835","DOI":"10.3390\/ijms23168835","article-title":"Molecular biomarkers in glioblastoma: a systematic review and meta-analysis","volume":"23","author":"H Sareen","year":"2022","journal-title":"Int J Mol Sci"},{"issue":"3","key":"pone.0351849.ref008","first-page":"211","article-title":"The Warburg effect: how does it benefit cancer cells? 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