{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T22:55:58Z","timestamp":1790549758414,"version":"4.1.0"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008240","name":"Department of Health of Hebei Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008240","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Although the third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) Osimertinib is widely used in lung adenocarcinoma (LUAD), acquired resistance to this agent remains a major clinical challenge perplexing the academic community. While the genomic drivers of osimertinib resistance (OR) have been relatively well characterized, the non-genetic contributions of the tumor microenvironment (TME), especially the role of macrophage-mediated remodeling remains incompletely understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address this gap, a total of 6 lung cancer tissue samples were collected from a cohort of 6 NSCLC patients and subjected to single-cell RNA sequencing (scRNA-seq). Weighted gene co-expression network analysis (WGCNA) was performed on Gene Expression Omnibus (GEO) data to screen out OR-associated gene modules. The intersection of scRNA-seq-derived differentially expressed genes (DEGs) and module genes was used to identify M2 macrophage- and OR-related DEGs (MORGs). An ensemble machine learning framework encompassing 112 algorithm combinations was applied to construct a gene signature. Survival analysis was conducted in the EGFR-mutant TCGA cohort. In vitro cell experiments were further performed for functional validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Single-cell RNA sequencing firstly revealed an increased proportion of M2 macrophages in the OR group, and M2 macrophage-specific DEGs were identified between OR and non-OR groups. A total of seven MORGs were obtained by intersecting DEGs and OR-related module genes. A 7-gene signature was successfully established via machine learning. Survival analysis demonstrated that RRBP1 showed the strongest association with overall survival and was selected for further investigation. In vitro experiments confirmed that RRBP1 was significantly upregulated in the H1975OR cells. Knockdown of RRBP1 reduced cell viability and migration, lowered the IC50 of osimertinib, and increased the expression of ER stress markers such as GRP78 and CHOP.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>In summary, our study identified a 7-MORGs signature for OR, among which RRBP1 may be a candidate regulator for OR.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fgene.2026.1928986","type":"journal-article","created":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T22:10:13Z","timestamp":1790547013000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative bioinformatics screening of a M2 macrophage-associated gene signature for osimertinib resistance"],"prefix":"10.3389","volume":"17","author":[{"given":"Yajie","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Medical Oncology, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaozhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Infectious Diseases, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, The Fourth Hospital of Hebei Medical University","place":["Shijiazhuang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1186\/s13059-017-1349-1","article-title":"xCell: digitally portraying the tissue cellular heterogeneity landscape","volume":"18","author":"Aran","year":"2017","journal-title":"Genome Biol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1038\/nrd.2018.169","article-title":"Targeting macrophages: therapeutic approaches in cancer","volume":"17","author":"Cassetta","year":"2018","journal-title":"Nat. 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